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Ep 127: Technical Analysis Case Studies & ABCD Patterns

Hey, this is Sasha Evdakov.

Today we’re going to take a look at technical analysis case study as far as Tesla goes. And we’ll do a little bit of practice.

This is great if you’re getting started with technical analysis or you want to learn a little more about technical analysis. You want to break apart a chart in great detail.

That’s what we’re going to do is we’re going to take a look at this chart. And we’re going to break it apart in detail.

If you’re reading this, I highly recommend you take a look at the video. I will give you some critical insights on what to watch for as well even if you’re reading this.

Before we go in detail onto this keep in mind that none of the stocks here are recommendations to buy or sell. Consult with your financial advisor before taking on any trade.

Illustration of the Example – Tesla

Let’s take a look at the chart. I’m going to use a drawing program here to illustrate this example. I believe you want to understand investing and looking at charts.

It’s essential that when you’re studying and evaluating charts the point behind it is to predict future price movements.

But to predict future price movements, you need to:

• understand how a stock moves
• understand how a stock behaves
• understand how it acts
• know what it’s done in the past
• see what you’re expecting to do in the future

For example, you might be looking at Michael Jordan when he was very popular. He was able to score a certain amount of points every single game.

Then more than likely the next game or the next game after that you would expect a specific type of result. Time and time again because that’s what that type of player or person did.

With stocks is the same story. Tesla has a specific type of group of traders. Not only because it trades at a particular price range, but because of the people that are attracted to trading this type of stock.

There are some day traders, swing traders, longer-term investors, but in general. It attracts a specific type of person. If you’re looking for like a Microsoft or a Bank of America, those attract a different kind of person.

Sometimes those things overlap, but in general, you have a type of person that gets attracted to these types of stocks. In either case, when we start evaluating these charts, you’re looking at human behavior.

The price tells you:

• about human behavior,
• what people want
• what people don’t want
• how things are acting
• when things are running out of fuel

Take a look at this upward trend right here. As you look at Tesla here and you’re looking for this uptrend that we’ve had eventually these things run out of gas. The steeper these things move eventually they run out of gas.

You can’t keep pushing a rocket ship to the moon without giving something back. Eventually, it runs out of gas. At least in our current environment and state that we live in.

In this case, the same concept as you continue to move higher eventually this is why you need pullbacks. When you see things continuing to move higher, that’s why finally you get these pullbacks. These pullbacks allow that stock to continue to move higher, digest or consolidate.

Stocks don’t go straight up. I think if you’ve been trading for a little bit of time you know and understand that. In either case, one of the things that I look at when I first look at charts is I tell myself a story.

Especially when I didn’t understand how stocks moved and behaved. I used to tell myself a little story, and when it comes to that, it’s an exciting concept. When you tell yourself a story, it makes things easier. Humans think in terms of stories.

When it comes to Tesla and we start looking at this you can see this stock was moving sideways. It was under the $50 range. The story here is this stock was the first company. It was getting started and building energy and momentum.

It’s getting its exposure, the marketing – that was happening. And people started to build into buying this stock. As they continue to develop into this eventually right around early 2013, you had some catalyst. You had a news catalyst, some awareness that started to happen. More people got excited about what this company was doing.

There was a lot more to the forefront of this company. And when this happened, this stock started to explode. You can see that by this big candle bar that happened. When this happened, more buyers continued to step in. There’s more excitement, and we continue to move higher to the upside.

You could discuss every bar and every tick here but in our sake for our examples of evaluating a stock chart we’re looking at the bigger picture.

The stock had a great movement to the upside up until about the end of 2013. In 2013 it made its presence known. Once its existence was known so many people got into the stock and then it needed a pullback. It was too far extended, and it was starting to run on fumes. The evaluation for how far it went in a short amount of time was far stretched.

If you look at the moving average here that we have and compare it to at the highs the distance, there is much greater than if we look at somewhere in June. The distance is a lot smaller. In either case, you can see it’s quite stretched.

• What are the stocks do when they get overextended?

Well, they pull back, and that’s what happened.

This stock needed a calming down period to acquire new investments to get more value. Also, people who got into the stock at these earlier points started taking some profits.

We loaded the gun back up and then we got more gas. After we got more gas, we continued higher. And a stock continued to power to the upside once more.

Over time things get a little extended. We’re coming towards the end of our tank, and we need a little bit of the breather.

• What does the stock do?

Towards mid-2014 we have a slight pullback. This is what you do if you’re telling yourself a story. Now the stock continued to move in this wave-like pattern for over the next year or two. But we slowly start running into roadblocks at higher prices. This is where the problem begins to lie with this stock.

We’re starting to move, but we’re not moving as fast. The stock now becomes a lot more known. It becomes more available to the public as far as news goes and as far as awareness goes. The price that it’s priced in. It needs to start hitting things in perfection for it to continue to grow. And that becomes a lot more difficult because they are innovating.

Over the next 2015 and 2016, we’ve been moving in a sideways pattern between the 175 and 270 level. And in there we’ve had some ups and downs. But on a bigger picture scope, we’ve had some sideways action.

If you break this apart once more, you can see that at the beginning we had an accumulation stage. Then in the next part, we had that growth stage. The stage where we had that upward movement. The initial investor started to come in. And then from then on, we’ve been in a distribution or consolidation stage. Or we are moving sideways.

You can see that this is how you’re looking at a stock when you’re starting to break it down.

ABCD Patterns – What to pay attention to?

When we start looking at some more technical patterns and technical evaluations you can start looking at things like ABCD patterns.

As you start getting more specific, you can look at ABCD patterns on the weekly. Or you can look at it on the daily, and you can look at it on the monthly. I’m doing it right now on the weekly because it’s easier to see, especially as a teaching example.

Basic questions:

• Where are ABCD patterns?
• How are they formed?

They’re usually based on swing points – where the stock changes directions. That’s what a swing point is.

Look at where a stock changes directions. You get an idea of the swing points. When we start looking at ABCD patterns what’s interesting about them is they talk about human behavior.

They’re telling you about the cyclical moves in stock and rhythm in a pattern. Like you have a sleep pattern. Typically most humans go to bed at 10 p.m. and wake up by 7 or 8 a.m.

You have that sleep pattern that you continue to go through day in and day out. You have that pattern, and ABCD patterns are very similar. If I plop A where the stock changed movement, then we go to the B at the next point where we had a change.

That would go to B to C. We have that little pullback and the C to D. You can see how that works out in terms of the patterns. If you start drawing things together and connecting these you can see that pattern standing out – ABCD patterns.

I’ll draw a couple more patterns so that you’re aware that ABCD patterns don’t have to happen to the upside.

Also, you have them to the downside. Sometimes they’re not as clean, but I will still share with you how those look. That way you get an idea, so we had a downward ABCD pattern and 2015 to about 2016 – Tesla. You can see there are another nice and clean ABCD patterns. Yes, there are other ABCD patterns in between that, but right now I’m showing you the clean patterns.

That way you learn what you’re looking for. And once you discover what you’re looking for you can search for subtle little ABCD patterns. It’s better to focus on cleaner patterns. That way you understand what’s going on. Once you know what’s going on with ABCD patterns, then you can modify these. You can manipulate these and use them on your terms and your chart.

Now here’s the other exciting fact behind ABCD patterns. When you’re looking at ABCD patterns, you’re looking for the volume to confirm this pattern.

Essential question: How is that volume coming into the stock?

From the A to B pattern you have massive volume coming in or good volume. When you have the volume, you have gas in the gas tank. In this case, you can see that we have volume coming in. And we have the full price spread that follows through.

That’s why you continue to get the follow through on that movement. When you start getting a pullback on the B to C leg, this is typically lower volume.

As you get into the next stage, it might even appear that it’s higher volume. The thing is if you compare it to the previous volume it should be lighter volume. Then finally the next leg (C to D) you want to see the more substantial volume to the upside from the C to D leg.

It’s more massive than the B to C leg. This is not always the case I found over the years. You may see some lighter volume, but the expansion can still be pretty good. Or you still want an increase in volume.

If you notice here in Tesla in 2014 even though we don’t have heavier volume than the retracement B to C, we still have an incline in volume. Or the volume starts to grow. As you can see it’s not always the case, but that’s what you want to see.

What’s Happening to The Downside?

This is the same thing when it happens to the downside. If we take the 2015 movement to the downside, the A to B you can see that it’s the slightly red volume is picking up.

The bearish volume or the big sticks on the red bars are picking up. As we get this retracement which is the B to C you can see the volume is very light. The B to C moves to the upside because this is a bearish pattern.

It’s contracting, and when we’re talking about the bullish volume, you can see it’s a lot lighter. Then as we get our next break lower again which is the C to D, you can see that volume picking up again on the bearish end.

That’s what you like to see on these charts. And that’s what makes a significant price movement. That’s healthy. If you see a stock moving in a direction with lighter volume that means there’s less gas. That means that there’s less conviction. It’s less likely to stand the test of time. It’s less likely to continue moving in that direction.

It doesn’t mean it can’t. It means it’s less likely to continue. That’s why we always look at volume as a clear sign or indicator for future price movements and actions and behaviors.

When we start evaluating at this chart, you’re combining multiple things.

You’re combining:

• ABCD pattern
• the price action

When we’re talking about price action, it’s essential how the price is moving, which means these wide bars from the open to the close.

You’re putting those together to create and evaluating that chart. At least that’s the simple and basic form.

Pro tip: You could use other indicators to help with this, but I find that the more complicated you make things, the more cluttered your mind becomes. Keep things simple.

As we look at the chart as a whole one of the things that I always like to stress is you start comparing these charts. You start comparing them to the previous volumes.

The other thing is when you look at retracements and pullbacks a typical retracement is 50%. That means nothing is wrong with the stock. You can get other retracements which are 61.8 or 38.2, but it’s based on the Fibonacci sequence.

The simple thing behind the Bonacci sequence is it’s a way to gauge retracement. Fibonacci numbers are mathematical numbers. When you add the first two, you get the next one. Then you add the following two you reach the next number and so forth.

They continue moving along those lines. But as you put them in charts, it allows us to predict human behavior better. These are not magic numbers or formulas that are going to give you perfect answers whenever a pullback is going to stop. But it’ll give you a guideline of what’s a healthy pullback and retracement.

If we take this initial movement and we look at this stock, overall you can see our pullbacks here are 50%, 38.2%, 23.6%, and 61.8%.

I focus on 50%, 61.8%, and 38.2% – those are the main ones that you want to focus on. Something like 23.6% doesn’t hit as often. But if you look at this and we drew the line from the lows or the swing point to the highs you can see what these lines tell you.

Our pullback comes right at 50%. You can see if I draw that line all the way across you can see we’re hitting right at 50%.

The other important thing you can remember is the projected move. How far you expect the next step to go from the C point or after you get the retracement?

Well, if you measure A to B, you will get the distance from C to D.

In this case, if we take our numbers (call it $40), you get about 155 points on the left.

If you draw the same thing from the lows of point C and you go all the way to D, you get about 148 points. About five points off which is not a big deal when it comes to stocks.

You can see it’s close when it comes to the projected move. It’s interesting how this works out.

Next example – ABCD pattern:

If you look at the next leg or the next stage, we have our ABCD pattern. We look at this measure move right there. You can see from here to here we have 147 and then from here to here we also have 114. It’s a little bit less, but the move is still in that range. It’s not going to be perfect in the market.

Take a Look at Retracement

From the retracement of 2014 on that movement the pullback we went from about $130 to about $264.

And we pulled back to about 180-190. You can see that it pulls back right into that 61.8% level. It’s fascinating how things work out in those ways.

Bearish Example

Take a look at the bearish example that we had in 2016. Here’s our projected ABCD pattern. It’s a little bit different looking it’s not as clean. But it gives you the same concept.

You could even say that you had a smaller ABCD pattern right here. That’s another one as well to look at. You can see we’re coming in on this retracement right there to the 50%. Notice that we have 50%, and there are our resistance and retracement.

Our pullback is into 50%. The measured move from A to B – we call it 78 points. And from highs to the end is about 92 points. It’s about 20 off, but it’s very similar. It’s not going to be perfect, but it’s very similar.

If we look at the volume of these movements, you can see the volume is building right here on the downward leg. Then if you look at retracement and you go straight down and look at the volume our volume is drawing up. And then all of a sudden on the break the volume picks up.

That’s what we’re watching on these moves and these measured price moves. And that’s what you get. You get a subtle clean movement in the stock when you’re watching these things.

When you’re looking at ABCD patterns, swing points, and measured moves, this is your money leg. The C to D is your money leg. Once you find the A to B and B to C pattern, the C to D is that money leg.

Whether that’s to the upside or whether that’s to the downside – that’s your money leg.

You can play things in ranges. We have sideways patterns right here in the stock. You can play things in ranges, but these ranges are not as big in terms of price movement as a C to D or an ABCD pattern.

I often find beginners struggle finding ABCD patterns. This is partly due to the stocks that they’re looking at, but also their understanding of ABCD patterns. If you struggle to find ABCD patterns, it’s crucial that you move on to another stock. That is because you’re either looking for something that’s not there. Or you’re forcing the trade.

If you can’t find an ABCD pattern, move on to another stock. Let me show you some examples of some famous companies. The reason why they’re popular is that they move in the appropriate behavior.

They have enough volume. If you’re trading stocks with low volume (penny stocks), sometimes it’s challenging to find ABCD pattern. But in general, with most stocks, you can discover ABCD patterns.

Let’s take a look at a few quick companies, and you’ll get an idea. I’ll backtrack this to Apple. Precisely the same thing what we discussed. If you take a look here is our A to B and then B to C and C to D pattern.

If we take the measured move from A to B, it should be equal similar to C to D. This pullback from B to C should be about 50% give or take.

Let’s take the measured move:

• A to B (about 90 points)
• C to D (about 80 points)

It’s very close and similar. If we calculate this pullback, there is a 50% pullback. It hits that line entirely and then takes off to the upside.

Now we’re looking at this next price movement that we have.

• Can this be forming another ABCD pattern?

Absolutely.

Let’s take a look from A to B. We have right there it’s coming in it’s hitting right around 50%. It’s similar on the pullback. There’s our line of support. It’s also hitting the line of resistance right here of the previous one. It was coming back to retest it. And if you do the measured move, you can see that we get about 80 points.

We’re looking for potentially a target of 172 for this stock. That’s how you get a necessary target for the upside.

Focus on this: pay close attention to the volume. I usually want to see a larger volume to the upside here. Now with this stock, unfortunately, we got a little bit lighter volume than here on some of the pullbacks.

This is not one thing I like to see. It can happen, but it’s not something I like. The main reason is that I always want to see the volume going with it.

If I go into the weekly chart, you want to see more volume coming in as we’re moving higher. On these pullbacks, you want to see the lighter volume. This is not the case when it comes to Apple. And you can see the volume is also dying down as we continue moving higher.

That’s not usually a good case. But it doesn’t mean the stock can’t go higher. It means I’m more cautious about these stocks as they continue to move higher especially if they have less volume.

You have smaller ABCD patterns within this. Because even to the downside if you’re doing calculations so you could trade those but usually keeping a bear market. The downside movements are generally smaller in terms of time frame.

Amazon – ABCD patterns

Let’s take a look at Amazon. Amazon also had quite a handful of good ABCD patterns. Here we are on a weekly chart. There’s an ABCD pattern from 2016 to 2017. It’s a great stock.

Measured move calculation:

At the start, we got 390 points. I would expect from the pullback we also would get about 390 points. And you can see we got about 365.

When we take a look at the pullback at how the pullback came in – what was it?

Well, you can see we came right into about 50% into that pullback and bounced at the 50%. Almost directly at the $500 price level.The stock made a classic move and what did we get later?

You got volume coming in on the break. Notice these high peaks at the highs we have the excellent volume on the bullish side. You did have this drawing up in volume slowly after the little pullback.

Then finally you got more massive buyers coming in the right after that. This stock looks like a classic ABCD pattern. Just like we’ve shown with Tesla and Apple. If you look at the monthly, you can see it from 2015.

GoPro Technical Analysis – ABCD Pattern

You could take about any stock, and we can do the same thing even with GoPro. As you start evaluating items take a look at a 2 or 3 day in this stock.

That way it’s going to be a little bit easier to evaluate it and see the chart. We look at GoPro, and we look at it to the upside. We could say this started very similar to Tesla. There’s consolidation pattern for a few months – July and August we moved sideways.

Then we had to come in volume. Exceptional bullish volume is coming into the stock. People got excited about it and then finally we had a slight little pullback.

We needed to take a gas break, and you can see the volume drawing up right there. Right under the stock, they’re drying up. And then finally we have more volume coming in and further extension of prices.

We had a slight distribution at the top and then the stock continued to head lower. In this chart, you have the accumulation and the upward move.

We have an upward move of about 33 points. Then let’s look at how far the pullback was. The pullback came into (the lighter pullback) 23.6% and then the stock bounce. We went into a 23.6% Fibonacci sequence and then the stock bounce.

Overall the movement was right around 30 to 33 points. If we take the lows of that B point and we go to the highs, you can see we hit it nearly perfectly 33 points. Look at that, and there is your classic ABCD pattern. If you don’t know much about technical analysis and you want to keep it simple, you could do what I’ve shown you here.

This is what you need to do:

• look for the A to B classic pattern
• look for that volume to come in
• wait for the drawing up of volume
• get in on the C to D leg
• take most of your profits as you approach closer to the D leg

This works the same way in reverse. And this is why GoPro is an excellent example. You can see it to the upside which you could also see it to the downside.

Let’s go into the moment when we’re getting into the stock, and it starts to sell off. And then the counter-trend bounce.

• Can you calculate what’s going to happen?

Well, here as we have the stock moving lower you can see we moved down about 58 points from the highs. We also had this retracement right here to the upside.

We had 58 points; then we got a retracement of about 26 points. If you do this Fibonacci calculation, you can see we don’t hit this perfect at the 50% or 38.2%.

If you do it more around the distribution area rather than the wicks, you’ll get it a little closer. That’s because sometimes here was euphoric buying. It depends on the stock movement. But here when we do this calculation for 2015, you can see we’re hitting it right at 50%.

We have some resistance right there, drying up, trying to break higher. It couldn’t do it, so stocks continue to roll over.

• What do I estimate?

As you go down, you’ll see what happens to the stock. We go 53, 54, 55 and we’ve got a price of $8. That is where we hit.

• What’s going to happen to the stock?

We’re back into this accumulation stage, and often I find that they’re tough to break out of these ranges.

Sometimes it takes multiple years – 3, 5, 10 years. It’s difficult, but you can see the patterns here in this stock. We’re very classic when it comes to the ABCD to the upside and the ABCD to the downside.

I hope this was helpful for you in understanding these price movements. Keep in mind you’re looking for the ABCD pattern first. You’re looking for the swing points and volume.

As the volume starts picking up here was the selling pressure that picked up. Then we had a retracement with lighter volume. And then more selling volume picked up.

The selling pressure stayed there until we hit this low point in early 2016 with GoPro. Now we’re distributing sideways and continuing to sell these stocks short.

Looking at these evaluations, stocks, prices and ABCD patterns, you can see you don’t need a lot of fancy indicators to be able to evaluate a stock on a simple level.

However, there are other things that you’ll go in detail as you continue to get better. But if you’re getting started this is an excellent starting point for you to begin evaluating charts.

What If You Can’t Find ABCD Pattern?

If you go to a specific stock (PepsiCo) and you can’t find an ABCD pattern then move on to another stock.

I’ll show you this right here. Sometimes C and D leg is a little more extended.

If you go to Goldman Sachs, you’ll notice the same thing. If you can’t find ABCD patterns move on to another stock. Especially in the more recent time frame because that’s where you’re trading with.

Maybe you’re trading in the more recent time frame, and you can’t find those ABCD patterns move on to another stock. That’s the case with Bank of America as well.

Important note: ABCD patterns are there, but they’re not there shining red lights at you. They’re not so obvious. You have to be the one that looks for them.

You have to be the one that shows up and listen to the stock. If you have this massive volume, that’s a vital sign. When you have a lot of volume coming in that usually helps find ABCD pattern.

What’s interesting is that the B to C might be sideways. Keep in mind that B to C doesn’t have to go down. It can also be sideways. You’re looking for an expansion move on these stocks. The targets don’t always get hit. It’s giving you some ideas and examples but look for those patterns. It doesn’t matter what company you’re looking for.

I hope that I’ve helped you when it comes to technical analysis case studies and ABCD patterns. Use everything you’ve seen here and keep practicing technical analysis.

Throughout this post, you’ve seen what the right way of understanding price movements, swing points, and volume is. Also, you’ve seen how to break apart a chart in details. That is crucial when it comes to technical analysis.

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Technical Analysis – A Beginner’s Guide

A trading approach that helps investors and traders make decisions by studying statistical patterns derived from trading activities, like price changes and trading volumes.

Prior to accepting a position as the Director of Operations Strategy at DJO Global, Manu was a management consultant with  McKinsey  & Company in Houston. He served clients, including presenting directly to C-level executives, in digital, strategy,  M&A , and operations projects.

Manu holds a PHD in Biomedical Engineering from Duke University and a BA in Physics from Cornell University.

What Is Technical Analysis?

Understanding technical analysis.

• Technical Analysis Using Candlestick Charts

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Candlestick patterns - bearish.

• Technical Analysis Based On Moving Averages

Using Moving Averages

• MACD: The Key Indicator Of Technical Analysis
• Other Indicators For Technical Analysis
• Fundamental Vs. Technical Analysis

Technical analysis is a widely practiced trading approach that helps investors and traders make decisions by studying statistical patterns derived from trading activities, like price changes and trading volumes.

Unlike fundamental analysis , which focuses on a company's financial performance , technical analysis concentrates on analyzing price and volume movements in the market.

It has a long history and is widely accepted among professionals, regulators, and academics, especially in behavioral finance. While technical analysis follows specific rules, its interpretation is subjective and depends on the analyst's style and approach.

This method aims to predict future price movements of securities, such as stocks or currency pairs , by analyzing market data.

The underlying principle is that market participants' collective buying and selling actions accurately reflect all relevant information, establishing a fair market value for the security. It is a valuable tool for traders and investors seeking insights into market trends and potential trading opportunities.

Key Takeaways

• Technical analysis is a trading approach that relies on historical price and volume data to predict future price movements, making it a valuable tool for traders and investors.
• It involves interpreting price movements and patterns, allowing investors to make informed decisions.
• Technical analysis tools include candlestick charts, moving averages, MACD indicators, support/resistance levels, Bollinger Bands, and RSI, offering a comprehensive overview of methods to analyze market trends and potential investment opportunities.
• Technical analysis offers additional tools like Bollinger Bands and the Relative Strength Index (RSI) to help traders assess market conditions and make informed decisions.

Technical analysis is a discipline investors use to evaluate investment opportunities.

If you’re a university student who has recently gained some interest in capital markets but is a complete amateur, this article is perfect for you. Technical analysis is an extremely helpful tool to assist you in making investment decisions.

Many retail day traders make a living purely off investing or trading in the stock exchange and post outrageous daily returns. However, this analysis is not limited to retail investors, as many finance professionals rely on technical indicators to improve their investment decisions. 

Unfortunately, nothing generates absolutely risk-free returns as with all investment strategies . You are still taking a risk with every trade you make, and whatever analysis you carry out is merely attempting to minimize that risk and maximize potential return. 

When evaluating stocks, technical analysis takes a different approach than fundamental analysis. But, generally speaking, there will be clear differences in the beliefs, attitudes, and mindsets toward investment decisions.

Most sophisticated investors understand both viewpoints and will likely use a mix of both. Therefore, if you are new to investing, you need to have a feel for the mentality of investors who use technical analysis before you decide it is the right way to invest. 

So, what are you doing when technically analyzing a stock? You’re looking at historical data (often prices), analyzing it, and computing particular mathematical formulas that allow you to see how the stock has been trading from a unique standpoint.

Subsequently, you will use your analysis to predict how the stock price will move. For example, if the company’s 50-day moving average share price trades below its 200-day moving average, some traders may interpret it as a potential sell signal, although interpretations can vary.

technical analysis using Candlestick charts 

The New York Stock Exchange (NYSE) has trading hours between 9:30 am and 4:00 pm Eastern Time from Monday to Friday. During this time, the market prices of stocks are updated every time a trade takes place, and there are ~12,000 million  shares exchanging hands daily.

It is safe to say that the market prices of shares fluctuate considerably during trading hours. Therefore, when trading stocks , a candlestick chart is one method to incorporate all that fluctuation into a company’s share price. Here is what a typical candlestick looks like: 

A candlestick will show how the price has fluctuated based on the selected period (Eg. 15-minute, 1-hour, or 1-day intervals). For example, if you decide to choose the  1-day interval candlestick , then it will show you: 

• The open price
• The highest price it traded at during the day
• The lowest price it traded at during the day
• The closing price

The candlestick will take on either a green or red color depending on whether the stock’s price has gone up or down during that period. If the stock price increases during the chosen period, the candlestick will be green. If the price has decreased, the candlestick will be red. 

Once you’ve looked at candlesticks for some time, you will start to get very comfortable reading them because they are extremely intuitive and simple to understand. In addition, the benefits of using a candlestick chart over a simple line chart are pretty straightforward. 

For example, if you were researching a company’s stock and decided to plot its daily share price using a line chart, you’d only see its closing prices and nothing about how it traded during the day. That would not be an issue with the candlestick chart. 

However, a candlestick is not without flaws. For example, although you’d be able to get a feel for how prices have fluctuated, you wouldn’t be able to see at what price the stock was mostly traded or the Volume-Weighted Average Price (VWAP) of that stock.

For example, if a stock opened at $2, closed at $5, mostly traded around $3, and reached an intra-day high of $7, you would see a green candlestick with a tiny upper shadow only if the closing price was higher than the opening price. If the closing price was lower, the candlestick would be red.

You can think of the volume-weighted average price that calculates the average price that the stock has traded based on every single time a trade has happened. The VWAP can reduce noise and display the price that most investors are buying the stock at.

Now that you understand candlesticks, there have been patterns that investors use to determine whether there is a bullish or bearish signal based on how a stock has been trading. Then, depending on the signal, investors would decide whether to long or short that stock.

If there is a bullish signal, it is a sign to buy the stock and open a long position .

A long position is where an investor buys a stock anticipating an increase in the price. In contrast, short-selling involves selling borrowed shares with the expectation of buying them back at a lower price, profiting from the price difference.

There are many bullish indicators, with some requiring just one candlestick and others requiring 2-3 candlesticks. It is seen here as follows: 

• Hammer:  Has a short candle with a long lower shadow. Despite the induced selling pressure, the price has managed to close above open, suggesting more buying power.
• Inverted hammer:  Has a short candle with a long upper shadow. This shows that some selling pressure followed strong buying power. The overall indicator is more buyers than sellers because the price has closed above open. This is the exact opposite of a normal bullish hammer candle.
• Bullish engulfing:  Formed when a large green candle “engulfs” the prior trading day red candle. The bullish engulfing pattern typically forms when a stock opens lower and closes higher than the prior trading day. 
• Three white soldiers:  Often occur after a string of selling days and is a reversal from the previous lows. The pattern is indicated by three consecutive days where the price has closed substantially from its open price. 

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Subsequently, bearish indicators signal that it might be a good idea for investors to sell their current open positions or open a new short position . 

Although there are many candlestick indicators, one (general) rule of thumb is that the bearish candlesticks take the opposite shape of the bullish candlesticks, requiring 1-3 candlesticks to form a signal. It is demonstrated as follows:

• Shooting star :  Opposite of an inverted hammer (in terms of price movement, not the look of the candle); has a small candle with a long upper shadow. This shows that buying pressure is running out, and investors are starting to take profits.
• Hanging man:  Opposite of a hammer; has a small candle with a long lower shadow. Even though prices closed above their low intraday, selling has overpowered buying pressure.
• Bearish engulfing:  Opposite of the bullish engulfing; this pattern can be seen when a stock opens higher and closes lower than its prior green trading day. The result is a much larger red candle going downwards, indicating strong selling pressure. 
• Three black crows: The opposite of the three white soldiers. Usually seen as the end of a bull run, prices are expected to trend downwards from thereon. This pattern is formed after 3 consecutive red candles that are considerably large. 

The examples above are by no means an exhaustive list. There are many candlestick patterns out there, and you should dive deeper into this topic yourself if this is something that you find appealing.

However, please do not rely purely on candlestick patterns to invest your hard-earned money. That would be considered more gambling than investing. Instead, use more technical indicators alongside other fundamental indicators.

If you are ever confused between the term “bull” and “bear”, think about how these animals attack. A bull charges by swinging its horns  upwards  (go up), while a bear attacks by swiping its claws  downwards  (go down). This reflects the direction of the stocks.

technical analysis based on Moving averages  

It was concluded above that stocks experience daily volatility and, as a result, adjust frequently. Sometimes, these prices can fluctuate to a strangely large extent although there is no catalyst driving the share price. 

What does “no catalyst” mean? Without going too much into detail, a company’s share price is expected only to adjust dramatically when new relevant information is released (see the  efficient market hypothesis ). 

When there is no new information, the stock’s price is not expected to move very much because there is no reason for it to do so - prices have reflected all presently available information and are considered “efficient”.

Therefore, many investors consider these unjustified fluctuations in the stock price as noise. Essentially, noise can be characterized as deviations from the share price’s efficient value—the greater the daily volatility, the noisier the market. 

The candlestick chart will capture all the noise in a company’s share price. So you’d be able to see all the prices that the stock has traded at, and in some cases, investors might find the noise misleading. Therefore, this is where the moving average will come in. 

The moving average calculates the average share price that a company has traded during the specified period. For example, a company’s 50-day moving average share price will show the average closing price of the last 50 days. 

Calculation

Calculating a company’s moving average price has the benefit of reducing the amount of noise that goes on during trading hours. There are two moving averages:

• Simple moving average (SMA)
• Exponential moving average ( EMA )

The simple moving average calculates a company’s average closing price for a specified duration. The formula can be seen as: 

SMA = (P t + P t-1 + P t-2 +... + P t-n+1 ) / n 

• P = Closing price 
• n = Number of days

Don’t overthink this formula too much, and understand its intuition. A company’s 5-day simple moving average share price represents its average closing price for the last 5 days. It is simple arithmetic math.

The exponential moving average reacts more quickly to price changes due to the exponential smoothing technique, which places greater weight on recent trading days, making it more responsive to recent price movements than the simple moving average (SMA).

In the interest of space, the formula for the exponential moving average will not be shown here. Either way, most stock information websites (Yahoo Finance, Market Watch, etc.) can calculate these formulas automatically. 

How are moving averages useful? Aside from tuning out the noise in capital markets, moving averages can also be used to indicate bull or bear signals and calculate certain indicators. Let’s start with the simplest signals. 

The simplest strategy that investors use is  crossovers.  Investors do this by computing two moving averages (one long & one short period) and decide to buy or sell company stock based on the interaction between both moving averages. A crossover strategy looks like this: 

• Calculate a long simple moving average (commonly 200 days)
• Calculate a short, simple moving average (commonly 50 days)
• It is a bullish sign when the short SMA intersects and crosses  ABOVE  the long SMA. This is considered the formation of a  golden cross , and investors should go long or close existing short positions .
• It is a bearish sign when the short SMA intersects and crosses  BELOW  the long SMA. This is considered a death cross formation, and investors should go short or close open long positions .

The chart below shows a clear example using Apple stock, with the orange line referring to its 50-day SMA and the purple line referring to its 200-day SMA. 

As you can see, selling after the Death Cross did help investors cut some losses, while buying after the Golden Cross provided some returns to investors. This is why crossovers are a useful reference for investors. 

However, one massive flaw in the crossover strategy is that they are inherently lagging. Therefore, there is a potential that investors may have missed the boat as markets have already moved before the crossover strategy picks it up. 

There isn’t really any hard and fast rule behind deciding the number of days into the SMA calculations. That really depends on your time frame as an investor. 

Some people also compare the short SMA to the trading price of the company stock. If the daily share price crosses above the short SMA, then investors would consider that as a buy signal, and vice versa. 

MACD: the key indicator of technical analysis

The MACD indicator really takes your technical analysis ability to the next level. As with all technical indicators, the MACD aims to identify buy and sell signals. There are 3 parts to the MACD: 

• The MACD line 
• The signal line
• The histogram

The MACD line essentially aims to simplify the interpretation of the crossover strategy explained above by comparing a long and a short moving average. The formula for the MACD is as follows: 

MACD = 12 day EMA - 26 day EMA 

Perhaps the long-period moving average isn’t very long, but this is the commonly used formula. The benefit of using 12 & 26 days and the EMA over SMA is that this indicator is much more reactive to market movement. 

Because of this formula, you can expect the MACD to oscillate above and below the value 0. The following are some key interpretations regarding the MACD value: 

• When the MACD takes on a positive value, the 12-day EMA is greater than the 26-day EMA
• When the MACD takes on a negative value, the 26-day EMA exceeds the 12-day EMA.
• When the MACD is 0, it can be interpreted similarly as either a golden cross or a death cross because when the MACD is 0, the 12-day EMA and 26-day EMA intersect. 

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How does the MACD differ from the crossover strategy?  

Well, not only does an increasing (decreasing) MACD indicate that the market is in a bull (bear) run, but there is more to the MACD. 

This would be where the signal line comes in. The signal line is calculated as the 9-day exponential moving average by default . Without going into too much detail, the crossovers between the signal & the MACD line would be your bull and bear signals. Basically: 

• When the MACD line crosses  ABOVE  the signal line, it is a buy signal
• When the MACD line crosses  BELOW  the signal line, it is a sell signal

The last part of this indicator is the histogram. The main purpose of the histogram is to measure the distance between the MACD line and the signal line. Therefore, traders will use the trend in the histogram to identify patterns and potential crossovers. 

Let us look at the example of Apple once again: 

The purple line here represents the MACD line, whereas the orange one represents the signal line. When the MACD crosses above (below) the signal line, investors are recommended to buy (sell) Apple stock. From this chart itself, there is one very clear benefit to using the MACD. 

You can notice that the buy or sell signals are indicated much earlier than when the MACD line hits 0. Therefore, this model does predict earlier movement. 

Remember, using easier moving average crossovers had an issue of being too slow due to inherent lag in the indicator. In this scenario, the 26-day and 12-day EMA intersect and form either a golden or death cross when the MACD line hits 0. 

As with regular crossovers, there isn’t any hard and fast rule behind the number of days used to form the MACD indicator- it is simply the default formula. In most cases, you can change the number of days calculated in this formula as you see fit. 

Because the number of days chosen in the MACD is of a relatively short time frame (and the indicator uses EMA over SMA), the MACD is considered more useful to short-term day traders than long-term investors. 

Other indicators for technical analysis

So far, this article should have done well in introducing you to the basics of this concept. However, there are many more indicators for you to dive deeper into. Therefore, here are some indicators to look at alongside basic descriptions. 

Support and resistance levels

Support and resistance levels are among the most common indicators in technical analysis. By looking at candlestick charts, investors can see points where the share price always experiences reversals. 

The support level can be characterized as the “lowest possible” price at which a company stock will trade. Investors view support levels as an opportunity to open a long position because there is little downside, as prices are expected to bounce back after hitting their support level. 

The resistance level can be characterized as the “highest possible” price at which company stock will trade. Investors view resistance levels as an opportunity to take profits and sell their positions to realize the maximum upside because prices won’t go up any higher. 

However, support and resistance levels aren’t invincible and can be broken. If support levels are broken, it is a bearish sign that investors have lost hope in the stock, and it is expected to trend further downward. The opposite applies to broken resistance levels. 

There is no ideal methodology for determining support and resistance levels. For example, some investors may look at a company’s all-time high and conclude that to be the resistance level. 

Aside from that, investors may notice that a stock has never traded below a certain price for the past year and determine that the support level should be around that price point.

Bollinger bands

Bollinger bands were developed by John Bollinger and were designed to capture most of a company’s share price movement. This is done by constructing an upper and lower band around a company’s 20-day simple moving average. 

Although it is customizable, Bollinger bands are generally constructed two standard deviations away from the default 20-day simple moving average. You can see this from the image above, where the blue lines represent the bands while the dotted orange line is the 20-day SMA. 

Therefore, if the share price is trading near the lower (upper) band, there is some indication that the stock may have been oversold (overbought), and a reversal is expected to occur. 

Investors who believe in the likelihood of this reversal should then capitalize on this opportunity by taking the appropriate action - buy when prices are near the lower band and sell when prices are near the upper band. 

The Bollinger band also has the additional benefit of indicating market volatility. The greater the volatility in a company’s share price, the wider the Bollinger bands would be. 

If they are very far apart, investors could trade the stock more conservatively or aggressively depending on their goal, thus making Bollinger bands a useful tool for investors and traders.

Relative strength index (RSI)

The relative strength index is often compared to the MACD because it is also displayed as an oscillator. Although often compared to the MACD, the process of deriving the RSI is significantly different and should, therefore be treated as a different indicator. 

The RSI is designed to oscillate between the numbers 0 to 100. Any number below 30 is an indicator that a stock has been oversold, and any number above 70 is an indicator that a stock has been overbought. You can see an example using the image below: 

The RSI is calculated by comparing the average gain to the average losses over a default 14-day period. If a company’s share price appreciates too much over 14 days, that will tempt many investors to take a profit. A high RSI value will capture this. 

The RSI will also capture oversold conditions when the share price drops too much over 14 days. Because markets are cyclical, investors who take a long position in company stock when its RSI value is 30 or lower will often see their position appreciate in value. 

Fundamental Vs. Technical analysis

Capital markets play a big part in the economy, from helping individuals generate passive income to giving corporations access to financing for various expenditure types. Therefore, it would be safe to say there isn’t only one way to invest in markets.

As mentioned earlier, technical analysis follows a completely different approach from fundamental analysis. 

Investors who fundamentally analyze company stock will attempt to establish its intrinsic value, which essentially means how much money the company can generate and, thus, how much you should be paying for the company. 

To fundamentally analyze a company, you’d need to understand its industry, its financial statements, revenue drivers, etc. Furthermore, you’d have to understand how this company will operate in the future and what its earnings will look like.

In contrast, investors who technically analyze a company don’t bother much about that type of information. Instead, they’d focus more on the company’s price charts and relevant indicators.

To understand better, let's take a look at the difference table below:

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How To Write A Great Technical Case Study In Three Hours

August 31, 2022

Good technical writing can often seem like magic. It’s not.

Over the last seven years, tons of “best practices” have bubbled up from the Gatsby community to user-facing engineers, product managers, customer success and support teams. We’ve built a culture of documentation so that tribal knowledge isn’t gated by the content team’s bandwidth.

One type of content can be a bit trickier: case studies. Case studies showcase the success of Gatsby users, and let community members see how a “best practice” might work in a real-world scenario. But sometimes non-marketers aren’t sure how to do this, or where to start.

Over the last five years, we’ve written about fifty case studies, and developed a technique to write and publish a case study in three to four hours. I wrote this blog post to share that technique. If you’re interested in telling user or customer stories, you’ll be interested to read this.

Let’s dive in.

Setting Up The Interview

Once someone’s agreed to let you write a case study, there are three guidelines that make for a successful interview. Engineers and product managers may recognize these, because they’re similar to what makes for good open-ended user interviews.

Make sure your interviewees are familiar with both the business and technical site of things. Sometimes a tech lead will be able to do both. Other times, you will need to make sure both an engineering manager and a marketing director, say, are in the room.

Schedule 45-60 minutes, and record the call, and use an auto-transcribing service. If you have less time, you are unlikely to capture the full story. After the initial chit-chat, I ask if I can record the conversation, and then turn it on. For auto-transcription, we use Rewatch internally which offers this functionality.

Establish yourself as an interested listener. In this conversation, you are a curious listener — not a subject matter authority. Pair your status to their status — and go lower if possible! (This can be very difficult for some people.) If you present as an authority figure, your interviewees may focus on whether they did something “right”, they are unlikely to open up, and you’ll struggle to piece the story together.

Conducting The Interview

The interview is the place where you are going to be able to get the highest-bandwidth information. You need to see the story you’re writing as an onion to unpeel. A great interview is a conversation staged as a sequence of “angles” to explain a complex event. 

Angles of attack

Highlight & Lowlights. This a great lead-in to the interview and a natural place to start. You’ll ask questions that trigger people’s emotional memory — the types of things that they might relay to coworkers.

• Goal : Build rapport while starting to get the story.
• Types of questions : “Freeform” questions that you might ask in a user interview.  
• Question examples : “What went well?” “What didn’t go well?” “What was surprising?” “Were there any big bumps in the road?”

Project Chronology. Once you’ve gotten the emotional overview of the project, it’s to plumb people’s factual memory. At its core, a case study is a story. 

• Goal : get a project timeline skeleton that you can relay to your readers — hopefully with some meat on the bones. 
• Types of questions : Focus on what happened, when, and why — and what events caused other events. If your subjects have to pause a couple of times to recall some detail that’s floating around in long-term memory, it’s not a bad thing. 
• Question examples : “So how did you hear about Gatsby?” “So what happened then?” “How did the project start?” “How did you decide to use Gatsby for this project?” “What was the client’s feedback?” “How long did the project take?” “What were the different stages?” “How did the launch go?” “How did the client react?” “What were the business metrics?”

Detail Spotlight. Interspersed with project chronology and highlights / lowlights you can point out parts of their website that they liked, as an invitation for them to tell you more. 

• Before the call: Take a look at what they’ve built, and note 2-3 things they did a great job on, especially if it clearly took a lot of time and effort.
• Goal: Get the backstory behind particularly interesting parts of the website, so you can relay them to the reader. 
• Question example: “I thought the way you did [X thing] was really interesting.” (followed by a pause)

Big Picture. As the interview starts drawing to a close, you’ll want to return to the most important pieces of information you collected earlier and get the “so what”. You’ll use these details to write the first couple sentences of your case study (the lede) and the headline.

• Goal: understand why this project was significant — to the agency, to the client, to the team, perhaps to the world. 
• Types of questions: Return to the most “important” pieces of information you collected earlier and get the “so what.” 
• Question examples: For Little Caesars the “story” was a Super Bowl ad traffic without team stress, so we asked about how the team felt. For Jaxxon it was an e-commerce site launch that doubled conversions, so we asked about the business impact.  

Important Interview Techniques

Stay present, stay curious. You can’t ask these questions perfunctionarily. People notice. You have to be listening very closely. You have to care.

Capture a multi-disciplinary perspective. At the very least, you should be getting both the developer experience and the business results — for each angle of attack! Websites are incredibly cross-functional projects, so there may be another perspective you want to capture as well (design, content architecture, e-commerce, illustration, animation, performance optimization, copywriting…)

Ask good questions, then shut up and listen. You can see this in podcasts recorded by great narrative interviewers like Guy Raz (How I Built This) or Jeff Meyerson (RIP) at Software Engineering Daily.

Dive into interesting details (but remember where you were). When people mention things that are surprising or interesting, don’t wait for them to stop talking — express interest or curiosity, right then, in a way that feels authentic to you. Then, you need to balance two things:

• figure out how to dive deeper right there and then (otherwise the moment will pass)
• remember where you were, so you can bring the conversation back there afterwards (otherwise the conversation will feel fragmented)

Writing Up The Case Study

Block 2 hours of writing time after the interview.

One evening in college doing a journalism internship, I went to a community event remembering a local high school senior who tragically passed away. I got back to the office around 7:30pm, and needed to have a story on my editor’s desk by 9, so I furiously pounded out 500 print-ready words in an hour and a half — in the process apparently oblivious to a colleague who wandered over to ask me something. Like coding, writing rewards blocks of uninterrupted time.

This process works the same way for case studies. Immediately after conducting an interview, you will have an emotional memory of the conversation, along with a sense of the 2-3 most important parts. If you wait, that memory will fade.

Block time immediately after the interview (mornings are best) — so if you had a 9-10am interview, block 10am to noon.

(credit to Sarah Perry )

Step 1: Braindump

The first thing you need to do is brain dump.

Throw your video into whatever tool you’re using to auto-transcribe. Flesh out the notes you were taking during the interview with all the details that come to mind as important but you didn’t capture. Copy and paste all of the notes from your note taking software into a Google Doc.

When your video finishes auto-transcribing (hopefully within 10-15 minutes), search through the transcribed interview to copy paste a bunch of passages in from the interview, to supplement the notes

Step 2: Organize

The technique you need to write quickly is middle-out composition (thanks, Silicon Valley).

Don’t start by writing your lede, or your conclusion. Just group your text together by similarity and then write some headers on top of the information groups that seem important or interesting. These might be important business results, parts of the DX that the developers loved, or the project timeline skeleton. Find the 3 or 4 important takeaways, and write draft subheads (H2).

The skill you’re using here is the ability to create an information hierarchy. 

Step 3: Move to notes, re-add, copy, paste, condense 

At this point you’ll have some very roughly organized information with subheads, along with a lot of clutter that is out of place. Put a “Notes” section, perhaps topped by a horizontal line, at the bottom of your document, and start moving all the information that feels less-important or out of place into the Notes section. Your document will now feel right but incomplete. Start supplementing the important points — with quotes from your interview, screenshots from a website, photos of people, and so on.

Try to avoid writing . Instead, copy and paste from notes and transcript. Then condense three or four sentences a subject said into a one-sentence summary. Rinse and repeat.

Step 4: Add the lede and conclusion

Once you’ve got the skeleton mostly done, you can add a draft lede and conclusion. That’s where you pull in the information you have about the big-picture so-what. Hopefully, that information is in your notes. 

Step 5: Edit until it shines

Now, you’ve got the whole case study. You also probably have run-on sentences, jumbled together, poorly organized sections, a mediocre lede, and so on. That’s okay.

There’s a simple algorithm here: run through the whole story, section by section. Polish whatever’s obvious. Then, return to the start, and do it again.

This avoids the most frequent time sucks of the editing process — perfectionism and overthinking. If you know something’s wrong but don’t know how to fix it, don’t worry. You’ll get it on the next run-through.

Focus on condensing complex ideas. Break long sentences up. Get rid of extraneous details. “Omit needless words.”

If you’re a newer writer, this is a great time to pair with a more experienced writer. Tell them they are the editor and ask them to rewrite confusing sentences or sections. Make it clear that they are driving.

Step 6: Spend a few minutes on the lede and title

People will read the title and first sentence more than anything else, so spend some extra effort. Consider a couple different approaches. Figure out what’s most catchy. 

Step 7: Publish, then seek comments

In an ideal world, this is where you throw it into WordPress and hit publish. You should do this unless you have a very good reason not to! Doing otherwise risks getting stuck in endless review traps waiting for third-party approvals.

Instead, publish it first — and then send it to third-parties to ask if there are any details that need fixing before you publicize it.

Step 8: Go get some lunch

If you’ve done all the previous steps in three to four hours, your brain is probably pretty tired. Be kind to yourself. Go somewhere you really like and think about non-work related stuff for a while. 

Writing great case studies isn’t magic. It’s a skill just like any other. And if you’re in a user- or customer-facing role, you’d do well to learn it. Sharing success stories internally is great, but sharing them externally will multiply your impact.

• Public relations
• Inbound marketing
• Inbound sales
• Marketing onboarding
• CRM implementation
• Sales onboarding
• Guides & downloads

What is a technical case study?

Unless you've been hiding under a rock, chances are you’ve heard all about case studies and application stories being used for marketing purposes, especially in a technical, industrial and engineering context. But the mere idea of producing almost a thousand words of good quality copy and then getting them approved by all parties involved sounds like an insurmountable amount of work. Nonetheless, the task can be easily managed if you know what exactly a case study is and how to tackle each step in developing one.

A technical case study is an analysis of a customer project that used your company’s products and services. It tells the story of how the customer approached the company, what the situation was and what issue they wanted to solve. The foundation of the case study is identifying the customer problem and then recommending and implementing the solution. The conclusion focuses on how the solution was implemented, talks about improvements and overall results and shows why the project was successful.

Characteristics:

• They require approval from other parties involved. Unlike other types of content you create, case studies require that third party endorsement. This is why before putting pen to paper you should first speak to the customer or the end user and get their approval to proceed with the case study. This prior approval is essential, as without explicit approval from the customer the case study does not have a leg to stand on.
• They are longer than other pieces of content and structured to tell a compelling story. Normally case studies contain more information than press releases and as such, they are double in size and require details about the context, the problem you company solved on behalf of the clients, installation journey, outcomes and long-term results.
• They focus on two paradigms: problem-solution-conclusion and feature-benefit relationships. These two facets are the most interesting to your target audience.

Every good case study should be built around the third-party endorsement and as such, quotes and personal testimonies from decision makers are crucial. They give extra weight to the story and allow the reader to put themselves into your customer’s shoes. 

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As explained before, written case studies are about 700-1000 words long and they follow the traditional story format: introduction, problem and context, discussion, solution and outcomes. For information that is not essential to the case study but adds additional details, box outs and graphs are suitable. They can contain facts and figures, information about the customer, the boiler plate (information about your company), quotes and useful website links. Essential for any application story are pictures and if possible videos of the product in action. Some companies use interview-style videos with talking heads explaining the technology or even giving verbal testimonies. 

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Friday Nov 24 2023 10:11

What is technical Analysis: A comprehensive guide

In the complex landscape of financial markets, making informed trading decisions is a critical skill. One of the key methodologies employed by traders worldwide to guide these decisions is technical analysis . This approach involves the systematic study of past market data, primarily price and volume, to forecast future price movements of financial instruments.

Whether you are an experienced trader aiming to sharpen your strategy or a novice trader seeking a foundational understanding of this analysis method, this comprehensive guide is designed to equip you with the knowledge and tools necessary for success.

What is technical analysis?

Technical analysis is a type of financial analysis that looks at historical price movements and trading volumes to predict future price movements in the market. 

It involves studying trends, chart patterns, momentum indicators, and other factors to make informed decisions about trading.

Technical analysis can help traders gain insight into market sentiment, timing their trades for optimal returns.

Why is technical analysis important?

Technical analysis is a critical component of successful financial and trading strategies. It helps investors understand the past performance of a security, identify current trends and anticipate future price movements. 

Technical analysis relies on mathematical calculations and charting techniques to evaluate securities, which can be an invaluable tool for traders to optimize returns and manage risk.

Here are several reasons why technical analysis is considered important by traders and investors:

• Technical analysis helps traders to identify and confirm existing price trends in a security. Recognizing trends early can allow traders to take positions that are in line with the trend, increasing the likelihood of a profitable trade.
• By studying historical price data, traders can identify potential entry and exit points for trades. This is done through various chart patterns and technical indicators, which signal when a security might be overbought or oversold.
• Technical analysis can be a key part of a risk management strategy. For example, traders can set stop-loss orders based on a security's technical profile, helping them to limit potential losses on a trade.
• By focusing on chart patterns and technical indicators, traders can base their decisions on data rather than emotion. This can lead to more rational and potentially more profitable trading decisions.
• Technical analysis can be applied to virtually any security that has historical trading data. This makes it a versatile tool for traders and investors with diverse portfolios.
• While some traders use technical analysis exclusively, others use it in conjunction with fundamental analysis (which evaluates securities based on factors like earnings, valuation, and economic indicators). Combining these two approaches can give traders a more complete picture of a security's prospects.

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Trading CFDs carries a considerable risk of capital loss.

Which tool is best for technical analysis?

There are many tools that can be used for technical analysis, and different traders may have different preferences. Some commonly used tools include:

• These charts visually represent price movements in a specific time period, with the 'body' of the candlestick showing the opening and closing prices, and the 'wicks' showing the highest and lowest prices. Traders use various candlestick patterns (e.g., Doji, Hammer, Engulfing) to predict future price movements based on past patterns.
• Moving averages, such as the Simple Moving Average (SMA) and Exponential Moving Average (EMA), help to smooth out price action and identify the direction of the trend. For example, a common strategy is to look for crossovers between a short-term MA and a long-term MA as potential buy or sell signals.
• The RSI indicator is a momentum oscillator that ranges from 0 to 100. It is generally used to identify overbought or oversold conditions in a traded security. A common interpretation is that an RSI above 70 suggests a security is overbought, while an RSI below 30 suggests it is oversold.
• Created by John Bollinger, these bands consist of a middle band (a simple moving average), and an upper and lower band, which are typically two standard deviations away from the middle band. The bands expand and contract based on market volatility. Price touching the upper band is often interpreted as overbought, and touching the lower band as oversold.
• This is a trend-following momentum indicator that shows the relationship between two moving averages of a security’s price. The MACD indicator is calculated by subtracting the 26-period EMA from the 12-period EMA. Traders look for signal line crossovers, divergences, and overbought/oversold conditions to generate trade signals.

Technical analysis FAQs

How long to learn technical analysis.

The time required to learn technical analysis can vary widely among individuals, depending on their prior knowledge of finance, dedication to studying, and practice. It can take anywhere from a few months for a basic understanding to several years to become highly proficient, with ongoing learning required as markets and tools evolve.

Is technical analysis useful for long-term investment 

Yes, technical analysis can be useful for long-term investment strategies as it can help traders identify and confirm trends, support and resistance levels, and potential entry and exit points.

However, for long-term strategies, it is often beneficial to complement technical analysis with fundamental analysis, which assesses a company's financial health, industry conditions, and broader economic factors, to make more comprehensive and informed decisions.

Technical Analysis in a nutshell

Technical analysis is a powerful tool for traders to analyze and forecast future price movements of securities based on historical price and volume data.

Therefore, it is prudent for traders to use technical analysis as part of a broader, more comprehensive strategy, often in conjunction with fundamental analysis and a strong risk management plan. 

Ultimately, like any skill, proficiency in technical analysis requires study, practice, and ongoing learning in the face of ever-evolving market conditions.

Ready to embark on your trading journey? No matter how much you know, how much money you have, or how you trade, we at markets.com have crafted a trading experience that is tailored just for you.

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“When considering foreign currency (forex) for trading and price predictions, remember that trading CFDs involves a significant degree of risk and could result in capital loss. Past performance is not indicative of any future results. This information is provided for informative purposes only and should not be construed to be investment advice.”

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What Is Technical Analysis?

Understanding technical analysis, using technical analysis.

• Underlying Assumptions
• Technical vs. Fundamental Analysis

Limitations of Technical Analysis

Chartered market technician (cmt).

• Technical Analysis FAQs

Technical Analysis: What It Is and How to Use It in Investing

Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive derivative trading expertise, Adam is an expert in economics and behavioral finance. Adam received his master's in economics from The New School for Social Research and his Ph.D. from the University of Wisconsin-Madison in sociology. He is a CFA charterholder as well as holding FINRA Series 7, 55 & 63 licenses. He currently researches and teaches economic sociology and the social studies of finance at the Hebrew University in Jerusalem.

Charlene Rhinehart is a CPA , CFE, chair of an Illinois CPA Society committee, and has a degree in accounting and finance from DePaul University.

Technical analysis is a trading discipline employed to evaluate investments and identify trading opportunities by analyzing statistical trends gathered from trading activity, such as price movement and volume. Unlike fundamental analysis, which attempts to evaluate a security's value based on business results such as sales and earnings,  technical analysis  focuses on the study of price and volume.

Key Takeaways

• Technical analysis is a trading discipline employed to evaluate investments and identify trading opportunities in price trends and patterns seen on charts.
• Technical analysts believe past trading activity and price changes of a security can be valuable indicators of the security's future price movements.
• Technical analysis may be contrasted with fundamental analysis, which focuses on a company's financials rather than historical price patterns or stock trends.

Investopedia / Candra Huff

Technical analysis tools are used to scrutinize the ways supply and demand for a security will affect changes in price, volume, and implied volatility. It operates from the assumption that past trading activity and price changes of a security can be valuable indicators of the security's future price movements when paired with appropriate investing or trading rules.

It is often used to generate short-term trading signals from various charting tools, but can also help improve the evaluation of a security's strength or weakness relative to the broader market or one of its sectors. This information helps analysts improve their overall valuation estimate.

Technical analysis as we know it today was first introduced by Charles Dow and the Dow Theory in the late 1800s. Several noteworthy researchers including William P. Hamilton, Robert Rhea, Edson Gould, and John Magee further contributed to Dow Theory concepts helping to form its basis. Nowadays technical analysis has evolved to include hundreds of patterns and signals developed through years of research.

Professional analysts often use technical analysis in conjunction with other forms of research. Retail traders may make decisions based solely on the price charts of a security and similar statistics, but practicing equity analysts rarely limit their research to fundamental or technical analysis alone.

Technical analysis can be applied to any security with historical trading data. This includes stocks,  futures ,  commodities , fixed-income, currencies, and other securities. In fact, technical analysis is far more prevalent in commodities and  forex  markets where  traders  focus on short-term price movements.

Technical analysis attempts to forecast the price movement of virtually any tradable instrument that is generally subject to forces of supply and demand, including stocks, bonds, futures, and currency pairs. In fact, some view technical analysis as simply the study of supply and demand forces as reflected in the market price movements of a security.

Technical analysis most commonly applies to price changes, but some analysts track numbers other than just price, such as trading volume or open interest figures.

Technical Analysis Indicators

Across the industry, there are hundreds of patterns and signals that have been developed by researchers to support technical analysis trading. Technical analysts have also developed numerous types of trading systems to help them forecast and trade on price movements.

Some indicators are focused primarily on identifying the current market trend, including support and resistance areas, while others are focused on determining the strength of a trend and the likelihood of its continuation. Commonly used technical indicators and charting patterns include trendlines, channels, moving averages, and momentum indicators.

In general, technical analysts look at the following broad types of indicators:

• Price trends
• Chart patterns
• Volume and momentum indicators
• Oscillators
• Moving averages
• Support and resistance levels

Underlying Assumptions of Technical Analysis

There are two primary methods used to analyze securities and make investment decisions:  fundamental analysis  and technical analysis. Fundamental analysis involves analyzing a company’s financial statements to determine the fair value of the business, while technical analysis assumes that a security's price already reflects all publicly available information and instead focuses on the statistical analysis of price movements .

Technical analysis attempts to understand the market sentiment behind price trends by looking for patterns and trends rather than analyzing a security's fundamental attributes.

Charles Dow released a series of editorials discussing technical analysis theory. His writings included two basic assumptions that have continued to form the framework for technical analysis trading.

• Markets are efficient with values representing factors that influence a security's price, but
• Even random market price movements appear to move in identifiable patterns and trends that tend to repeat over time.

Today the field of technical analysis builds on Dow's work. Professional analysts typically accept three general assumptions for the discipline:

• The market discounts everything: Technical analysts believe that everything from a company's fundamentals to broad market factors to  market psychology  is already priced into the stock. This point of view is congruent with the Efficient Markets Hypothesis (EMH) which assumes a similar conclusion about prices. The only thing remaining is the analysis of price movements, which technical analysts view as the product of supply and demand for a particular stock in the market.
• Price moves in trends: Technical analysts expect that prices, even in random market movements, will exhibit trends regardless of the time frame being observed. In other words, a stock price is more likely to continue a past trend than move erratically. Most technical trading strategies are based on this assumption.
• History tends to repeat itself: Technical analysts believe that history tends to repeat itself. The repetitive nature of price movements is often attributed to market psychology, which tends to be very predictable based on emotions like fear or excitement. Technical analysis uses chart patterns to analyze these emotions and subsequent market movements to understand trends. While many forms of technical analysis have been used for more than 100 years, they are still believed to be relevant because they illustrate patterns in price movements that often repeat themselves.

Technical Analysis vs. Fundamental Analysis

Fundamental analysis and technical analysis, the major schools of thought when it comes to approaching the markets, are at opposite ends of the spectrum. Both methods are used for researching and forecasting future trends in stock prices, and like any investment strategy or philosophy, both have their advocates and adversaries.

Fundamental analysis is a method of evaluating securities by attempting to measure the  intrinsic value  of a stock. Fundamental analysts study everything from the overall economy and industry conditions to the financial condition and management of companies.  Earnings ,  expenses , assets, and liabilities are all important characteristics to fundamental analysts.

Technical analysis differs from fundamental analysis in that the stock's price and volume are the only inputs. The core assumption is that all known fundamentals are factored into price; thus, there is no need to pay close attention to them. Technical analysts do not attempt to measure a security's intrinsic value, but instead, use stock charts to identify patterns and trends that suggest what a stock will do in the future.

Some analysts and academic researchers expect that the EMH demonstrates why they shouldn't expect any actionable information to be contained in historical pric e and volume data; however, by the same reasoning, neither should business fundamentals provide any actionable information. These points of view are known as the weak form and semi-strong form of the EMH.

Another criticism of technical analysis is that history does not repeat itself exactly, so price pattern study is of dubious importance and can be ignored. Prices seem to be better modeled by assuming a random walk.

A third criticism of technical analysis is that it works in some cases but only because it constitutes a self-fulfilling prophecy. For example, many technical traders will place a  stop-loss order  below the 200-day moving average of a certain company. If a large number of traders have done so and the stock reaches this price, there will be a large number of sell orders, which will push the stock down, confirming the movement traders anticipated.

Then, other traders will see the price decrease and also sell their positions, reinforcing the strength of the trend. This short-term selling pressure can be considered self-fulfilling, but it will have little bearing on where the asset's price will be weeks or months from now.

In sum, if enough people use the same signals, they could cause the movement foretold by the signal, but over the long run, this sole group of traders cannot drive the price.

Among professional analysts, the CMT Association supports the largest collection of chartered or certified analysts using technical analysis professionally around the world. The association's Chartered Market Technician (CMT) designation can be obtained after three levels of exams that cover both a broad and deep look at technical analysis tools.

The association now waives Level 1 of the CMT exam for those who are Certified Financial Analyst (CFA) charterholders. This demonstrates how well the two disciplines reinforce each other.

What Assumptions Do Technical Analysts Make?

Professional technical analysts typically accept three general assumptions for the discipline. The first is that, similar to the efficient market hypothesis, the market discounts everything. Second, they expect that prices, even in random market movements, will exhibit trends regardless of the time frame being observed. Finally, they believe that history tends to repeat itself. The repetitive nature of price movements is often attributed to market psychology, which tends to be very predictable based on emotions like fear or excitement. 

What's the Difference Between Fundamental and Technical Analysis?

Fundamental analysis is a method of evaluating securities by attempting to measure the intrinsic value of a stock. The core assumption of technical analysis, on the other hand, is that all known fundamentals are factored into price; thus, there is no need to pay close attention to them. Technical analysts do not attempt to measure a security's intrinsic value, but instead, use stock charts to identify patterns and trends that might suggest what the security will do in the future.

How Can I Learn Technical Analysis?

There are a variety of ways to learn technical analysis . The first step is to learn the basics of investing, stocks, markets, and financials. This can all be done through books, online courses, online material, and classes. Once the basics are understood, from there you can use the same types of materials but those that focus specifically on technical analysis. Investopedia's course on technical analysis is one specific option.

John J. Murphy. "Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications," Page 23. Penguin, 1999.

CFA Institute Research Foundation. " Technical Analysis: Modern Perspectives ," Page 1.

CMT Association. " Technical Analysis: Three Premises ."

CMT Association. " Enroll in the CMT Program ."

CMT Association. " Level I Waiver for CFA Charterholders ."

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Technical Analysis

Technicians (sometimes called chartists) are only interested in the price movements in the market. Despite all the fancy and exotic tools it employs, technical analysis really just studies supply and demand in a market in an attempt to determine what direction, or trend, will continue in the future. In other words, technical analysis attempts to understand the emotions in the market by studying the market itself, as opposed to its components. If you understand the benefits and limitations of technical analysis, it can give you a new set of tools or skills that will enable you to be a better trader or investor. Page 1 of 42) Copyright © 2005, Investopedia.

com – All rights reserved. Investopedia. com – Your Source For Investing Education. In this tutorial, we’ll introduce you to the subject of technical analysis. It’s a broad topic, so we’ll just cover the basics, providing you with the foundation you’ll need to understand more advanced concepts down the road. The Basic Assumptions What Is Technical Analysis? Technical analysis is a method of evaluating securities by analyzing the statistics generated by market activity, such as past prices and volume.

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Technical analysts do not attempt to measure a security’s intrinsic value, but instead use charts and other tools to identify patterns that can suggest future activity. Just as there are many investment styles on the fundamental side, there are also many different types of technical traders. Some rely on chart patterns, others use technical indicators and oscillators, and most use some combination of the two. In any case, technical analysts’ exclusive use of historical price and volume data is what separates them from their fundamental counterparts.

Unlike fundamental analysts, technical analysts don’t care whether a stock is undervalued – the only thing that matters is a security’s past trading data and what information this data can provide about where the security might move in the future.

The field of technical analysis is based on three assumptions: 1. 2. 3. The market discounts everything. Price moves in trends.

History tends to repeat itself. 1. The Market Discounts Everything A major criticism of technical analysis is that it only considers price movement, ignoring the fundamental factors of the company.

However, technical analysis assumes that, at any given time, a stock’s price reflects everything that has or could affect the company – including fundamental factors. Technical analysts believe that the company’s fundamentals, along with broader economic factors and market psychology, are all priced into the stock, removing the need to actually consider these factors separately.

This only leaves the analysis of price movement, which technical theory views as a product of the supply and demand for a particular stock in the market. 2. Price Moves in Trends In technical analysis, price movements are believed to follow trends.

This means that after a trend has been established, the future price movement is more likely to be in the same direction as the trend than to be against it. Most technical trading strategies are based on this assumption.

This tutorial can be found at: http://www. investopedia. com/university/technicalanalysis/default. asp (Page 2 of 42) Copyright © 2006, Investopedia. com – All rights reserved.

Investopedia. com – Your Source For Investing Education. 3. History Tends To Repeat Itself Another important idea in technical analysis is that history tends to repeat itself, mainly in terms of price movement.

The repetitive nature of price movements is attributed to market psychology; in other words, market participants tend to provide a consistent reaction to similar market stimuli over time.

Technical analysis uses chart patterns to analyze market movements and understand trends. Although many of these charts have been used for more than 100 years, they are still believed to be relevant because they illustrate patterns in price movements that often repeat themselves. Not Just for Stocks Technical analysis can be used on any security with historical trading data.

This includes stocks, futures and commodities, fixed-income securities, forex, etc. In this tutorial, we’ll usually analyze stocks in our examples, but keep in mind that these concepts can be applied to any type of security.

In fact, technical analysis is more frequently associated with commodities and forex, where the participants are predominantly traders. Now that you understand the philosophy behind technical analysis, we’ll get into explaining how it really works. One of the best ways to understand what technical analysis is (and is not) is to compare it to fundamental analysis.

We’ll do this in the next section. For further reading, check out Defining Active Trading, Day Trading Strategies For Beginners and What Can Investors Learn From Traders?. Fundamental Vs.

Technical Analysis Technical analysis and fundamental analysis are the two main schools of thought in the financial markets. As we’ve mentioned, technical analysis looks at the price movement of a security and uses this data to predict its future price movements. Fundamental analysis, on the other hand, looks at economic factors, known as fundamentals.

Let’s get into the details of how these two approaches differ, the criticisms against technical analysis and how technical and fundamental analysis can be used together to analyze securities. The Differences Charts vs. Financial Statements At the most basic level, a technical analyst approaches a security from the charts, while a fundamental analyst starts with the financial statements.

(For further reading, see Introduction To Fundamental Analysis and Advanced This tutorial can be found at: http://www. investopedia. com/university/technicalanalysis/default. asp (Page 3 of 42) Copyright © 2006, Investopedia. com – All rights reserved.

Investopedia. com – Your Source For Investing Education. Financial Statement Analysis. ) By looking at the balance sheet, cash flow statement and income statement, a fundamental analyst tries to determine a company’s value. In financial terms, an analyst attempts to measure a company’s intrinsic value.

In this approach, investment decisions are fairly easy to make – if the price of a stock trades below its intrinsic value, it’s a good investment. Although this is an oversimplification (fundamental analysis goes beyond just the financial statements) for the purposes of this tutorial, this simple tenet holds true.

Technical traders, on the other hand, believe there is no reason to analyze a company’s fundamentals because these are all accounted for in the stock’s price. Technicians believe that all the information they need about a stock can be found in its charts. Time Horizon Fundamental analysis takes a relatively long-term approach to analyzing the market compared to technical analysis. While technical analysis can be used on a timeframe of weeks, days or even minutes, fundamental analysis often looks at data over a number of years.

The different timeframes that these two approaches use is a result of the nature of the investing style to which they each adhere. It can take a long time for a company’s value to be reflected in the market, so when a fundamental analyst estimates intrinsic value, a gain is not realized until the stock’s market price rises to its “correct” value. This type of investing is called value investing and assumes that the short-term market is wrong, but that the price of a particular stock will correct itself over the long run. This “long run” can represent a timeframe of as long as several years, in some cases. For more insight, read Warren Buffett: How He Does It and What Is Warren Buffett’s Investing Style? ) Furthermore, the numbers that a fundamentalist analyzes are only released over long periods of time.

Financial statements are filed quarterly and changes in earnings per share don’t emerge on a daily basis like price and volume information. Also remember that fundamentals are the actual characteristics of a business. New management can’t implement sweeping changes overnight and it takes time to create new products, marketing campaigns, supply chains, etc.

Part of the reason that fundamental analysts use a long-term timeframe, therefore, is because the data they use to analyze a stock is generated much more slowly than the price and volume data used by technical analysts. Trading Versus Investing Not only is technical analysis more short term in nature that fundamental analysis, but the goals of a purchase (or sale) of a stock are usually different for This tutorial can be found at: http://www.

investopedia. com/university/technicalanalysis/default. asp (Page 4 of 42) Copyright © 2006, Investopedia. com – All rights reserved. Investopedia. om – Your Source For Investing Education.

each approach. In general, technical analysis is used for a trade, whereas fundamental analysis is used to make an investment. Investors buy assets they believe can increase in value, while traders buy assets they believe they can sell to somebody else at a greater price. The line between a trade and an investment can be blurry, but it does characterize a difference between the two schools. The Critics Some critics see technical analysis as a form of black magic.

Don’t be surprised to see them question the validity of the discipline to the point where they mock its supporters.

In fact, technical analysis has only recently begun to enjoy some mainstream credibility. While most analysts on Wall Street focus on the fundamental side, just about any major brokerage now employs technical analysts as well. Much of the criticism of technical analysis has its roots in academic theory specifically the efficient market hypothesis (EMH). This theory says that the market’s price is always the correct one – any past trading information is already reflected in the price of the stock and, therefore, any analysis to find undervalued securities is useless.

There are three versions of EMH. In the first, called weak form efficiency, all past price information is already included in the current price. According to weak form efficiency, technical analysis can’t predict future movements because all past information has already been accounted for and, therefore, analyzing the stock’s past price movements will provide no insight into its future movements. In the second, semi-strong form efficiency, fundamental analysis is also claimed to be of little use in finding investment opportunities.

The third is strong form efficiency, which states that all information in the market is accounted for in a stock’s price and neither technical nor fundamental analysis can provide investors with an edge.

The vast majority of academics believe in at least the weak version of EMH, therefore, from their point of view, if technical analysis works, market efficiency will be called into question. (For more insight, read What Is Market Efficiency? and Working Through The Efficient Market Hypothesis. ) There is no right answer as to who is correct.

There are arguments to be made on both sides and, therefore, it’s up to you to do the homework and determine your own philosophy. Can They Co-Exist? Although technical analysis and fundamental analysis are seen by many as polar opposites – the oil and water of investing – many market participants have experienced great success by combining the two.

For example, some fundamental analysts use technical analysis techniques to figure out the best time to enter into an undervalued security. Oftentimes, this situation occurs when This tutorial can be found at: http://www. investopedia. om/university/technicalanalysis/default. asp (Page 5 of 42) Copyright © 2006, Investopedia. com – All rights reserved.

Investopedia. com – Your Source For Investing Education. the security is severely oversold. By timing entry into a security, the gains on the investment can be greatly improved. Alternatively, some technical traders might look at fundamentals to add strength to a technical signal. For example, if a sell signal is given through technical patterns and indicators, a technical trader might look to reaffirm his or her decision by looking at some key fundamental data.

Oftentimes, having both the fundamentals and technicals on your side can provide the best-case scenario for a trade. While mixing some of the components of technical and fundamental analysis is not well received by the most devoted groups in each school, there are certainly benefits to at least understanding both schools of thought. In the following sections, we’ll take a more detailed look at technical analysis. The Use Of Trend One of the most important concepts in technical analysis is that of trend.

The meaning in finance isn’t all that different from the general definition of the term – a trend is really nothing more than the general direction in which a security or market is headed.

Take a look at the chart below: Figure 1 This tutorial can be found at: http://www. investopedia. com/university/technicalanalysis/default. asp (Page 6 of 42) Copyright © 2006, Investopedia. com – All rights reserved. Investopedia.

com – Your Source For Investing Education. It isn’t hard to see that the trend in Figure 1 is up. However, it’s not always this easy to see a trend:

Figure 2 There are lots of ups and downs in this chart, but there isn’t a clear indication of which direction this security is headed. A More Formal Definition Unfortunately, trends are not always easy to see. In other words, defining a trend goes well beyond the obvious. In any given chart, you will probably notice that prices do not tend to move in a straight line in any direction, but rather in a series of highs and lows.

In technical analysis, it is the movement of the highs and lows that constitutes a trend. For example, an uptrend is classified as a series of higher highs and higher ows, while a downtrend is one of lower lows and lower highs. This tutorial can be found at: http://www. investopedia. com/university/technicalanalysis/default. asp (Page 7 of 42) Copyright © 2006, Investopedia.

com – All rights reserved. Investopedia. com – Your Source For Investing Education. Figure 3 Figure 3 is an example of an uptrend. Point 2 in the chart is the first high, which is determined after the price falls from this point.

Point 3 is the low that is established as the price falls from the high. For this to remain an uptrend, each successive low must not fall below the previous lowest point or the trend is deemed a reversal.

Types of Trend There are three types of trend: • • • Uptrends Downtrends Sideways/Horizontal Trends As the names imply, when each successive peak and trough is higher, it’s referred to as an upward trend. If the peaks and troughs are getting lower, it’s a downtrend. When there is little movement up or down in the peaks and troughs, it’s a sideways or horizontal trend. If you want to get really technical, you might even say that a sideways trend is actually not a trend on its own, but a lack of a well-defined trend in either direction.

In any case, the market can really only trend in these three ways: up, down or nowhere. For more insight, see Peak-AndTrough Analysis. ) Trend Lengths Along with these three trend directions, there are three trend classifications. A trend of any direction can be classified as a long-term trend, intermediate trend or a short-term trend. In terms of the stock market, a major trend is generally categorized as one lasting longer than a year. An intermediate trend is considered to last between one and three months and a near-term trend is anything less than a month.

A long-term trend is composed of several intermediate trends, which often move against the direction of the major trend.

If the major trend is upward and there is a downward correction in price movement followed by a continuation of the uptrend, the correction is considered to be an intermediate trend. The short-term trends are components of both major and intermediate trends. Take a look a Figure 4 to get a sense of how these three trend lengths might look. This tutorial can be found at: http://www.

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com – Your Source For Investing Education. Figure 4

When analyzing trends, it is important that the chart is constructed to best reflect the type of trend being analyzed. To help identify long-term trends, weekly charts or daily charts spanning a five-year period are used by chartists to get a better idea of the long-term trend. Daily data charts are best used when analyzing both intermediate and short-term trends. It is also important to remember that the longer the trend, the more important it is; for example, a one-month trend is not as significant as a five-year trend. (To read more, see Short-, Intermediate- And Long-Term Trends.

Trendlines A trendline is a simple charting technique that adds a line to a chart to represent the trend in the market or a stock. Drawing a trendline is as simple as drawing a straight line that follows a general trend. These lines are used to clearly show the trend and are also used in the identification of trend reversals. As you can see in Figure 5, an upward trendline is drawn at the lows of an upward trend. This line represents the support the stock has every time it moves from a high to a low.

Notice how the price is propped up by this support.

This type of trendline helps traders to anticipate the point at which a stock’s price will begin moving upwards again. Similarly, a downward trendline is drawn at the highs of the downward trend. This line represents the resistance level that a stock faces every time the price moves from a low to a high. (To read more, see Support & Resistance Basics and Support And Resistance Zones – Part 1 and Part 2.

) This tutorial can be found at: http://www. investopedia. com/university/technicalanalysis/default. asp (Page 9 of 42) Copyright © 2006, Investopedia. com – All rights reserved. Investopedia.

om – Your Source For Investing Education. Figure 5 Channels A channel, or channel lines, is the addition of two parallel trendlines that act as strong areas of support and resistance. The upper trendline connects a series of highs, while the lower trendline connects a series of lows. A channel can slope upward, downward or sideways but, regardless of the direction, the interpretation remains the same. Traders will expect a given security to trade between the two levels of support and resistance until it breaks beyond one of the levels, in which case traders can expect a sharp move in the direction of the break.

Along with clearly displaying the trend, channels are mainly used to illustrate important areas of support and resistance. Figure 6 This tutorial can be found at: http://www. investopedia. com/university/technicalanalysis/default. asp (Page 10 of 42) Copyright © 2006, Investopedia. com – All rights reserved.

Investopedia. com – Your Source For Investing Education. Figure 6 illustrates a descending channel on a stock chart; the upper trendline has been placed on the highs and the lower trendline is on the lows. The price has bounced off of these lines several times, and has remained range-bound for several months.

As long as the price does not fall below the lower line or move beyond the upper resistance, the range-bound downtrend is expected to continue. The Importance of Trend It is important to be able to understand and identify trends so that you can trade with rather than against them.

Two important sayings in technical analysis are “the trend is your friend” and “don’t buck the trend,” illustrating how important trend analysis is for technical traders. Support And Resistance Once you understand the concept of a trend, the next major concept is that of support and resistance.

You’ll often hear technical analysts talk about the ongoing battle between the bulls and the bears, or the struggle between buyers (demand) and sellers (supply). This is revealed by the prices a security seldom moves above (resistance) or below (support). Figure 1 As you can see in Figure 1, support is the price level through which a stock or market seldom falls (illustrated by the blue arrows).

Resistance, on the other hand, is the price level that a stock or market seldom surpasses (illustrated by the red arrows). This tutorial can be found at: http://www. investopedia. com/university/technicalanalysis/default. sp (Page 11 of 42) Copyright © 2006, Investopedia.

com – All rights reserved. Investopedia. com – Your Source For Investing Education. Why Does it Happen? These support and resistance levels are seen as important in terms of market psychology and supply and demand. Support and resistance levels are the levels at which a lot of traders are willing to buy the stock (in the case of a support) or sell it (in the case of resistance). When these trendlines are broken, the supply and demand and the psychology behind the stock’s movements is thought to have shifted, in which case new levels of support and resistance will likely be established.

Round Numbers and Support and Resistance One type of universal support and resistance that tends to be seen across a large number of securities is round numbers. Round numbers like 10, 20, 35, 50, 100 and 1,000 tend be important in support and resistance levels because they often represent the major psychological turning points at which many traders will make buy or sell decisions. Buyers will often purchase large amounts of stock once the price starts to fall toward a major round number such as $50, which makes it more difficult for shares to fall below the level.

On the other hand, sellers start to sell off a stock as it moves toward a round number peak, making it difficult to move past this upper level as well. It is the increased buying and selling pressure at these levels that makes them important points of support and resistance and, in many cases, major psychological points as well.

Role Reversal Once a resistance or support level is broken, its role is reversed. If the price falls below a support level, that level will become resistance. If the price rises above a resistance level, it will often become support.

As the price moves past a level of support or resistance, it is thought that supply and demand has shifted, causing the breached level to reverse its role. For a true reversal to occur, however, it is important that the price make a strong move through either the support or resistance.

(For further reading, see Retracement Or Reversal: Know The Difference. ) Figure 2 This tutorial can be found at: http://www. investopedia. com/university/technicalanalysis/default. asp (Page 12 of 42) Copyright © 2006, Investopedia.

com – All rights reserved. Investopedia. om – Your Source For Investing Education. For example, as you can see in Figure 2, the dotted line is shown as a level of resistance that has prevented the price from heading higher on two previous occasions (Points 1 and 2). However, once the resistance is broken, it becomes a level of support (shown by Points 3 and 4) by propping up the price and preventing it from heading lower again.

Many traders who begin using technical analysis find this concept hard to believe and don’t realize that this phenomenon occurs rather frequently, even with some of the most well-known companies.

For example, as you can see in Figure 3, this phenomenon is evident on the Wal-Mart Stores Inc. (WMT) chart between 2003 and 2006. Notice how the role of the $51 level changes from a strong level of support to a level of resistance. Figure 3 In almost every case, a stock will have both a level of support and a level of resistance and will trade in this range as it bounces between these levels. This is most often seen when a stock is trading in a generally sideways manner as the price moves through successive peaks and troughs, testing resistance and support.

The Importance of Support and Resistance Support and resistance analysis is an important part of trends because it can be used to make trading decisions and identify when a trend is reversing. For example, if a trader identifies an important level of resistance that has been tested several times but never broken, he or she may decide to take profits as the security moves toward this point because it is unlikely that it will move past this level. This tutorial can be found at: http://www. investopedia. com/university/technicalanalysis/default. asp (Page 13 of 42) Copyright © 2006, Investopedia.

om – All rights reserved. Investopedia. com – Your Source For Investing Education. Support and resistance levels both test and confirm trends and need to be monitored by anyone who uses technical analysis. As long as the price of the share remains between these levels of support and resistance, the trend is likely to continue. It is important to note, however, that a break beyond a level of support or resistance does not always have to be a reversal.

For example, if prices moved above the resistance levels of an upward trending channel, the trend has accelerated, not reversed.

This means that the price appreciation is expected to be faster than it was in the channel. Being aware of these important support and resistance points should affect the way that you trade a stock. Traders should avoid placing orders at these major points, as the area around them is usually marked by a lot of volatility. If you feel confident about making a trade near a support or resistance level, it is important that you follow this simple rule: do not place orders directly at the support or resistance level. This is because in many cases, the price never actually reaches the whole number, but flirts with it instead.

So if you’re bullish on a stock that is moving toward an important support level, do not place the trade at the support level. Instead, place it above the support level, but within a few points. On the other hand, if you are placing stops or short selling, set up your trade price at or below the level of support. The Importance Of Volume To this point, we’ve only discussed the price of a security. While price is the primary item of concern in technical analysis, volume is also extremely important. What is Volume? Volume is simply the number of shares or contracts that trade over a given period of time, usually a day.

The higher the volume, the more active the security. To determine the movement of the volume (up or down), chartists look at the volume bars that can usually be found at the bottom of any chart. Volume bars illustrate how many shares have traded per period and show trends in the same way that prices do. (For further reading, see Price Patterns – Part 3, Gauging Support And Resistance With Price By Volume. ) This tutorial can be found at: http://www.

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Why Volume is Important Volume is an important aspect of technical analysis because it is used to confirm trends and chart patterns. Any price movement up or down with relatively high volume is seen as a stronger, more relevant move than a similar move with weak volume. Therefore, if you are looking at a large price movement, you should also examine the volume to see whether it tells the same story. Say, for example, that a stock jumps 5% in one trading day after being in a long downtrend. Is this a sign of a trend reversal?

This is where volume helps traders. If volume is high during the day relative to the average daily volume, it is a sign that the reversal is probably for real.

On the other hand, if the volume is below average, there may not be enough conviction to support a true trend reversal. (To read more, check out Trading Volume – Crowd Psychology. ) Volume should move with the trend. If prices are moving in an upward trend, volume should increase (and vice versa). If the previous relationship between volume and price movements starts to deteriorate, it is usually a sign of weakness in the trend.

For example, if the stock is in an uptrend but the up trading days are marked with lower volume, it is a sign that the trend is starting to lose its legs and may soon end.

When volume tells a different story, it is a case of divergence, which refers to a contradiction between two different indicators. The simplest example of divergence is a clear upward trend on declining volume. (For additional insight, read Divergences, Momentum And Rate Of Change. ) Volume and Chart Patterns The other use of volume is to confirm chart patterns. Patterns such as head and This tutorial can be found at: http://www.

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shoulders, triangles, flags and other price patterns can be confirmed with volume, a process which we’ll describe in more detail later in this tutorial. In most chart patterns, there are several pivotal points that are vital to what the chart is able to convey to chartists. Basically, if the volume is not there to confirm the pivotal moments of a chart pattern, the quality of the signal formed by the pattern is weakened.

Volume Precedes Price Another important idea in technical analysis is that price is preceded by volume. Volume is closely monitored by technicians and chartists to form ideas on upcoming trend reversals.

If volume is starting to decrease in an uptrend, it is usually a sign that the upward run is about to end. Now that we have a better understanding of some of the important factors of technical analysis, we can move on to charts, which help to identify trading opportunities in prices movements. What Is A Chart? In technical analysis, charts are similar to the charts that you see in any business setting.

A chart is simply a graphical representation of a series of prices over a set time frame. For example, a chart may show a stock’s price movement over a one-year period, where each point on the graph represents the closing price for each day the stock is traded: Figure 1 Figure 1 provides an example of a basic chart. It is a representation of the price This tutorial can be found at: http://www.

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movements of a stock over a 1. year period. The bottom of the graph, running horizontally (x-axis), is the date or time scale. On the right hand side, running vertically (y-axis), the price of the security is shown. By looking at the graph we see that in October 2004 (Point 1), the price of this stock was around $245, whereas in June 2005 (Point 2), the stock’s price is around $265.

This tells us that the stock has risen between October 2004 and June 2005. Chart Properties There are several things that you should be aware of when looking at a chart, as these factors can affect the information that is provided.

They include the time scale, the price scale and the price point properties used. The Time Scale The time scale refers to the range of dates at the bottom of the chart, which can vary from decades to seconds. The most frequently used time scales are intraday, daily, weekly, monthly, quarterly and annually.

The shorter the time frame, the more detailed the chart. Each data point can represent the closing price of the period or show the open, the high, the low and the close depending on the chart used. Intraday charts plot price movement within the period of one day.

This means that the time scale could be as short as five minutes or could cover the whole trading day from the opening bell to the closing bell. Daily charts are comprised of a series of price movements in which each price point on the chart is a full day’s trading condensed into one point. Again, each point on the graph can be simply the closing price or can entail the open, high, low and close for the stock over the day.

These data points are spread out over weekly, monthly and even yearly time scales to monitor both short-term and intermediate trends in price movement.

Weekly, monthly, quarterly and yearly charts are used to analyze longer term trends in the movement of a stock’s price. Each data point in these graphs will be a condensed version of what happened over the specified period. So for a weekly chart, each data point will be a representation of the price movement of the week. For example, if you are looking at a chart of weekly data spread over a five-year period and each data point is the closing price for the week, the price that is plotted will be the closing price on the last trading day of the week, which is usually a Friday.

The Price Scale and Price Point Properties The price scale is on the right-hand side of the chart. It shows a stock’s current price and compares it to past data points. This may seem like a simple concept in that the price scale goes from lower prices to higher prices as you move along This tutorial can be found at: http://www. investopedia. com/university/technicalanalysis/default.

asp (Page 17 of 42) Copyright © 2006, Investopedia. com – All rights reserved. Investopedia. com – Your Source For Investing Education. the scale from the bottom to the top.

The problem, however, is in the structure of the scale itself. A scale can either be constructed in a linear (arithmetic) or logarithmic way, and both of these options are available on most charting services. If a price scale is constructed using a linear scale, the space between each price point (10, 20, 30, 40) is separated by an equal amount. A price move from 10 to 20 on a linear scale is the same distance on the chart as a move from 40 to 50. In other words, the price scale measures moves in absolute terms and does not show the effects of percent change.

Figure 2 If a price scale is in logarithmic terms, then the distance between points will be equal in terms of percent change. A price change from 10 to 20 is a 100% increase in the price while a move from 40 to 50 is only a 25% change, even though they are represented by the same distance on a linear scale. On a logarithmic scale, the distance of the 100% price change from 10 to 20 will not be the same as the 25% change from 40 to 50. In this case, the move from 10 to 20 is represented by a larger space one the chart, while the move from 40 to 50, is represented by a smaller space because, percentage-wise, it indicates a smaller move.

In Figure 2, the logarithmic price scale on the right leaves the same amount of space between 10 and 20 as it does between 20 and 40 because these both represent 100% increases.

Chart Types There are four main types of charts that are used by investors and traders depending on the information that they are seeking and their individual skill levels. The chart types are: the line chart, the bar chart, the candlestick chart and This tutorial can be found at: http://www. investopedia. com/university/technicalanalysis/default. asp (Page 18 of 42) Copyright © 2006, Investopedia.

om – All rights reserved. Investopedia. com – Your Source For Investing Education. the point and figure chart. In the following sections, we will focus on the S&P 500 Index during the period of January 2006 through May 2006. Notice how the data used to create the charts is the same, but the way the data is plotted and shown in the charts is different.

Line Chart The most basic of the four charts is the line chart because it represents only the closing prices over a set period of time. The line is formed by connecting the closing prices over the time frame.

Line charts do not provide visual information of the trading range for the individual points such as the high, low and opening prices. However, the closing price is often considered to be the most important price in stock data compared to the high and low for the day and this is why it is the only value used in line charts. Figure 1: A line chart Bar Charts The bar chart expands on the line chart by adding several more key pieces of information to each data point.

The chart is made up of a series of vertical lines that represent each data point.

This vertical line represents the high and low for the trading period, along with the closing price. The close and open are represented on the vertical line by a horizontal dash. The opening price on a bar chart is illustrated by the dash that is located on the left side of the vertical bar. Conversely, the close is represented by the dash on the right.

Generally, if the left dash (open) is lower than the right dash (close) then the bar will be shaded black, representing an up period for the stock, which means it has gained value. A bar that is colored red signals that the stock has gone down in value over that period.

When this is the case, the dash on the right (close) is lower than the dash on the left (open). This tutorial can be found at: http://www. investopedia.

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Figure 2: A bar chart Candlestick Charts The candlestick chart is similar to a bar chart, but it differs in the way that it is visually constructed. Similar to the bar chart, the candlestick also has a thin vertical line showing the period’s trading range.

The difference comes in the formation of a wide bar on the vertical line, which illustrates the difference between the open and close. And, like bar charts, candlesticks also rely heavily on the use of colors to explain what has happened during the trading period. A major problem with the candlestick color configuration, however, is that different sites use different standards; therefore, it is important to understand the candlestick configuration used at the chart site you are working with. There are two color constructs for days up and one for days that the price falls.

When the price of the stock is up and closes above the opening trade, the candlestick will usually be white or clear. If the stock has traded down for the period, then the candlestick will usually be red or black, depending on the site. If the stock’s price has closed above the previous day’s close but below the day’s open, the candlestick will be black or filled with the color that is used to indicate an up day. (To read more, see The Art Of Candlestick Charting – Part 1, Part 2, Part 3 and Part 4. ) This tutorial can be found at: http://www.

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com – Your Source For Investing Education. Figure 3: A candlestick chart Point and Figure Charts The point and figure chart is not well known or used by the average investor but it has had a long history of use dating back to the first technical traders. This type of chart reflects price movements and is not as concerned about time and volume in the formulation of the points. The point and figure chart removes the noise, or insignificant price movements, in the stock, which can distort traders’ views of the price trends.

These types of charts also try to neutralize the skewing effect that time has on chart analysis. (For further reading, see Point And Figure Charting.

) Figure 4: A point and figure chart This tutorial can be found at: http://www. investopedia. com/university/technicalanalysis/default. asp (Page 21 of 42) Copyright © 2006, Investopedia. com – All rights reserved. Investopedia.

com – Your Source For Investing Education. When first looking at a point and figure chart, you will notice a series of Xs and Os. The Xs represent upward price trends and the Os represent downward price trends.

There are also numbers and letters in the chart; these represent months, and give investors an idea of the date. Each box on the chart represents the price scale, which adjusts depending on the price of the stock: the higher the stock’s price the more each box represents. On most charts where the price is between $20 and $100, a box represents $1, or 1 point for the stock.

The other critical point of a point and figure chart is the reversal criteria. This is usually set at three but it can also be set according to the chartist’s discretion.

The reversal criteria set how much the price has to move away from the high or low in the price trend to create a new trend or, in other words, how much the price has to move in order for a column of Xs to become a column of Os, or vice versa. When the price trend has moved from one trend to another, it shifts to the right, signaling a trend change. Conclusion Charts are one of the most fundamental aspects of technical analysis.

It is important that you clearly understand what is being shown on a chart and the information that it provides.

Now that we have an idea of how charts are constructed, we can move on to the different types of chart patterns. Chart Patterns A chart pattern is a distinct formation on a stock chart that creates a trading signal, or a sign of future price movements. Chartists use these patterns to identify current trends and trend reversals and to trigger buy and sell signals. In the first section of this tutorial, we talked about the three assumptions of technical analysis, the third of which was that in technical analysis, history repeats itself.

The theory behind chart patters is based on this assumption.

The idea is that certain patterns are seen many times, and that these patterns signal a certain high probability move in a stock. Based on the historic trend of a chart pattern setting up a certain price movement, chartists look for these patterns to identify trading opportunities. While there are general ideas and components to every chart pattern, there is no chart pattern that will tell you with 100% certainty where a security is headed. This creates some leeway and debate as to what a good pattern looks like, and is a major reason why charting is often seen as more of an art than a science.

For more insight, see Is finance an art or a science? ) There are two types of patterns within this area of technical analysis, reversal This tutorial can be found at: http://www. investopedia. com/university/technicalanalysis/default. asp (Page 22 of 42) Copyright © 2006, Investopedia. com – All rights reserved.

Investopedia. com – Your Source For Investing Education. and continuation. A reversal pattern signals that a prior trend will reverse upon completion of the pattern. A continuation pattern, on the other hand, signals that a trend will continue once the pattern is complete.

These patterns can be found over charts of any timeframe.

In this section, we will review some of the more popular chart patterns. (To learn more, check out Continuation Patterns – Part 1, Part 2, Part 3 and Part 4. ) Head and Shoulders This is one of the most popular and reliable chart patterns in technical analysis. Head and shoulders is a reversal chart pattern that when formed, signals that the security is likely to move against the previous trend. As you can see in Figure 1, there are two versions of the head and shoulders chart pattern.

Head and shoulders top (shown on the left) is a hart pattern that is formed at the high of an upward movement and signals that the upward trend is about to end. Head and shoulders bottom, also known as inverse head and shoulders (shown on the right) is the lesser known of the two, but is used to signal a reversal in a downtrend. Figure 1: Head and shoulders top is shown on the left. Head and shoulders bottom, or inverse head and shoulders, is on the right. Both of these head and shoulders patterns are similar in that there are four main parts: two shoulders, a head and a neckline. Also, each individual head and shoulder is comprised of a high and a low.

For example, in the head and shoulders top image shown on the left side in Figure 1, the left shoulder is made up of a high followed by a low. In this pattern, the neckline is a level of support or resistance. Remember that an upward trend is a period of successive rising highs and rising lows. The head and shoulders chart pattern, therefore, illustrates a weakening in a trend by showing the deterioration in the successive movements of the highs and lows. (To learn more, see Price Patterns – Part 2.

) This tutorial can be found at: http://www. investopedia. om/university/technicalanalysis/default. asp (Page 23 of 42) Copyright © 2006, Investopedia. com – All rights reserved. Investopedia.

com – Your Source For Investing Education. Cup and Handle A cup and handle chart is a bullish continuation pattern in which the upward trend has paused but will continue in an upward direction once the pattern is confirmed. Figure 2 As you can see in Figure 2, this price pattern forms what looks like a cup, which is preceded by an upward trend. The handle follows the cup formation and is formed by a generally downward/sideways movement in the security’s price.

Once the price movement pushes above the resistance lines formed in the handle, the upward trend can continue.

There is a wide ranging time frame for this type of pattern, with the span ranging from several months to more than a year. Double Tops and Bottoms This chart pattern is another well-known pattern that signals a trend reversal – it is considered to be one of the most reliable and is commonly used. These patterns are formed after a sustained trend and signal to chartists that the trend is about to reverse.

The pattern is created when a price movement tests support or resistance levels twice and is unable to break through. This pattern is often used to signal intermediate and long-term trend reversals. This tutorial can be found at: http://www.

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com – Your Source For Investing Education. Figure 3: A double top pattern is shown on the left, while a double bottom pattern is shown on the right.

In the case of the double top pattern in Figure 3, the price movement has twice tried to move above a certain price level. After two unsuccessful attempts at pushing the price higher, the trend reverses and the price heads lower. In the case of a double bottom (shown on the right), the price movement has tried to go lower twice, but has found support each time. After the second bounce off of the support, the security enters a new trend and heads upward.

(For more in-depth reading, see The Memory Of Price and Price Patterns – Part 4. Triangles Triangles are some of the most well-known chart patterns used in technical analysis. The three types of triangles, which vary in construct and implication, are the symmetrical triangle, ascending and descending triangle. These chart patterns are considered to last anywhere from a couple of weeks to several months. This tutorial can be found at: http://www.

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Figure 4 The symmetrical triangle in Figure 4 is a pattern in which two trendlines converge toward each other. This pattern is neutral in that a breakout to the upside or downside is a confirmation of a trend in that direction. In an ascending triangle, the upper trendline is flat, while the bottom trendline is upward sloping. This is generally thought of as a bullish pattern in which chartists look for an upside breakout. In a descending triangle, the lower trendline is flat and the upper trendline is descending. This is generally seen as a bearish pattern where chartists look for a downside breakout.

Flag and Pennant These two short-term chart patterns are continuation patterns that are formed when there is a sharp price movement followed by a generally sideways price movement. This pattern is then completed upon another sharp price movement in the same direction as the move that started the trend. The patterns are generally thought to last from one to three weeks. This tutorial can be found at: http://www. investopedia.

com/university/technicalanalysis/default. asp (Page 26 of 42) Copyright © 2006, Investopedia. com – All rights reserved. Investopedia. om – Your Source For Investing Education.

Figure 5 As you can see in Figure 5, there is little difference between a pennant and a flag. The main difference between these price movements can be seen in the middle section of the chart pattern. In a pennant, the middle section is characterized by converging trendlines, much like what is seen in a symmetrical triangle. The middle section on the flag pattern, on the other hand, shows a channel pattern, with no convergence between the trendlines. In both cases, the trend is expected to continue when the price moves above the upper trendline.

Wedge The wedge chart pattern can be either a continuation or reversal pattern. It is similar to a symmetrical triangle except that the wedge pattern slants in an upward or downward direction, while the symmetrical triangle generally shows a sideways movement. The other difference is that wedges tend to form over longer periods, usually between three and six months. Figure 6 The fact that wedges are classified as both continuation and reversal patterns can make reading signals confusing. However, at the most basic level, a falling This tutorial can be found at: http://www. investopedia. om/university/technicalanalysis/default. asp (Page 27 of 42) Copyright © 2006, Investopedia. com – All rights reserved. Investopedia. com – Your Source For Investing Education. wedge is bullish and a rising wedge is bearish. In Figure 6, we have a falling wedge in which two trendlines are converging in a downward direction. If the price was to rise above the upper trendline, it would form a continuation pattern, while a move below the lower trendline would signal a reversal pattern. Gaps A gap in a chart is an empty space between a trading period and the following trading period.

This occurs when there is a large difference in prices between two sequential trading periods. For example, if the trading range in one period is between $25 and $30 and the next trading period opens at $40, there will be a large gap on the chart between these two periods. Gap price movements can be found on bar charts and candlestick charts but will not be found on point and figure or basic line charts. Gaps generally show that something of significance has happened in the security, such as a better-than-expected earnings announcement. There are three main types of gaps, breakaway, runaway (measuring) and exhaustion.

A breakaway gap forms at the start of a trend, a runaway gap forms during the middle of a trend and an exhaustion gap forms near the end of a trend. (For more insight, read Playing The Gap. ) Triple Tops and Bottoms Triple tops and triple bottoms are another type of reversal chart pattern in chart analysis. These are not as prevalent in charts as head and shoulders and double tops and bottoms, but they act in a similar fashion. These two chart patterns are formed when the price movement tests a level of support or resistance three times and is unable to break through; this signals a reversal of the prior trend.

Figure 7 This tutorial can be found at: http://www. investopedia. com/university/technicalanalysis/default. asp (Page 28 of 42) Copyright © 2006, Investopedia. com – All rights reserved. Investopedia. com – Your Source For Investing Education. Confusion can form with triple tops and bottoms during the formation of the pattern because they can look similar to other chart patterns. After the first two support/resistance tests are formed in the price movement, the pattern will look like a double top or bottom, which could lead a chartist to enter a reversal position too soon.

Rounding Bottom A rounding bottom, also referred to as a saucer bottom, is a long-term reversal pattern that signals a shift from a downward trend to an upward trend. This pattern is traditionally thought to last anywhere from several months to several years. Figure 8 A rounding bottom chart pattern looks similar to a cup and handle pattern but without the handle. The long-term nature of this pattern and the lack of a confirmation trigger, such as the handle in the cup and handle, makes it a difficult pattern to trade. We have finished our look at some of the more popular chart patterns.

You should now be able to recognize each chart pattern as well the signal it can form for chartists. We will now move on to other technical techniques and examine how they are used by technical traders to gauge price movements. Moving Averages Most chart patterns show a lot of variation in price movement. This can make it difficult for traders to get an idea of a security’s overall trend. One simple method traders use to combat this is to apply moving averages. A moving average is the average price of a security over a set amount of time. By plotting a This tutorial can be found at: http://www. investopedia. om/university/technicalanalysis/default. asp (Page 29 of 42) Copyright © 2006, Investopedia. com – All rights reserved. Investopedia. com – Your Source For Investing Education. security’s average price, the price movement is smoothed out. Once the day-today fluctuations are removed, traders are better able to identify the true trend and increase the probability that it will work in their favor. (To learn more, read the Moving Averages tutorial. ) Types of Moving Averages There are a number of different types of moving averages that vary in the way they are calculated, but how each average is interpreted remains the same.

The calculations only differ in regards to the weighting that they place on the price data, shifting from equal weighting of each price point to more weight being placed on recent data. The three most common types of moving averages are simple, linear and exponential. Simple Moving Average (SMA) This is the most common method used to calculate the moving average of prices. It simply takes the sum of all of the past closing prices over the time period and divides the result by the number of prices used in the calculation. For example, in a 10-day moving average, the last 10 closing prices are added together and then divided by 10.

As you can see in Figure 1, a trader is able to make the average less responsive to changing prices by increasing the number of periods used in the calculation. Increasing the number of time periods in the calculation is one of the best ways to gauge the strength of the long-term trend and the likelihood that it will reverse. Figure 1 Many individuals argue that the usefulness of this type of average is limited because each point in the data series has the same impact on the result regardless of where it occurs in the sequence.

The critics argue that the most recent data is more important and, therefore, it should also have a higher This tutorial can be found at: http://www. investopedia. com/university/technicalanalysis/default. asp (Page 30 of 42) Copyright © 2006, Investopedia. com – All rights reserved. Investopedia. com – Your Source For Investing Education. weighting. This type of criticism has been one of the main factors leading to the invention of other forms of moving averages. Linear Weighted Average This moving average indicator is the least common out of the three and is used to address the problem of the equal weighting.

The linear weighted moving average is calculated by taking the sum of all the closing prices over a certain time period and multiplying them by the position of the data point and then dividing by the sum of the number of periods. For example, in a five-day linear weighted average, today’s closing price is multiplied by five, yesterday’s by four and so on until the first day in the period range is reached. These numbers are then added together and divided by the sum of the multipliers.

Exponential Moving Average (EMA) This moving average calculation uses a smoothing factor to place a higher weight on recent data points and is regarded as much more efficient than the linear weighted average. Having an understanding of the calculation is not generally required for most traders because most charting packages do the calculation for you. The most important thing to remember about the exponential moving average is that it is more responsive to new information relative to the simple moving average. This responsiveness is one of the key factors of why this is the moving average of choice among many technical traders.

As you can see in Figure 2, a 15-period EMA rises and falls faster than a 15-period SMA. This slight difference doesn’t seem like much, but it is an important factor to be aware of since it can affect returns. Figure 2 Major Uses of Moving Averages Moving averages are used to identify current trends and trend reversals as well as to set up support and resistance levels. Moving averages can be used to quickly identify whether a security is moving in This tutorial can be found at: http://www. investopedia. com/university/technicalanalysis/default. asp (Page 31 of 42) Copyright © 2006, Investopedia. om – All rights reserved. Investopedia. com – Your Source For Investing Education. an uptrend or a downtrend depending on the direction of the moving average. As you can see in Figure 3, when a moving average is heading upward and the price is above it, the security is in an uptrend. Conversely, a downward sloping moving average with the price below can be used to signal a downtrend. Figure 3 Another method of determining momentum is to look at the order of a pair of moving averages. When a short-term average is above a longer-term average, the trend is up.

On the other hand, a long-term average above a shorter-term average signals a downward movement in the trend. Moving average trend reversals are formed in two main ways: when the price moves through a moving average and when it moves through moving average crossovers. The first common signal is when the price moves through an important moving average. For example, when the price of a security that was in an uptrend falls below a 50-period moving average, like in Figure 4, it is a sign that the uptrend may be reversing. This tutorial can be found at: http://www. investopedia. om/university/technicalanalysis/default. asp (Page 32 of 42) Copyright © 2006, Investopedia. com – All rights reserved. Investopedia. com – Your Source For Investing Education. Figure 4 The other signal of a trend reversal is when one moving average crosses through another. For example, as you can see in Figure 5, if the 15-day moving average crosses above the 50-day moving average, it is a positive sign that the price will start to increase. Figure 5 If the periods used in the calculation are relatively short, for example 15 and 35, this could signal a short-term trend reversal.

On the other hand, when two averages with relatively long time frames cross over (50 and 200, for example), this is used to suggest a long-term shift in trend. Another major way moving averages are used is to identify support and resistance levels. It is not uncommon to see a stock that has been falling stop its decline and reverse direction once it hits the support of a major moving average. A move through a major moving average is often used as a signal by technical traders that the trend is reversing. For example, if the price breaks through the This tutorial can be found at: http://www. nvestopedia. com/university/technicalanalysis/default. asp (Page 33 of 42) Copyright © 2006, Investopedia. com – All rights reserved. Investopedia. com – Your Source For Investing Education. 200-day moving average in a downward direction, it is a signal that the uptrend is reversing. Figure 6 Moving averages are a powerful tool for analyzing the trend in a security. They provide useful support and resistance points and are very easy to use. The most common time frames that are used when creating moving averages are the 200day, 100-day, 50-day, 20-day and 10-day.

The 200-day average is thought to be a good measure of a trading year, a 100-day average of a half a year, a 50-day average of a quarter of a year, a 20-day average of a month and 10-day average of two weeks. Moving averages help technical traders smooth out some of the noise that is found in day-to-day price movements, giving traders a clearer view of the price trend. So far we have been focused on price movement, through charts and averages. In the next section, we’ll look at some other techniques used to confirm price movement and patterns. Indicators And Oscillators

Indicators are calculations based on the price and the volume of a security that measure such things as money flow, trends, volatility and momentum. Indicators are used as a secondary measure to the actual price movements and add additional information to the analysis of securities. Indicators are used in two main ways: to confirm price movement and the quality of chart patterns, and to form buy and sell signals. There are two main types of indicators: leading and lagging. A leading indicator precedes price movements, giving them a predictive quality, while a lagging indicator is a confirmation tool because it follows price movement.

A leading indicator is thought to be the strongest during periods of sideways or nontrending trading ranges, while the lagging indicators are still useful during This tutorial can be found at: http://www. investopedia. com/university/technicalanalysis/default. asp (Page 34 of 42) Copyright © 2006, Investopedia. com – All rights reserved. Investopedia. com – Your Source For Investing Education. trending periods. There are also two types of indicator constructions: those that fall in a bounded range and those that do not.

The ones that are bound within a range are called oscillators – these are the most common type of indicators. Oscillator indicators have a range, for example between zero and 100, and signal periods where the security is overbought (near 100) or oversold (near zero). Non-bounded indicators still form buy and sell signals along with displaying strength or weakness, but they vary in the way they do this. The two main ways that indicators are used to form buy and sell signals in technical analysis is through crossovers and divergence.

Crossovers are the most popular and are reflected when either the price moves through the moving average, or when two different moving averages cross over each other. The second way indicators are used is through divergence, which happens when the direction of the price trend and the direction of the indicator trend are moving in the opposite direction. This signals to indicator users that t

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Technical Analysis Plain and Simple: Charting the Markets in Your Language, Third Edition by Michael N. Kahn - CMT

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30. Case Study—The Perfect World

Now that you know what charting and technical analysis are all about and that they are not going to replace your current means of stock selection at this time, the pieces can be assembled together in a case study. This involves the major areas of recognizing trends, finding patterns, and using supporting studies to assess the stock market and pick a winning stock.

Here’s the process:

• Determine if conditions are favorable for equity assets using our current analytical techniques (earnings, inflation, etc....).

• If they are fair to good, then determine what sectors of the market would be best to focus upon.

• Now that the best sectors are found, which stocks should you buy?

• What technical tools should ...

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Trading for Living

• 1 . 1 Basics of Futures Trading
• 1 . 2 Understanding US Index Futures
• 1 . 3 Differences between futures and other investment instruments
• 2 . 1 Introduction to different US indexes
• 2 . 2 Analysis of ES (S&P 500 futures)
• 2 . 3 Role of indexes in trading
• 3 . 1 Deep Dive into The S&P 500 Index
• 3 . 2 Sectors of the S&P 500
• 3 . 3 Key companies within the S&P 500
• 4 . 1 Introduction to Fundamental Analysis
• 4 . 2 Using Fundamental Analysis in trading index futures
• 4 . 3 Case Studies in Fundamental Analysis
• 5 . 1 Understanding Technical Analysis
• 5 . 2 Technical Indicators relevant for Index Futures
• 5 . 3 Case Studies in Technical Analysis
• 6 . 1 Introduction to Medium Term Trading
• 6 . 2 Developing your own Medium Term Trading Strategy
• 6 . 3 Risk Management in Medium Term Trading
• 7 . 1 Understanding Long Term Investing
• 7 . 2 Developing your own Long Term Investing Strategy
• 7 . 3 Risk Management in Long Term Investing
• 8 . 1 Understanding Trading Psychology
• 8 . 2 Emotional Control and Decision-Making
• 8 . 3 Developing a Trading Mindset
• 9 . 1 Basics of Money Management
• 9 . 2 Position sizing and Leverage
• 9 . 3 Risk-Control Techniques
• 10 . 1 Introduction to Trading Systems
• 10 . 2 Understanding the Trading Platform
• 10 . 3 Executing a Trade
• 11 . 1 Understanding Trading Regulations
• 11 . 2 Tax implications for Traders
• 11 . 3 Complying with Local and Federal laws
• 12 . 1 Importance of a Trading Plan
• 12 . 2 Elements of a Trading Plan
• 12 . 3 Implementing and Revising Your Plan
• 13 . 1 Developing your own Live Trading Plan
• 13 . 2 Sharing and Review of Trading Plans
• 13 . 3 Course Wrap-up and Next Steps

Technical Analysis

Case studies in technical analysis.

Security analysis methodology.

Technical analysis is a powerful tool for traders, providing insights into market trends and potential future price movements. To fully understand its application, we will delve into real-life trading scenarios and examine how technical analysis was used to make trading decisions.

Case Study 1: Moving Averages

In this case, we look at a trader who used moving averages to identify a potential uptrend in the S&P 500 futures. The trader noticed that the 50-day moving average had crossed above the 200-day moving average, a bullish signal known as a "golden cross". Acting on this signal, the trader bought futures contracts and was able to profit from the subsequent uptrend.

Key Takeaway: Moving averages can help identify potential trends. A golden cross, where the short-term moving average crosses above the long-term moving average, is a bullish signal.

Case Study 2: Relative Strength Index (RSI)

Our second case involves a trader who used the Relative Strength Index (RSI) to identify overbought and oversold conditions in the market. The trader noticed that the RSI for the S&P 500 futures had dropped below 30, indicating that the market was potentially oversold. The trader decided to buy futures contracts, anticipating a price rebound. The market did rebound, and the trader was able to make a profit.

Key Takeaway: The RSI can help identify potential overbought or oversold conditions. An RSI below 30 typically indicates an oversold market, which could be a buying opportunity.

Case Study 3: Bollinger Bands

In our third case, a trader used Bollinger Bands to identify periods of high and low volatility in the market. The trader noticed that the S&P 500 futures price had moved to the upper Bollinger Band, indicating a period of high volatility. The trader decided to sell futures contracts, anticipating a price decrease. The market did decrease, and the trader was able to make a profit.

Key Takeaway: Bollinger Bands can help identify periods of high and low volatility. When the price touches the upper band, it could indicate a selling opportunity.

These case studies illustrate the practical application of technical analysis in trading. By understanding and interpreting different indicators, traders can make informed decisions and potentially increase their chances of success. However, it's important to remember that no indicator is foolproof, and they should be used in conjunction with other forms of analysis and risk management techniques.

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Technical Analysis Trading Strategy (Rules, Backtest And Example)

Technical analysis strategy is a popular way of analyzing and forecasting price movements of financial assets such as currencies, stocks, commodities, bonds, and cryptocurrencies. While this strategy is as old as modern financial markets, not many know what it is and how it works. What is a technical analysis strategy ?

Technical analysis strategy is the use of past and present price data to analyze a financial market and predict the likely future movement. It can be done by analyzing the price movement themselves or with the help of technical indicators, which are mathematical representations of the price data. This strategy may involve the use of trend-following tools like moving averages, and momentum-based tools like stochastic to identify entries and exits in the market.

In this post, we answer some questions about the technical analysis and we end the post with a backtest.

Table of contents:

What is a technical analysis trading strategy?

Technical analysis strategy is a method of analyzing and forecasting the price movement of an asset using past and current price and volume data. It involves the study of past prices and volume data, together with different technical indicators to identify trends and patterns that can be used to make trading decisions.

However, there is no precise definition, and trader A can define it differently than Trader B.

The strategy might be based on the concept that price patterns, trends, and technical indicators. The main idea is to provide valuable information into market psychology and help traders predict future price movements.

One of the theories of technical analysis is that the price of an asset tends to trend, and another is that the price has a mean-reversion tendency. Thus, technical analysis strategies can mainly be categorized into trend-following and mean-reversion strategies.

• Trend Following Trading Strategies and Systems Explained (Including Backtest and Statistics)
• Do Trend Following Trading Strategies Work?
• Mean Reversion Trading Strategies and Backtest | (Pros And Cons Of Mean Reverting Systems & Indicators)
• 3 Best Mean Reversion Trading Strategies (Backtested Buy And Sell Signals)

Trend following and mean reversion complements each other well, and hence can be used in a portfolio of trading strategies.

Trend-following strategies involve the identification of a trend in the price of an asset and then buying or selling to profit from the trend. For instance, a trader might employ the use of a moving average to identify an uptrend and then buy when the price is above the moving average or sell when the price is in a downtrend and below the moving average. The 200-day moving average strategy is an example of a trend following strategy .

Mean-reversion strategies involve identifying a particular level that an asset tends to pull towards and then buying or selling when the price wanders too far from that level. This is based on the concept that price tends to revert to its mean after an explosive move. A buy the dip strategy is an example of a mean reversion strategy .

It is important to note that technical analysis does not measure the intrinsic value of an asset, but instead, uses charts and other tools to identify patterns that can forecast future price movements. Fundamental analysis is mostly ignored, however, it can be paired with fundamental analysis, which focuses on the current economic outlook that may affect the future price of an asset.

What types of technical analysis strategies exist for trading?

There are several technical analysis strategies that you can employ. Below we list the most common types of strategies.

• Trend-following. This strategy involves the identification of the overall trend in a given market and then using this trend to make buying and selling decisions.
• Mean-reversion strategies. This involves identifying a particular level that an asset tends to pull towards and then buying or selling when the price wanders too far from that level. This is based on the concept that price tends to revert to its mean after an explosive move.
• Momentum strategy . This uses momentum indicators, such as MACD, RSI, stochastic, and co to measure the price momentum and trade along that direction.
• Breakout strategy . This involves trading when the price breaks out of support or resistance level.
• Chart patterns. This involves identifying specific patterns on the price chart and using them to predict future price movements. An example of a chart pattern is the heads and shoulders pattern .

How can I identify a profitable trading strategy using technical analysis?

There are a few steps you need to make in order to trade successfully. Here are a few important steps you can follow:

• Define your trading goals and risk tolerance . You can use this to decide what kind of plan is best for you. For instance, if you have a high-risk tolerance, you might feel at ease using a technique that has a higher chance of loss but the possibility for bigger rewards. However, most traders have a lower risk tolerance than they realize. When you get losses and drawdowns , you are much more exposed to trading biases in trading .
• Use technical indicators . Technical indicators can aid in the identification of prospective trade setups by providing information on the strength and direction of a trend, as well as potential reversal points. Moving averages, the relative strength index (RSI), and the stochastic oscillator are all popular technical indicators.
• Backtest your strategy. Once you’ve identified a suitable trading technique, you should put it to the test to check if it’s realistic. Backtesting the approach using historical data to evaluate how it works is a necessary step in finding a profitable strategy. We have written a guide in how to backtest a strategy .
• Monitor and optimize your strategy. As market conditions change, you may need to modify your technique to continue making profitable trades. It is critical to examine and monitor your plan frequently to verify that it is still effective. Trading is all about feedback loops and learn from the past. One of the best tools to learn trading is to use a trading journal .

What are the advantages of using technical analysis for trading?

Some potential advantages of using technical analysis for trading include:

• It is easy to code into a trading algorithm . Technical analysis is often based on price and volume data and mathematical formulae, which makes them easy to be converted into a trading algo. Coding into a trading platform is not as daunting as it may sound. We have been able to do it, and that means most others also can! It just requires a few days of learning to get started, and from then you learn gradually.
• It can be applied to any security . You can use it on any security that has historical price data, such as stocks, bonds, currencies, and commodities. We recommend trading many instruments.
• It can be used to make both short-term and long-term trades. Depending on the period of the chart being reviewed, technical analysis can be used to make both short-term and long-term trades.
• Long-Term Trading Strategy e x a m p l e
• Short-Term Trading Strategy example

How can I create a comprehensive trading strategy based on technical analysis?

Let’s look at how you can develop a trading strategy:

• Determine the market in which you want to trade . Think about your level of experience, risk tolerance, and the time you have available to commit to trading.
• Determine your time frame . Technical analysis may be performed on different time frames. Choose a time range that corresponds to your style of trading and risk tolerance. We believe the daily time frame is best for trading .
• Select the technical indicators you like : Choose from a variety of technical indicators, including moving averages, the relative strength index (RSI), and the Moving Average Convergence Divergence (MACD). Experiment with several indicators to determine which ones work best for your plan.
• Establish trade entry and exit points . Use technical analysis to discover probable trade entry and exit points. Look for chart patterns such as head and shoulders or triangles, or utilize indicators such as the RSI to identify overbought or oversold circumstances.
• Backtest your plan . Test your strategy using historical data to see how it might have fared in the past. This will allow you to spot any flaws and make necessary improvements before trading with real money. If a strategy has not worked in the past, it’s unlikely it will work in the future.
• Implement risk management . Risk management is an integral component of every trading strategy. To assist minimize risk, consider elements like position sizing and stop-loss orders.
• Examine and improve your strategy . Review and evaluate the effectiveness of your approach regularly. Be ready to make changes as needed in response to changing market conditions or your own evolving trading style.

Related reading: Technical Indicators Strategy

What indicators should I use to identify trading opportunities?

There are different trading indicators you can use to discover trading opportunities.

However, the type of indicator you use is determined by the approach you are employing. Moving averages, for example, can be useful if you are a trend trader.

Oscillators are widely employed by short-term traders to identify market extremes such as overbought and oversold conditions. Other indicators include On-balance volume, Williams’ Percent R, Alligator, Ichimoku cloud, etc. Williams %R is a very good indicator .

How should I backtest a trading strategy based on technical analysis?

• Determine how much data you need . You must choose how far back in time you want to test your plan. It could take days, weeks, months, or even years, depending on the strategy. An intraday strategy may require less than a year’s data to get a good sample size, while a position trading strategy may require more than 10 years of data. Perhaps more important, is to include at least one bear market.
• Source your price data . For the assets you intend to trade, you will need to gather historical pricing information. This is frequently available for free from several sources, including Google Finance and Yahoo Finance. But be aware of bad quotes. In trading, garbage in equals garbage out!
• Write the trading algorithm . Use the right programming language for the platform you are using to write the trading algo, specifying the entry and exit rules. Please read our guide about algorithmic trading strategies .
• Execute the backtest . To simulate past trades, use your pricing data and strategy execution. If you have to tweak and optimize your strategy, you will need to divide your data into in-sample and out-of-sample groups.
• Analyze the results . You can examine the backtest data after it is finished to determine how well your strategy performed. Evaluate the performance indicators, such as win rate, returns, maximum drawdown, and Sharpe ratio.

What are the most reliable entry and exit points for a trading strategy?

Your entry and exit points are determined by your trading strategy and there is no most reliable entry and exit. For example, if you are using the moving average crossover strategy, you buy when the fast moving average crosses above the slow moving average and exit when it crosses below.

When using a technical analysis strategy, it is important to clearly state your entry and exit conditions and make sure to adhere to them. For any strategy, the most reliable entry and exit points would depend on what your backtesting results show.

If you are a mean reversion trader, you might want to consider the QS exit sell signal o f w h e n t o s e l l .

What tools should I use to analyze a trading strategy based on technical analysis?

• Charting software. You may visualize price movements and other data using charting software, such as TradingView, TradeStation, and MetaTrader. We prefer Amibroker and TradeStation. Please have a look at our Amibroker review and Amibroker course .
• Backtesting software . This can be used to find out how the plan would have performed in the past.
• Your trading journal . This is where you maintain a record of your trades, including the reasons you entered and exited them, if you are using a manual strategy. For an automated strategy, the system takes a record of the trades and gives you the necessary data for your assessment.

How can I optimize a trading strategy based on technical analysis?

• Backtesting . This involves evaluating the performance of the strategy using historical data. This will enable you to spot any strategy flaws or shortcomings and make the appropriate corrections.
• Tweaking the parameters . You can experiment by changing the parameters of the components of the strategy to see if the strategy performs better.
• Trying other timeframes : Test the same technique on other time frames (such as daily, hourly, and 15-minute charts) to determine which one performs the best.
• Including risk management . Risk management should be taken into account. This may involve having stop-loss orders and adjusting position sizing.

Please also read our article that show you how to optimize a trading strategy . We recommend optimizing so you get a better grasp of what is driving the returns.

What types of datasets should I use for backtesting a trading strategy?

You can make use of historical price data to backtest your trading strategy. However, be aware that a strategy may perform well in backtesting and do poorly in live trading due to curve fitting . To avoid this, divide your data into in-sample and out-of-sample data if you need to optimize the parameters of the strategy.

Perhaps better, put your trading strategies on hold or in incubation for many months, perhaps a year, before you start live trading. You can use a demo account for this.

• Out of sample backtesting tutorial

How can I determine if a trading strategy based on technical analysis is profitable?

You may backtest a technical analysis-based trading strategy using historical data to examine how it would have performed and ascertain whether it is profitable.

To check how the strategy operates in actual market conditions, you can also forward-test with a demo account. Some of the performance metrics to assess include profit factor, risk-to-reward ratio, and win rate.

What technical analysis techniques should I use to develop a trading strategy?

Anything that shows an exploitable inefficiency in the market can be the basis of a trading strategy. It could be a specific price action pattern, a time of the day, technical indicators, or any other thing. If you find out that the price moves a particular way if a 2-period RSI reaches a certain level, then that becomes the technique for your strategy.

What are the most important considerations when backtesting a trading strategy?

• Data frequency . The frequency of the data (e.g., daily, hourly) can impact the results of your backtest.
• Starting and ending points . The starting and ending points of your backtest can impact the results. Be sure to choose a representative period to test your strategy. The longer the better, and also make sure you include different market sentiments, for example bull and bear markets.
• Slippage and commissions. Your backtesting result is unlikely to include those factors, so keep them in mind. We made a guide about trading commissions .
• Multiple testing . It can be helpful to test your strategy on multiple markets or periods to ensure that it is robust and has a consistent track record. But don’t expect a strategy to perform well on all assets. Forex is, for example, very different from stocks.

How can I apply technical analysis to evaluate a trading strategy?

You can use technical analysis to assess a trading strategy by looking at past price data to spot patterns and trends and utilizing indicators to gauge how strong these trends are. Your plan can also be back-tested to determine how well it might have worked in the past.

What is the best way to test the effectiveness of a trading strategy?

The easiest way to determine whether a trading strategy is effective is to backtest it using historical data and then forward-test it using real-time data to determine whether the results are reliable. Additionally, you can forward-test it with a demo account to see how it performs in live market circumstances.

What type of data should I use when testing a trading strategy?

Use high-quality data that applies to the trading technique you are testing when evaluating it. Depending on the sort of approach you are implementing, this may contain price data, volume data, and economic data.

Additionally, it’s critical to employ a significant amount of data to accurately assess the effectiveness of the strategy.

How can I determine the risk associated with a trading strategy?

To evaluate the risk-return tradeoff of the strategy, you may also use risk metrics like the Sharpe ratio, maximum drawdown, Jensen’s alpha, and so on.

We have a written about all the main metrics used in backtesting and trading performance:

• Win ratio in trading – what it is and why it is important (winning ratio)
• Trading strategy and system performance metrics (What is it and how to use it)
• What Is A Good Equity Curve? – Profit & Loss Curves Best Practices
• The Sharpe Ratio Explained (What is a good Sharpe Ratio? Examples)
• The profit factor explained (what is a good profit factor in trading? Examples of profit factors)
• What is K-Ratio?
• Treynor Ratio, how to calculate it: What is it and what is good?
• Jensen Ratio – what is it and how is it calculated? (Jensen’s Performance Index)
• Sortino Ratio – what is it and how do you use it?
• Ulcer Index — What Is It?

Technical analysis strategy backtest

Let’s end the article with a simple backtest of the most popular trading indicator- the Relative Strength Index (RSI) .

We make the following trading rules:

• When the 3-day RSI drops below 15, we go long.
• We sell when the close ends higher than yesterday’s high.

This simple strategy has returned the following equity curve for Nasdaq 100 (the ETF that tracks Nasdaq-100 is QQQ):

There are only 201 trades, but the average gain per trade is a solid 1.26%. The win rate is 72% and max drawdown is 19%. 100 000 invested in year 2000 is worth 1.1 million today, which equals 10.8% annual returns despite being invested only 12.8% of the time.

The full performance report looks like this:

Worth noting is the risk-adjusted return , which is the annual return divide by the time spent in the market.

Let’s end the article by looking at the monthly and annual returns:

Even though this is a long-only strategy, it has performed spectacularly during bear markets!

To identify a profitable trading strategy, traders should define their goals and risk tolerance, use technical indicators for analysis, backtest the strategy with historical data, and continually monitor and optimize the strategy based on changing market conditions.

To create a comprehensive trading strategy, traders should determine the market and timeframe, select relevant technical indicators, establish entry and exit points, backtest the strategy, implement risk management, and regularly examine and optimize the strategy.

Backtesting involves using historical data to simulate past trades and evaluate a strategy’s performance. Traders should determine the data frequency, source high-quality price data, write the trading algorithm, execute the backtest, and analyze the results.

I’ve got an Msc from Heriot-Watt University, Edinburgh (1996), in addition a to a business administration degree the Norwegian School of Management (BI – 1994). Did my mandatory military service in between.

After university, I worked two years as an auditor (1996-1998).

I co-founded Aksjeforum.com in 1998/99 - one of the first websites about trading and investing in Norway. It was later acquired by Digi.no in 2001.

I have written 4 books about trading (in Norwegian). One of them has sold 30,000 copies, a record for a financial book in Norway.

From 2001 until 2018 full-time independent prop trader (Series 7 in 2001) and investor.

2018-today: Investor, writer, analyst.

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Home » Case Study – Methods, Examples and Guide

Case Study – Methods, Examples and Guide

Table of Contents

A case study is a research method that involves an in-depth examination and analysis of a particular phenomenon or case, such as an individual, organization, community, event, or situation.

It is a qualitative research approach that aims to provide a detailed and comprehensive understanding of the case being studied. Case studies typically involve multiple sources of data, including interviews, observations, documents, and artifacts, which are analyzed using various techniques, such as content analysis, thematic analysis, and grounded theory. The findings of a case study are often used to develop theories, inform policy or practice, or generate new research questions.

Types of Case Study

Types and Methods of Case Study are as follows:

Single-Case Study

A single-case study is an in-depth analysis of a single case. This type of case study is useful when the researcher wants to understand a specific phenomenon in detail.

For Example , A researcher might conduct a single-case study on a particular individual to understand their experiences with a particular health condition or a specific organization to explore their management practices. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a single-case study are often used to generate new research questions, develop theories, or inform policy or practice.

Multiple-Case Study

A multiple-case study involves the analysis of several cases that are similar in nature. This type of case study is useful when the researcher wants to identify similarities and differences between the cases.

For Example, a researcher might conduct a multiple-case study on several companies to explore the factors that contribute to their success or failure. The researcher collects data from each case, compares and contrasts the findings, and uses various techniques to analyze the data, such as comparative analysis or pattern-matching. The findings of a multiple-case study can be used to develop theories, inform policy or practice, or generate new research questions.

Exploratory Case Study

An exploratory case study is used to explore a new or understudied phenomenon. This type of case study is useful when the researcher wants to generate hypotheses or theories about the phenomenon.

For Example, a researcher might conduct an exploratory case study on a new technology to understand its potential impact on society. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as grounded theory or content analysis. The findings of an exploratory case study can be used to generate new research questions, develop theories, or inform policy or practice.

Descriptive Case Study

A descriptive case study is used to describe a particular phenomenon in detail. This type of case study is useful when the researcher wants to provide a comprehensive account of the phenomenon.

For Example, a researcher might conduct a descriptive case study on a particular community to understand its social and economic characteristics. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a descriptive case study can be used to inform policy or practice or generate new research questions.

Instrumental Case Study

An instrumental case study is used to understand a particular phenomenon that is instrumental in achieving a particular goal. This type of case study is useful when the researcher wants to understand the role of the phenomenon in achieving the goal.

For Example, a researcher might conduct an instrumental case study on a particular policy to understand its impact on achieving a particular goal, such as reducing poverty. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of an instrumental case study can be used to inform policy or practice or generate new research questions.

Case Study Data Collection Methods

Here are some common data collection methods for case studies:

Interviews involve asking questions to individuals who have knowledge or experience relevant to the case study. Interviews can be structured (where the same questions are asked to all participants) or unstructured (where the interviewer follows up on the responses with further questions). Interviews can be conducted in person, over the phone, or through video conferencing.

Observations

Observations involve watching and recording the behavior and activities of individuals or groups relevant to the case study. Observations can be participant (where the researcher actively participates in the activities) or non-participant (where the researcher observes from a distance). Observations can be recorded using notes, audio or video recordings, or photographs.

Documents can be used as a source of information for case studies. Documents can include reports, memos, emails, letters, and other written materials related to the case study. Documents can be collected from the case study participants or from public sources.

Surveys involve asking a set of questions to a sample of individuals relevant to the case study. Surveys can be administered in person, over the phone, through mail or email, or online. Surveys can be used to gather information on attitudes, opinions, or behaviors related to the case study.

Artifacts are physical objects relevant to the case study. Artifacts can include tools, equipment, products, or other objects that provide insights into the case study phenomenon.

How to conduct Case Study Research

Conducting a case study research involves several steps that need to be followed to ensure the quality and rigor of the study. Here are the steps to conduct case study research:

• Define the research questions: The first step in conducting a case study research is to define the research questions. The research questions should be specific, measurable, and relevant to the case study phenomenon under investigation.
• Select the case: The next step is to select the case or cases to be studied. The case should be relevant to the research questions and should provide rich and diverse data that can be used to answer the research questions.
• Collect data: Data can be collected using various methods, such as interviews, observations, documents, surveys, and artifacts. The data collection method should be selected based on the research questions and the nature of the case study phenomenon.
• Analyze the data: The data collected from the case study should be analyzed using various techniques, such as content analysis, thematic analysis, or grounded theory. The analysis should be guided by the research questions and should aim to provide insights and conclusions relevant to the research questions.
• Draw conclusions: The conclusions drawn from the case study should be based on the data analysis and should be relevant to the research questions. The conclusions should be supported by evidence and should be clearly stated.
• Validate the findings: The findings of the case study should be validated by reviewing the data and the analysis with participants or other experts in the field. This helps to ensure the validity and reliability of the findings.
• Write the report: The final step is to write the report of the case study research. The report should provide a clear description of the case study phenomenon, the research questions, the data collection methods, the data analysis, the findings, and the conclusions. The report should be written in a clear and concise manner and should follow the guidelines for academic writing.

Examples of Case Study

Here are some examples of case study research:

• The Hawthorne Studies : Conducted between 1924 and 1932, the Hawthorne Studies were a series of case studies conducted by Elton Mayo and his colleagues to examine the impact of work environment on employee productivity. The studies were conducted at the Hawthorne Works plant of the Western Electric Company in Chicago and included interviews, observations, and experiments.
• The Stanford Prison Experiment: Conducted in 1971, the Stanford Prison Experiment was a case study conducted by Philip Zimbardo to examine the psychological effects of power and authority. The study involved simulating a prison environment and assigning participants to the role of guards or prisoners. The study was controversial due to the ethical issues it raised.
• The Challenger Disaster: The Challenger Disaster was a case study conducted to examine the causes of the Space Shuttle Challenger explosion in 1986. The study included interviews, observations, and analysis of data to identify the technical, organizational, and cultural factors that contributed to the disaster.
• The Enron Scandal: The Enron Scandal was a case study conducted to examine the causes of the Enron Corporation’s bankruptcy in 2001. The study included interviews, analysis of financial data, and review of documents to identify the accounting practices, corporate culture, and ethical issues that led to the company’s downfall.
• The Fukushima Nuclear Disaster : The Fukushima Nuclear Disaster was a case study conducted to examine the causes of the nuclear accident that occurred at the Fukushima Daiichi Nuclear Power Plant in Japan in 2011. The study included interviews, analysis of data, and review of documents to identify the technical, organizational, and cultural factors that contributed to the disaster.

Application of Case Study

Case studies have a wide range of applications across various fields and industries. Here are some examples:

Business and Management

Case studies are widely used in business and management to examine real-life situations and develop problem-solving skills. Case studies can help students and professionals to develop a deep understanding of business concepts, theories, and best practices.

Case studies are used in healthcare to examine patient care, treatment options, and outcomes. Case studies can help healthcare professionals to develop critical thinking skills, diagnose complex medical conditions, and develop effective treatment plans.

Case studies are used in education to examine teaching and learning practices. Case studies can help educators to develop effective teaching strategies, evaluate student progress, and identify areas for improvement.

Social Sciences

Case studies are widely used in social sciences to examine human behavior, social phenomena, and cultural practices. Case studies can help researchers to develop theories, test hypotheses, and gain insights into complex social issues.

Law and Ethics

Case studies are used in law and ethics to examine legal and ethical dilemmas. Case studies can help lawyers, policymakers, and ethical professionals to develop critical thinking skills, analyze complex cases, and make informed decisions.

Purpose of Case Study

The purpose of a case study is to provide a detailed analysis of a specific phenomenon, issue, or problem in its real-life context. A case study is a qualitative research method that involves the in-depth exploration and analysis of a particular case, which can be an individual, group, organization, event, or community.

The primary purpose of a case study is to generate a comprehensive and nuanced understanding of the case, including its history, context, and dynamics. Case studies can help researchers to identify and examine the underlying factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and detailed understanding of the case, which can inform future research, practice, or policy.

Case studies can also serve other purposes, including:

• Illustrating a theory or concept: Case studies can be used to illustrate and explain theoretical concepts and frameworks, providing concrete examples of how they can be applied in real-life situations.
• Developing hypotheses: Case studies can help to generate hypotheses about the causal relationships between different factors and outcomes, which can be tested through further research.
• Providing insight into complex issues: Case studies can provide insights into complex and multifaceted issues, which may be difficult to understand through other research methods.
• Informing practice or policy: Case studies can be used to inform practice or policy by identifying best practices, lessons learned, or areas for improvement.

Advantages of Case Study Research

There are several advantages of case study research, including:

• In-depth exploration: Case study research allows for a detailed exploration and analysis of a specific phenomenon, issue, or problem in its real-life context. This can provide a comprehensive understanding of the case and its dynamics, which may not be possible through other research methods.
• Rich data: Case study research can generate rich and detailed data, including qualitative data such as interviews, observations, and documents. This can provide a nuanced understanding of the case and its complexity.
• Holistic perspective: Case study research allows for a holistic perspective of the case, taking into account the various factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and comprehensive understanding of the case.
• Theory development: Case study research can help to develop and refine theories and concepts by providing empirical evidence and concrete examples of how they can be applied in real-life situations.
• Practical application: Case study research can inform practice or policy by identifying best practices, lessons learned, or areas for improvement.
• Contextualization: Case study research takes into account the specific context in which the case is situated, which can help to understand how the case is influenced by the social, cultural, and historical factors of its environment.

Limitations of Case Study Research

There are several limitations of case study research, including:

• Limited generalizability : Case studies are typically focused on a single case or a small number of cases, which limits the generalizability of the findings. The unique characteristics of the case may not be applicable to other contexts or populations, which may limit the external validity of the research.
• Biased sampling: Case studies may rely on purposive or convenience sampling, which can introduce bias into the sample selection process. This may limit the representativeness of the sample and the generalizability of the findings.
• Subjectivity: Case studies rely on the interpretation of the researcher, which can introduce subjectivity into the analysis. The researcher’s own biases, assumptions, and perspectives may influence the findings, which may limit the objectivity of the research.
• Limited control: Case studies are typically conducted in naturalistic settings, which limits the control that the researcher has over the environment and the variables being studied. This may limit the ability to establish causal relationships between variables.
• Time-consuming: Case studies can be time-consuming to conduct, as they typically involve a detailed exploration and analysis of a specific case. This may limit the feasibility of conducting multiple case studies or conducting case studies in a timely manner.
• Resource-intensive: Case studies may require significant resources, including time, funding, and expertise. This may limit the ability of researchers to conduct case studies in resource-constrained settings.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Definition and Introduction

Case analysis is a problem-based teaching and learning method that involves critically analyzing complex scenarios within an organizational setting for the purpose of placing the student in a “real world” situation and applying reflection and critical thinking skills to contemplate appropriate solutions, decisions, or recommended courses of action. It is considered a more effective teaching technique than in-class role playing or simulation activities. The analytical process is often guided by questions provided by the instructor that ask students to contemplate relationships between the facts and critical incidents described in the case.

Cases generally include both descriptive and statistical elements and rely on students applying abductive reasoning to develop and argue for preferred or best outcomes [i.e., case scenarios rarely have a single correct or perfect answer based on the evidence provided]. Rather than emphasizing theories or concepts, case analysis assignments emphasize building a bridge of relevancy between abstract thinking and practical application and, by so doing, teaches the value of both within a specific area of professional practice.

Given this, the purpose of a case analysis paper is to present a structured and logically organized format for analyzing the case situation. It can be assigned to students individually or as a small group assignment and it may include an in-class presentation component. Case analysis is predominately taught in economics and business-related courses, but it is also a method of teaching and learning found in other applied social sciences disciplines, such as, social work, public relations, education, journalism, and public administration.

Ellet, William. The Case Study Handbook: A Student's Guide . Revised Edition. Boston, MA: Harvard Business School Publishing, 2018; Christoph Rasche and Achim Seisreiner. Guidelines for Business Case Analysis . University of Potsdam; Writing a Case Analysis . Writing Center, Baruch College; Volpe, Guglielmo. "Case Teaching in Economics: History, Practice and Evidence." Cogent Economics and Finance 3 (December 2015). doi:https://doi.org/10.1080/23322039.2015.1120977.

How to Approach Writing a Case Analysis Paper

The organization and structure of a case analysis paper can vary depending on the organizational setting, the situation, and how your professor wants you to approach the assignment. Nevertheless, preparing to write a case analysis paper involves several important steps. As Hawes notes, a case analysis assignment “...is useful in developing the ability to get to the heart of a problem, analyze it thoroughly, and to indicate the appropriate solution as well as how it should be implemented” [p.48]. This statement encapsulates how you should approach preparing to write a case analysis paper.

Before you begin to write your paper, consider the following analytical procedures:

• Review the case to get an overview of the situation . A case can be only a few pages in length, however, it is most often very lengthy and contains a significant amount of detailed background information and statistics, with multilayered descriptions of the scenario, the roles and behaviors of various stakeholder groups, and situational events. Therefore, a quick reading of the case will help you gain an overall sense of the situation and illuminate the types of issues and problems that you will need to address in your paper. If your professor has provided questions intended to help frame your analysis, use them to guide your initial reading of the case.
• Read the case thoroughly . After gaining a general overview of the case, carefully read the content again with the purpose of understanding key circumstances, events, and behaviors among stakeholder groups. Look for information or data that appears contradictory, extraneous, or misleading. At this point, you should be taking notes as you read because this will help you develop a general outline of your paper. The aim is to obtain a complete understanding of the situation so that you can begin contemplating tentative answers to any questions your professor has provided or, if they have not provided, developing answers to your own questions about the case scenario and its connection to the course readings,lectures, and class discussions.
• Determine key stakeholder groups, issues, and events and the relationships they all have to each other . As you analyze the content, pay particular attention to identifying individuals, groups, or organizations described in the case and identify evidence of any problems or issues of concern that impact the situation in a negative way. Other things to look for include identifying any assumptions being made by or about each stakeholder, potential biased explanations or actions, explicit demands or ultimatums , and the underlying concerns that motivate these behaviors among stakeholders. The goal at this stage is to develop a comprehensive understanding of the situational and behavioral dynamics of the case and the explicit and implicit consequences of each of these actions.
• Identify the core problems . The next step in most case analysis assignments is to discern what the core [i.e., most damaging, detrimental, injurious] problems are within the organizational setting and to determine their implications. The purpose at this stage of preparing to write your analysis paper is to distinguish between the symptoms of core problems and the core problems themselves and to decide which of these must be addressed immediately and which problems do not appear critical but may escalate over time. Identify evidence from the case to support your decisions by determining what information or data is essential to addressing the core problems and what information is not relevant or is misleading.
• Explore alternative solutions . As noted, case analysis scenarios rarely have only one correct answer. Therefore, it is important to keep in mind that the process of analyzing the case and diagnosing core problems, while based on evidence, is a subjective process open to various avenues of interpretation. This means that you must consider alternative solutions or courses of action by critically examining strengths and weaknesses, risk factors, and the differences between short and long-term solutions. For each possible solution or course of action, consider the consequences they may have related to their implementation and how these recommendations might lead to new problems. Also, consider thinking about your recommended solutions or courses of action in relation to issues of fairness, equity, and inclusion.
• Decide on a final set of recommendations . The last stage in preparing to write a case analysis paper is to assert an opinion or viewpoint about the recommendations needed to help resolve the core problems as you see them and to make a persuasive argument for supporting this point of view. Prepare a clear rationale for your recommendations based on examining each element of your analysis. Anticipate possible obstacles that could derail their implementation. Consider any counter-arguments that could be made concerning the validity of your recommended actions. Finally, describe a set of criteria and measurable indicators that could be applied to evaluating the effectiveness of your implementation plan.

Use these steps as the framework for writing your paper. Remember that the more detailed you are in taking notes as you critically examine each element of the case, the more information you will have to draw from when you begin to write. This will save you time.

NOTE : If the process of preparing to write a case analysis paper is assigned as a student group project, consider having each member of the group analyze a specific element of the case, including drafting answers to the corresponding questions used by your professor to frame the analysis. This will help make the analytical process more efficient and ensure that the distribution of work is equitable. This can also facilitate who is responsible for drafting each part of the final case analysis paper and, if applicable, the in-class presentation.

Framework for Case Analysis . College of Management. University of Massachusetts; Hawes, Jon M. "Teaching is Not Telling: The Case Method as a Form of Interactive Learning." Journal for Advancement of Marketing Education 5 (Winter 2004): 47-54; Rasche, Christoph and Achim Seisreiner. Guidelines for Business Case Analysis . University of Potsdam; Writing a Case Study Analysis . University of Arizona Global Campus Writing Center; Van Ness, Raymond K. A Guide to Case Analysis . School of Business. State University of New York, Albany; Writing a Case Analysis . Business School, University of New South Wales.

Structure and Writing Style

A case analysis paper should be detailed, concise, persuasive, clearly written, and professional in tone and in the use of language . As with other forms of college-level academic writing, declarative statements that convey information, provide a fact, or offer an explanation or any recommended courses of action should be based on evidence. If allowed by your professor, any external sources used to support your analysis, such as course readings, should be properly cited under a list of references. The organization and structure of case analysis papers can vary depending on your professor’s preferred format, but its structure generally follows the steps used for analyzing the case.

Introduction

The introduction should provide a succinct but thorough descriptive overview of the main facts, issues, and core problems of the case . The introduction should also include a brief summary of the most relevant details about the situation and organizational setting. This includes defining the theoretical framework or conceptual model on which any questions were used to frame your analysis.

Following the rules of most college-level research papers, the introduction should then inform the reader how the paper will be organized. This includes describing the major sections of the paper and the order in which they will be presented. Unless you are told to do so by your professor, you do not need to preview your final recommendations in the introduction. U nlike most college-level research papers , the introduction does not include a statement about the significance of your findings because a case analysis assignment does not involve contributing new knowledge about a research problem.

Background Analysis

Background analysis can vary depending on any guiding questions provided by your professor and the underlying concept or theory that the case is based upon. In general, however, this section of your paper should focus on:

• Providing an overarching analysis of problems identified from the case scenario, including identifying events that stakeholders find challenging or troublesome,
• Identifying assumptions made by each stakeholder and any apparent biases they may exhibit,
• Describing any demands or claims made by or forced upon key stakeholders, and
• Highlighting any issues of concern or complaints expressed by stakeholders in response to those demands or claims.

These aspects of the case are often in the form of behavioral responses expressed by individuals or groups within the organizational setting. However, note that problems in a case situation can also be reflected in data [or the lack thereof] and in the decision-making, operational, cultural, or institutional structure of the organization. Additionally, demands or claims can be either internal and external to the organization [e.g., a case analysis involving a president considering arms sales to Saudi Arabia could include managing internal demands from White House advisors as well as demands from members of Congress].

Throughout this section, present all relevant evidence from the case that supports your analysis. Do not simply claim there is a problem, an assumption, a demand, or a concern; tell the reader what part of the case informed how you identified these background elements.

Identification of Problems

In most case analysis assignments, there are problems, and then there are problems . Each problem can reflect a multitude of underlying symptoms that are detrimental to the interests of the organization. The purpose of identifying problems is to teach students how to differentiate between problems that vary in severity, impact, and relative importance. Given this, problems can be described in three general forms: those that must be addressed immediately, those that should be addressed but the impact is not severe, and those that do not require immediate attention and can be set aside for the time being.

All of the problems you identify from the case should be identified in this section of your paper, with a description based on evidence explaining the problem variances. If the assignment asks you to conduct research to further support your assessment of the problems, include this in your explanation. Remember to cite those sources in a list of references. Use specific evidence from the case and apply appropriate concepts, theories, and models discussed in class or in relevant course readings to highlight and explain the key problems [or problem] that you believe must be solved immediately and describe the underlying symptoms and why they are so critical.

Alternative Solutions

This section is where you provide specific, realistic, and evidence-based solutions to the problems you have identified and make recommendations about how to alleviate the underlying symptomatic conditions impacting the organizational setting. For each solution, you must explain why it was chosen and provide clear evidence to support your reasoning. This can include, for example, course readings and class discussions as well as research resources, such as, books, journal articles, research reports, or government documents. In some cases, your professor may encourage you to include personal, anecdotal experiences as evidence to support why you chose a particular solution or set of solutions. Using anecdotal evidence helps promote reflective thinking about the process of determining what qualifies as a core problem and relevant solution .

Throughout this part of the paper, keep in mind the entire array of problems that must be addressed and describe in detail the solutions that might be implemented to resolve these problems.

Recommended Courses of Action

In some case analysis assignments, your professor may ask you to combine the alternative solutions section with your recommended courses of action. However, it is important to know the difference between the two. A solution refers to the answer to a problem. A course of action refers to a procedure or deliberate sequence of activities adopted to proactively confront a situation, often in the context of accomplishing a goal. In this context, proposed courses of action are based on your analysis of alternative solutions. Your description and justification for pursuing each course of action should represent the overall plan for implementing your recommendations.

For each course of action, you need to explain the rationale for your recommendation in a way that confronts challenges, explains risks, and anticipates any counter-arguments from stakeholders. Do this by considering the strengths and weaknesses of each course of action framed in relation to how the action is expected to resolve the core problems presented, the possible ways the action may affect remaining problems, and how the recommended action will be perceived by each stakeholder.

In addition, you should describe the criteria needed to measure how well the implementation of these actions is working and explain which individuals or groups are responsible for ensuring your recommendations are successful. In addition, always consider the law of unintended consequences. Outline difficulties that may arise in implementing each course of action and describe how implementing the proposed courses of action [either individually or collectively] may lead to new problems [both large and small].

Throughout this section, you must consider the costs and benefits of recommending your courses of action in relation to uncertainties or missing information and the negative consequences of success.

The conclusion should be brief and introspective. Unlike a research paper, the conclusion in a case analysis paper does not include a summary of key findings and their significance, a statement about how the study contributed to existing knowledge, or indicate opportunities for future research.

Begin by synthesizing the core problems presented in the case and the relevance of your recommended solutions. This can include an explanation of what you have learned about the case in the context of your answers to the questions provided by your professor. The conclusion is also where you link what you learned from analyzing the case with the course readings or class discussions. This can further demonstrate your understanding of the relationships between the practical case situation and the theoretical and abstract content of assigned readings and other course content.

Problems to Avoid

The literature on case analysis assignments often includes examples of difficulties students have with applying methods of critical analysis and effectively reporting the results of their assessment of the situation. A common reason cited by scholars is that the application of this type of teaching and learning method is limited to applied fields of social and behavioral sciences and, as a result, writing a case analysis paper can be unfamiliar to most students entering college.

After you have drafted your paper, proofread the narrative flow and revise any of these common errors:

• Unnecessary detail in the background section . The background section should highlight the essential elements of the case based on your analysis. Focus on summarizing the facts and highlighting the key factors that become relevant in the other sections of the paper by eliminating any unnecessary information.
• Analysis relies too much on opinion . Your analysis is interpretive, but the narrative must be connected clearly to evidence from the case and any models and theories discussed in class or in course readings. Any positions or arguments you make should be supported by evidence.
• Analysis does not focus on the most important elements of the case . Your paper should provide a thorough overview of the case. However, the analysis should focus on providing evidence about what you identify are the key events, stakeholders, issues, and problems. Emphasize what you identify as the most critical aspects of the case to be developed throughout your analysis. Be thorough but succinct.
• Writing is too descriptive . A paper with too much descriptive information detracts from your analysis of the complexities of the case situation. Questions about what happened, where, when, and by whom should only be included as essential information leading to your examination of questions related to why, how, and for what purpose.
• Inadequate definition of a core problem and associated symptoms . A common error found in case analysis papers is recommending a solution or course of action without adequately defining or demonstrating that you understand the problem. Make sure you have clearly described the problem and its impact and scope within the organizational setting. Ensure that you have adequately described the root causes w hen describing the symptoms of the problem.
• Recommendations lack specificity . Identify any use of vague statements and indeterminate terminology, such as, “A particular experience” or “a large increase to the budget.” These statements cannot be measured and, as a result, there is no way to evaluate their successful implementation. Provide specific data and use direct language in describing recommended actions.
• Unrealistic, exaggerated, or unattainable recommendations . Review your recommendations to ensure that they are based on the situational facts of the case. Your recommended solutions and courses of action must be based on realistic assumptions and fit within the constraints of the situation. Also note that the case scenario has already happened, therefore, any speculation or arguments about what could have occurred if the circumstances were different should be revised or eliminated.

Bee, Lian Song et al. "Business Students' Perspectives on Case Method Coaching for Problem-Based Learning: Impacts on Student Engagement and Learning Performance in Higher Education." Education & Training 64 (2022): 416-432; The Case Analysis . Fred Meijer Center for Writing and Michigan Authors. Grand Valley State University; Georgallis, Panikos and Kayleigh Bruijn. "Sustainability Teaching using Case-Based Debates." Journal of International Education in Business 15 (2022): 147-163; Hawes, Jon M. "Teaching is Not Telling: The Case Method as a Form of Interactive Learning." Journal for Advancement of Marketing Education 5 (Winter 2004): 47-54; Georgallis, Panikos, and Kayleigh Bruijn. "Sustainability Teaching Using Case-based Debates." Journal of International Education in Business 15 (2022): 147-163; .Dean,  Kathy Lund and Charles J. Fornaciari. "How to Create and Use Experiential Case-Based Exercises in a Management Classroom." Journal of Management Education 26 (October 2002): 586-603; Klebba, Joanne M. and Janet G. Hamilton. "Structured Case Analysis: Developing Critical Thinking Skills in a Marketing Case Course." Journal of Marketing Education 29 (August 2007): 132-137, 139; Klein, Norman. "The Case Discussion Method Revisited: Some Questions about Student Skills." Exchange: The Organizational Behavior Teaching Journal 6 (November 1981): 30-32; Mukherjee, Arup. "Effective Use of In-Class Mini Case Analysis for Discovery Learning in an Undergraduate MIS Course." The Journal of Computer Information Systems 40 (Spring 2000): 15-23; Pessoa, Silviaet al. "Scaffolding the Case Analysis in an Organizational Behavior Course: Making Analytical Language Explicit." Journal of Management Education 46 (2022): 226-251: Ramsey, V. J. and L. D. Dodge. "Case Analysis: A Structured Approach." Exchange: The Organizational Behavior Teaching Journal 6 (November 1981): 27-29; Schweitzer, Karen. "How to Write and Format a Business Case Study." ThoughtCo. https://www.thoughtco.com/how-to-write-and-format-a-business-case-study-466324 (accessed December 5, 2022); Reddy, C. D. "Teaching Research Methodology: Everything's a Case." Electronic Journal of Business Research Methods 18 (December 2020): 178-188; Volpe, Guglielmo. "Case Teaching in Economics: History, Practice and Evidence." Cogent Economics and Finance 3 (December 2015). doi:https://doi.org/10.1080/23322039.2015.1120977.

Writing Tip

Ca se Study and Case Analysis Are Not the Same!

Confusion often exists between what it means to write a paper that uses a case study research design and writing a paper that analyzes a case; they are two different types of approaches to learning in the social and behavioral sciences. Professors as well as educational researchers contribute to this confusion because they often use the term "case study" when describing the subject of analysis for a case analysis paper. But you are not studying a case for the purpose of generating a comprehensive, multi-faceted understanding of a research problem. R ather, you are critically analyzing a specific scenario to argue logically for recommended solutions and courses of action that lead to optimal outcomes applicable to professional practice.

To avoid any confusion, here are twelve characteristics that delineate the differences between writing a paper using the case study research method and writing a case analysis paper:

• Case study is a method of in-depth research and rigorous inquiry ; case analysis is a reliable method of teaching and learning . A case study is a modality of research that investigates a phenomenon for the purpose of creating new knowledge, solving a problem, or testing a hypothesis using empirical evidence derived from the case being studied. Often, the results are used to generalize about a larger population or within a wider context. The writing adheres to the traditional standards of a scholarly research study. A case analysis is a pedagogical tool used to teach students how to reflect and think critically about a practical, real-life problem in an organizational setting.
• The researcher is responsible for identifying the case to study; a case analysis is assigned by your professor . As the researcher, you choose the case study to investigate in support of obtaining new knowledge and understanding about the research problem. The case in a case analysis assignment is almost always provided, and sometimes written, by your professor and either given to every student in class to analyze individually or to a small group of students, or students select a case to analyze from a predetermined list.
• A case study is indeterminate and boundless; a case analysis is predetermined and confined . A case study can be almost anything [see item 9 below] as long as it relates directly to examining the research problem. This relationship is the only limit to what a researcher can choose as the subject of their case study. The content of a case analysis is determined by your professor and its parameters are well-defined and limited to elucidating insights of practical value applied to practice.
• Case study is fact-based and describes actual events or situations; case analysis can be entirely fictional or adapted from an actual situation . The entire content of a case study must be grounded in reality to be a valid subject of investigation in an empirical research study. A case analysis only needs to set the stage for critically examining a situation in practice and, therefore, can be entirely fictional or adapted, all or in-part, from an actual situation.
• Research using a case study method must adhere to principles of intellectual honesty and academic integrity; a case analysis scenario can include misleading or false information . A case study paper must report research objectively and factually to ensure that any findings are understood to be logically correct and trustworthy. A case analysis scenario may include misleading or false information intended to deliberately distract from the central issues of the case. The purpose is to teach students how to sort through conflicting or useless information in order to come up with the preferred solution. Any use of misleading or false information in academic research is considered unethical.
• Case study is linked to a research problem; case analysis is linked to a practical situation or scenario . In the social sciences, the subject of an investigation is most often framed as a problem that must be researched in order to generate new knowledge leading to a solution. Case analysis narratives are grounded in real life scenarios for the purpose of examining the realities of decision-making behavior and processes within organizational settings. A case analysis assignments include a problem or set of problems to be analyzed. However, the goal is centered around the act of identifying and evaluating courses of action leading to best possible outcomes.
• The purpose of a case study is to create new knowledge through research; the purpose of a case analysis is to teach new understanding . Case studies are a choice of methodological design intended to create new knowledge about resolving a research problem. A case analysis is a mode of teaching and learning intended to create new understanding and an awareness of uncertainty applied to practice through acts of critical thinking and reflection.
• A case study seeks to identify the best possible solution to a research problem; case analysis can have an indeterminate set of solutions or outcomes . Your role in studying a case is to discover the most logical, evidence-based ways to address a research problem. A case analysis assignment rarely has a single correct answer because one of the goals is to force students to confront the real life dynamics of uncertainly, ambiguity, and missing or conflicting information within professional practice. Under these conditions, a perfect outcome or solution almost never exists.
• Case study is unbounded and relies on gathering external information; case analysis is a self-contained subject of analysis . The scope of a case study chosen as a method of research is bounded. However, the researcher is free to gather whatever information and data is necessary to investigate its relevance to understanding the research problem. For a case analysis assignment, your professor will often ask you to examine solutions or recommended courses of action based solely on facts and information from the case.
• Case study can be a person, place, object, issue, event, condition, or phenomenon; a case analysis is a carefully constructed synopsis of events, situations, and behaviors . The research problem dictates the type of case being studied and, therefore, the design can encompass almost anything tangible as long as it fulfills the objective of generating new knowledge and understanding. A case analysis is in the form of a narrative containing descriptions of facts, situations, processes, rules, and behaviors within a particular setting and under a specific set of circumstances.
• Case study can represent an open-ended subject of inquiry; a case analysis is a narrative about something that has happened in the past . A case study is not restricted by time and can encompass an event or issue with no temporal limit or end. For example, the current war in Ukraine can be used as a case study of how medical personnel help civilians during a large military conflict, even though circumstances around this event are still evolving. A case analysis can be used to elicit critical thinking about current or future situations in practice, but the case itself is a narrative about something finite and that has taken place in the past.
• Multiple case studies can be used in a research study; case analysis involves examining a single scenario . Case study research can use two or more cases to examine a problem, often for the purpose of conducting a comparative investigation intended to discover hidden relationships, document emerging trends, or determine variations among different examples. A case analysis assignment typically describes a stand-alone, self-contained situation and any comparisons among cases are conducted during in-class discussions and/or student presentations.

The Case Analysis . Fred Meijer Center for Writing and Michigan Authors. Grand Valley State University; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Ramsey, V. J. and L. D. Dodge. "Case Analysis: A Structured Approach." Exchange: The Organizational Behavior Teaching Journal 6 (November 1981): 27-29; Yin, Robert K. Case Study Research and Applications: Design and Methods . 6th edition. Thousand Oaks, CA: Sage, 2017; Crowe, Sarah et al. “The Case Study Approach.” BMC Medical Research Methodology 11 (2011):  doi: 10.1186/1471-2288-11-100; Yin, Robert K. Case Study Research: Design and Methods . 4th edition. Thousand Oaks, CA: Sage Publishing; 1994.

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Top 20 Analytics Case Studies in 2024

Although the potential of Big Data and business intelligence are recognized by organizations, Gartner analyst Nick Heudecker says that the failure rate of analytics projects is close to 85%. Uncovering the power of analytics improves business operations, reduces costs, enhances decision-making , and enables the launching of more personalized products.

In this article, our research covers:

How to measure analytics success?

What are some analytics case studies.

According to  Gartner CDO Survey,  the top 3 critical success factors of analytics projects are:

• Creation of a data-driven culture within the organization,
• Data integration and data skills training across the organization,
• And implementation of a data management and analytics strategy.

The success of the process of analytics depends on asking the right question. It requires an understanding of the appropriate data required for each goal to be achieved. We’ve listed 20 successful analytics applications/case studies from different industries.

During our research, we examined that partnering with an analytics consultant helps organizations boost their success if organizations’ tech team lacks certain data skills.

*Vendors have not shared the client name

For more on analytics

If your organization is willing to implement an analytics solution but doesn’t know where to start, here are some of the articles we’ve written before that can help you learn more:

• AI in analytics: How AI is shaping analytics
• Edge Analytics in 2022: What it is, Why it matters & Use Cases
• Application Analytics: Tracking KPIs that lead to success

Finally, if you believe that your business would benefit from adopting an analytics solution, we have data-driven lists of vendors on our analytics hub and analytics platforms

We will help you choose the best solution tailored to your needs:

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month. Cem's work has been cited by leading global publications including Business Insider , Forbes, Washington Post , global firms like Deloitte , HPE, NGOs like World Economic Forum and supranational organizations like European Commission . You can see more reputable companies and media that referenced AIMultiple. Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization. He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider . Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

To stay up-to-date on B2B tech & accelerate your enterprise:

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Using Technical Analysis in Forex Trading: Examples and Case Studies

• Post author By Forex Academy
• Post date 9 September, 2023
• No Comments on Using Technical Analysis in Forex Trading: Examples and Case Studies

Using Technical Analysis in Forex Trading: Examples and Case StudiesTechnical analysis is a popular method used by forex traders to analyze and predict market trends. It involves studying historical price movements, patterns, and indicators to make informed trading decisions. By analyzing charts and patterns, traders can identify potential buying and selling opportunities.In this article, we…

Trend Analysis:

Support and resistance levels:, chart patterns:, indicators:.

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• Open access
• Published: 24 February 2018

Examination of the profitability of technical analysis based on moving average strategies in BRICS

• Matheus José Silva de Souza 1 ,
• Danilo Guimarães Franco Ramos 2 ,
• Marina Garcia Pena 2 ,
• Vinicius Amorim Sobreiro 2 &
• Herbert Kimura 2  

Financial Innovation volume  4 , Article number:  3 ( 2018 ) Cite this article

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In this paper, we investigated the profitability of technical analysis as applied to the stock markets of the BRICS member nations. In addition, we searched for evidence that technical analysis and fundamental analysis can complement each other in these markets. To implement this research, we created a comprehensive portfolio containing the assets traded in the markets of each BRICS member. We developed an automated trading system that simulated transactions in this portfolio using technical analysis techniques. Our assessment updated the findings of previous research by including more recent data and adding South Africa, the latest member included in BRICS. Our results showed that the returns obtained by the automated system, on average, exceeded the value invested. There were groups of assets from each country that performed well above the portfolio average, surpassing the returns obtained using a buy and hold strategy. The returns from the sample portfolio were very strong in Russia and India. We also found that technical analysis can help fundamental analysis identify the most dynamic companies in the stock market.

Introduction

The basic principle of technical analysis (TA) is that patterns related to past prices of instruments traded in the asset markets can be used to predict the direction of future prices. The objective is to enhance the return of an investment portfolio by understanding the interaction of price indicators for the portfolio’s holdings over an identified time period. According to Stanković et al. ( 2015 ), TA is a way of detecting trends in asset prices based on the premise that the price series moves according to investors’ perceived standards. Their study demonstrated that the duration of these standards is sufficient for the investor to make above-average profits, even if the investments incur transaction costs.

The goal of our research was to investigate the profitability of trading strategies based on TA in the stock markets of BRICS countries. To this end, we developed an automated trading system based on the moving averages of past prices. We demonstrated that this trading system, using technical analysis techniques, could surpass the profitability of a buy and hold strategy for a portion of the traded assets, calculated by country. The work presented in this paper updated the findings of previous research, and found that technical analysis can help fundamental analysis identify the most dynamic companies in the stock market.

TA uses a systematic, graphical approach to identify patterns of historical trading prices and market movements, and then formulate predictions that may generate abnormally strong returns. According to Murphy ( 1999 , pp. 1–2), graphs are the primary instruments of TA. The graphs reflect indicators, such as moving averages and oscillators, that allow analysts to detect trends, identify points of inflection in the price movement, and track capital inflows and outflows.

The tools used by TA can provide an index of resistance and support as well. Indicators include the Relative Strength Index (RSI), the Moving Average Convergence Divergence (MACD), and the Average Directional Index (ADX), among others. These indicators seek to estimate patterns of future behavior and predict buy and sell opportunities solely from the previously verified pricing of assets. More specifically, Vandewalle et al. ( 1999 , pp. 170–172) defined moving averages as transformations of a price series that allow us to identify trends from data smoothing.

According to Gerritsen ( 2016 ), the success of technical analysis trading rules would conflict with the weak form of the Efficient Market Hypothesis (EMH) (Fama 1970 ), which holds that current asset prices reflect all relevant past data. In its weak form, EMH states that it is not possible to obtain above-average returns from the study of past prices (Malkiel and Fama 1970 , p. 383), implying that a price series has a unit root. Therefore, belief in the validity of TA means rejecting EMH. Expressed in economic terms, Jensen ( 1978 , p. 97) considered a market to be efficient if the economic profit is null, i.e., if the market meets the optimal condition that marginal benefit equals the marginal cost of acting based on the publicly available information.

Technical analysis is not compatible with the idea that stock prices can change at random (the random walk hypothesis), as pointed out by Lo and MacKinlay ( 1987 , pp. 87–88). A series of prices presents a unit root, or follows a random walk, if the observations at an instant t can be expressed as the price in t −  1 added to a random shock. In other words, random factors persist in determining the observations of the variable, since the shock is little dissipated over time. More formally, let pt. be the price of an asset at the instant t , and let εt be a term denoting a random shock. If the data generation process is in the following form:

, then the series of prices is said to be a unit root if α is not statistically different from 1, which means that the random shock is completely absorbed in the process.

In comparison to TA, fundamental analysis (FA) is focused on the economic and financial aspects of stocks and the markets. According to Lui and Mole ( 1998 ), FA turns to the microeconomic aspects of companies and to the macroeconomic fundamentals of sectors and countries — known as market fundamentals (Allen and Taylor 1990 ) — to justify past movements and to predict fluctuations. Through the review of previous research, we also made clear that FA and TA are not mutually exclusive tools for analyzing market data, but rather explore different drivers of price behavior. TA could be an auxiliary tool to FA. In fact, some studies explored a hybrid approach using both TA and FA, e.g., Lui and Mole ( 1998 ), Lam ( 2004 ), and António Silva and Neves ( 2015 ). In this paper, however, we focused primarily on TA. For our research, we assumed that prices are determined by the equilibrium between the supply and demand of the asset to which they refer. Therefore, prices captures any considerations that may be brought by fundamental analysis (Nison 1991 , pp. 8–11).

The remainder of this paper is structured as follows: In Section 2, we give a brief summary of related research regarding both the development of TA and the results of experiments with data from emerging countries. Section 3 provides the conceptual foundation of TA, while section 4 explains our method and the algorithm applied to generate buy and sell signals. Section 5 discusses the main results obtained, demonstrates the importance of using TA and FA as complementary tools for obtaining profits in the open market, and draws attention to the importance of these results for the literature. Section 6 provides our conclusion.

Related research

Scholars have tested the efficiency of the tools of technical analysis frequently, for example, in the studies of Allen and Taylor ( 1990 ), Jegadeesh ( 2000 ), and Kuang et al. ( 2014 ). The main reasons for this continued research, as discussed in Zhu and Zhou ( 2009 ), were that previous studies of the profitability of technical analysis obtained inconclusive results and lacked a scientific basis. Consequently, more consistent hypotheses to justify TA were needed. For example, Allen and Taylor ( 1990 ), Frankel and Froot ( 1986 ), Shiller ( 1989 ), and others pointed out the irrationality of TA. According to Allen and Taylor ( 1990 ), the subjectivity of this approach prevents it from acquiring a scientific character. Frankel and Froot ( 1986 ) and Shiller ( 1989 ) held that the use of technical indicators leads to overvaluation of asset prices, thereby heating up the demand for some assets without good reason.

There have been few experimental tests of the profitability of the TA indicators across the typical market structures of emerging countries. In particular, further work is needed regarding the BRICS member nations, a special subgroup composed of Brazil, Russia, India, China, and South Africa. Recently, studies were carried out on isolated emerging markets that are not similar to each other, including contributions by Chang et al. ( 2004 ), Kuang et al. ( 2014 ), Mitra ( 2011 ), and Mobarek et al. ( 2008 ). However, none of these studies proposed a comparison of the results for groups of similar countries, so they failed to answer whether TA is profitable for emerging markets as a whole.

Interest in these countries has been stimulated by the typical characteristics of their macroeconomic environments, such as instability, uncertainty, and inflation resulting from their adopted economic growth strategies. According to Chang et al. ( 2004 ), emerging countries became attractive markets to investors looking for portfolio diversification and financial returns above the average attainable from the consolidated markets of developed countries. Emerging markets differ from markets in developing countries insofar as they are closer to the markets of developed countries, making them more dynamic and attractive to foreign investors. On this topic, Mukherjee and Roy ( 2016 ) emphasized the relationship between instrument price fluctuations and macroeconomic particularities.

The good predictability of TA and the high returns in emerging markets are not unanimously accepted in the literature. Chang et al. ( 2004 ) and Harvey ( 1995 ) emphasized that there is a strong autocorrelation in the price series of emerging markets, which means that the random walk hypothesis is rejected. Therefore, there is a good predictive capacity in these markets. However, Costa et al. ( 2015 ) and Ratner and Leal ( 1999 ), who considered transaction costs, identified that the predictive capacity of TA does not lead to abnormally strong returns.

In this context, Urrutia ( 1995 ) identified positive results of TA for Latin American countries. Noakes and Rajaratnam ( 2014 ) signaled mixed results for South Africa because the profitability of TA for low capitalization assets sustains itself, which is the opposite of more commonly traded assets. Sharma and Kennedy ( 1977 ) showed negative results for India. Almujamed et al. ( 2013 ); Errunza and Losq ( 1985 ) suggested there is a lower degree of efficiency in emerging markets, compared to the consolidated markets of developed countries. Sobreiro et al. ( 2016 , p. 99) found that a strategy based on the crossover of moving averages generated greater profits than a static strategy for Russia, Brazil, and Argentina, but not for the markets of Jamaica and China.

Table  1 summarizes the results of the main studies of the profitability of TA in both emerging and developed countries. Surveys were considered to provide mixed evidence if their results demonstrated that the good performance of technical analysis was not sustained after considering transaction costs.

Based on this context, the objective of this paper was to investigate the profitability of moving average trading strategies in the stock markets of BRICS countries. We sought to analyze the performance of TA in environments that are different from those of developed countries and other emerging nations in terms of their stock markets, the behavior of investors, and national economic policies (Mozumder et al. 2015 ; Naresha et al. 2017 ).

For this research, we used an automated trading system (ATS) that simulated the transactions based on patterns verified by the data and related to the signals of the moving averages over the prices of the assets. We prepared a comprehensive portfolio for each country, containing all the assets traded in the markets of each BRICS member. For South Africa, China, and India, we included the asset prices from 2000 to 2016. For Brazil and Russia, we used price data from 2007 to 2016. Initial capital transactions were carried out as the model issued buy and sell signals from the interaction of the series of moving averages over prices.

In this work, we sought to complement the approach of Costa et al. ( 2015 ) and Sobreiro et al. ( 2016 ) in some respects. First, we studied the performance of technical analysis for the instruments traded in Brazil as verified in Costa et al. ( 2015 ), and also for the BRICS members, to check the profitability of indicators for a more general class of countries. In contrast to Sobreiro et al. ( 2016 ), we included transaction costs, aiming to establish more realistic assumptions.

Our study aimed to update results from Chong et al. ( 2010 ) by using more recent data and adding South Africa to the analysis, the latest member to be included in the BRICS countries. In this context, we investigated all BRICS countries, instead of only the BRIC nations, using data through 2016. It is important to highlight that both Sobreiro et al. ( 2016 ) and Chong et al. ( 2010 ) did not analyze the results of trading strategies that took into account transaction costs. Therefore, our automated trading system, by operating with and without brokerage fees, allowed us to assess the impact of transaction costs on the overall profitability of the strategies.

A brief overview of the conceptual foundation of technical a nalysis

Nison ( 1991 , pp. 8–11) added the psychological and emotional components of the rational agents to the study of asset prices in the financial market. This approach was capable of capturing the animal spirits spoken about by Keynes ( 1936 ), a concept that is not incorporated in fundamental analysis. Nison ( 1991 ) suggested that the study of technical analysis is important because it provides an understanding of why the market moves. The author emphasized that great negotiators make their decisions based on technical indicators. Both the previous price and the influence exercised by leaders over the decisions of other investors are factors that determine the price movement itself.

Ellis and Parbery ( 2005 ) highlighted the use of moving averages for the generation of buy and sell signals as a mechanism to identify price trends. While the short-term moving average is more sensitive to price changes, longer term moving averages capture medium- and long-term trends. Investors in the stock exchanges utilize technical analysis extensively, and moving averages are the most commonly used indicators because they are simple to understand and relatively easy to use.

Regarding the calculation of the moving averages, let h be the length of the moving average, i.e., the number of observations from which the average of the values will be extracted, and let N ≥ h be the position of a given observation from which the previous h values will be included in the calculation of the N -th moving average. If SMAN is the N -th simple moving average, and EMAN is the N th exponential moving average, they can be calculated as follows:

For a deeper explanation of the simple moving average, please see Vandewalle et al. ( 1999 ). According to Appel ( 2005 ), the exponential moving average is better than the simple moving average for identifying trends in a price series. Park and Irwin ( 2007 , p. 67) summarized the evidence for the profitability of technical analysis in futures contracts, foreign currency markets, and in the capital markets. According to the authors, from 1988 to 2004, 26 studies obtained positive results for the use of technical indicators in the capital markets, and 12 found negative results. However, Park and Irwin ( 2007 , pp. 29–30) concluded that the positive results of technical analysis were more consistent and significant for the futures and foreign currency markets, compared to results for the stock markets. Also, the authors concluded that TA’s positive results for asset markets were subject to data manipulation problems and the creation of ex-post strategies.

In previous research, findings about the profitability of technical analysis were quite inconsistent when applied to the stock markets of emerging countries. In general, the simple moving average (SMA) or exponential moving average (EMA) strategies assured a positive return, but the return was not sustained when transaction costs were considered, such as fees paid to the broker (Brock et al. 1992 ).

Similar results were presented by Mitra ( 2011 ), and Ratner and Leal ( 1999 ) when they compared the returns obtained from the generation of buy or sell signals with the returns of a static strategy such as buy and hold. The former study focused on financial assets traded in India, and found that when the short-term moving average crossed above the long-term moving average, the prices generated positive net results. However, when transaction costs were considered, this profitability did not sustain itself. Ratner and Leal’s study (Ratner and Leal 1999 ), which was broader and considered countries in Latin America and Asia, reached the same conclusion. The exceptions were the Taiwanese, Mexican, and Thai markets, whose profitability was maintained even after transaction costs were included.

For data regarding the United States of America (USA), Alexander ( 1961 ), Brock et al. ( 1992 ), and Fama and Blume ( 1966 ) found that if the transaction costs were not zero, the profitability gained by applying technical analysis was not significant. In comparison, Kuang et al. ( 2014 ) achieved an average annual return of approximately 30% for emerging countries’ stock markets. However, they considered that this profitability was not accurate, since it was the result of problems arising from prior manipulation of the data to discover ex-ante patterns.

In a study using data from Bangladesh, Mobarek et al. ( 2008 ) proposed that the accelerated growth of the capitalization level in that country was an investment opportunity. The research emphasized that Bangladesh was an emerging country that had undergone extreme structural economic changes in which the focus on agriculture was abandoned in favor of a strategy involving industrialization and the formation of new companies. The null hypothesis that the market is weakly efficient was rejected after verification.

These results showed the weakness of moving average techniques in predicting price behavior. They also suggested that if transaction costs are negligible, technical analysis becomes a viable alternative, indicating that under certain conditions the markets are not efficient. Treynor and Ferguson ( 1985 ) emphasized the importance of historical prices in forecasting price behavior as a complement to the role played by the information available to suppliers and claimants who are, above all, responsible for creating profit opportunities.

Shynkevich ( 2012 ) concluded that the profitability of technical analysis for portfolios holding small cap assets with less liquidity was greater than for portfolios holding large cap companies from the technology area. For this reason, it is especially relevant to analyze the returns of classic technical indicators for emerging markets where more small caps are expected, possibly because of policies used to stimulate industrial activity.

Recent empirical evidence for South Africa verified by Noakes and Rajaratnam ( 2014 ) suggested that the level of capitalization of traded assets in that country was inversely related to market inefficiency. Moreover, the authors suggested that the degree of market efficiency falls during periods of crisis, as during the financial crisis of 2008.

The research of Costa et al. ( 2015 ) analyzed the power of technical analysis indicators for the Brazilian asset market. The authors concluded that technical analysis has weak predictive power whether or not brokerage fees are considered. However, the use of crossing moving averages, simple or exponential, and Moving Average Convergence Divergence (MACD) provided a high probability of guaranteeing a return greater than the amount invested. In general, research indicated that it is natural for markets to become efficient, because they do not obtain significant returns from past price behavior. Thus, evidence for technical analysis in emerging markets suggested less efficiency in these countries, which might set up an attractive investment option for the foreign investor.

Sobreiro et al. ( 2016 ) obtained positive and above-average returns generated by the static buy and hold strategy for the short-term SMA crossing over the long-term SMA. However, although some combinations of short- and long-term SMAs were profitable for some countries, they did not provide sustained profitability for other emerging countries. Consequently, a more general conclusion could not be reached from the study. In general, buy and hold is a more profitable and risk-free alternative to an automated strategy for most emerging markets.

It is worth mentioning that the approach of Sobreiro et al. ( 2016 ) does not explore the impact of transaction cost on a portfolio’s return, which has a significant cooling effect on the performance of the trades, and is subject to currency rate volatility. With regard to this last aspect, it is worth noting that the authors’ use of 10,000.00 local currency units as the initial value of the portfolio left the investments open to the effects of exchange rate fluctuations and inflation that often impact the currencies of emerging countries.

Concerning the influence of technical analysis on fundamental analysis, Almujamed et al. ( 2013 , pp. 57–58) studied data for Kuwait. They concluded that investors check a firm’s profitability before looking at the stock chart movements and stock price trends of the company. Furthermore, they asserted that fundamental analysis that uses a more recent series of prices, usually within five years, is employed more commonly by investors in developed markets, while emerging markets are considered inefficient.

According to Bettman et al. ( 2009 , pp. 21–22), TA and FA are complementary, since models that combine the assumptions and elements of both analyses achieve higher profitability than models based on a single approach only. For their analysis of TA and FA, the authors ran linear regression models with explanatory variables from TA, e.g., trend and momentum indicators based on past prices. They also ran models using variables from FA, e.g., book value and earnings per share, and models using variables from both. Bettman’s findings indicated that a model with independent variables from both approaches provided better performance based on statistics such as the Akaike information criterion (AIC) and likelihood ratio tests. The work of Wang et al. ( 2014 , pp. 33) supported a similar conclusion, showing that the joint application of FA and TA reduced the risk of the investment.

Chong et al. ( 2010 , pp. 237–238) set out to compare the performance of the traditional technical analysis indicators for the BRIC1. They concluded that the average profit in Russia surpassed the returns obtained in the other countries, and the evidence indicated that the Brazilian open market was the most efficient. The authors attributed these findings to the fact that the age of the market was directly related to efficiency. Therefore, they supported the view that markets become efficient over time. However, the costs associated with open market buy and sell transactions were not considered. Lo et al. ( 2000 , pp. 1753–1764) demonstrated that technical analysis benefits from the automation provided by computerized trading systems, with emphasis on the identification of visual patterns in the asset price series.

Tharavanij et al. ( 2015 , pp. 39–40) analyzed the performance of a wide variety of technical indicators for similar Asian emerging markets, such as Malaysia, Indonesia, Singapore, and Thailand. The analysis was conducted on a risk-adjusted basis, and accounted for brokerage fees. The authors found several levels of efficiency in the markets, but overall, TA strategies could not beat the buy and hold benchmark, and prices could not foster excess returns above the market average. These results indicated that similar characteristics did not lead to a single winning strategy.

To meet the objectives of this paper, we developed a transaction model, called the automated trading system (ATS), that worked automatically based on classic technical analysis, especially the use of moving averages, to soften price series and identify trends. As described by Booth et al. ( 2014 , p. 3651), automated trading systems perform trades autonomously, identifying investment opportunities based on artificial intelligence methods. The procedures that define the strategy used to generate trading signals can vary substantially. Technical indicators have found wide spread use for this purpose as a result of their extensive application by market practitioners.

Whatever the method used in a trading system, the base assumption is still the same: price predictions are based on past price data. According to Cervelló-Royo et al. ( 2015 , p. 5963), this principle imposes an important challenge for individual investors and companies, because forecasts of future prices are subject to occasional unexpected fluctuations that do not depend on the historical behavior of the markets. Chen and Chen ( 2016 , pp. 261–262) indicated that the stock market is subject to many changes in the underlying environment, such as variations in economic, political, and industrial conditions. According to the authors, finding the proper means for analysis is paramount for defining better or worse strategies for generating profits in the market.

Concerning the psychological aspects of the investors, Pring ( 2016 , pp. 2–5) emphasized that TA reflects the concept that price trends depend on the attitudes of individuals, i.e., the mass psychology of the crowd. In this context, technical analysis relies on the assumption that herd behavior fluctuates between periods of fear or pessimism and times of confidence or optimism.

We chose to use the crossover of moving averages for the generation of buy and sell signals because this technique is employed extensively by financial market analysts, is based on graphical patterns of historical market prices (Alexander 1961 ; Reitz 2006 ), and allows for a comparatively simple approach to computational implementation. The algorithm for the generation of buy signals is based on the crossing of two series generated from the available quotations for the assets: the short-term moving average and the long-term moving average. For the analysis of the technical indicators, based on Ellis and Parbery ( 2005 ), we agreed that a buy signal would be issued when the short-term MA becomes bigger than the long-term MA, and a sales signal would be issued when the short-term MA becomes smaller than the long-term MA.

The study’s data came from the daily closing quotations for 1454 assets traded on the BRICS stock exchanges: 236 assets from South Africa, 198 assets from Brazil, 65 assets from Russia, 755 assets from India, and 300 assets from China, as shown in Table  2 . The data were taken from Bloomberg© and included historical prices for 2569 assets. For computing purposes, we opted to choose the 300 most dynamic assets in the Chinese market.

Of the total assets of the database, some did not allow the generation of buy/sell signals, and therefore were excluded from the portfolio. Data for South Africa, China, and India corresponded to the period from 2000 to 2016. For Brazil and Russia, the period considered was from 2007 to 2016. For the transaction simulations, we used the closing prices per day.

Also, the simulations were carried out considering an application of US$10,000.00 in local currency quoted on June 24, 2016 to normalize the investment from the perspective of an external investor. Returns obtained were compared with and without the inclusion of costs. Neither of these aspects were considered in Sobreiro et al. ( 2016 ), whose simulations were made with the initial application of 10,000.00 local currency units and without considering transaction costs. Similarly, costs were not considered in Chong et al. ( 2010 ).

For our research, we constructed a portfolio composed of a wide number of holdings. This approach allowed us to verify the average profitability gained through technical analysis for all assets traded in the stock market for each BRICS member country. Given these conditions, we considered an investor who was investing US$10,000.00 in each asset of the country, converted at the exchange rate on June 24, 2016.

In the moving average system, a buy signal is generated when the short-term MA becomes greater than the long-term MA, indicating the start of an uptrend and the end of a downtrend. On the other hand, if the long-term MA becomes greater than the short-term MA, a sell signal is generated. This is one of the very basic principles agreed upon among chartists.

It is worth noting that three types of moving average crossovers were analyzed in our trading system: SMA-SMA, SMA-EMA, and EMA-EMA. In each class, we used groups of MA combinations, with the short-term MA ranging from 5 to 40 periods, and the long-term MA varying from 80 to 120 periods. Although the periods were arbitrary, the short-term MA reflected a time horizon of approximately 2 months, and the long-term MA a time horizon between 4 to 6 months. To perform the computational experiment, the algorithm was implemented in the software’s programming language.

Since the short-term MA varied between 5 and 40 periods, and the long-term MA varied between 80 and 120 periods, we had 1.476 strategies for a single class of crossover. Thus, we had 4.428 strategies, and for each one, three simulations were made: without transaction costs, with brokerage costs of 2%, and with brokerage costs of 5%.

Since the purpose of the study was to formulate an automated model to investigate the profitability and efficiency of technical analysis in emerging markets, the return obtained in local currency was converted into dollars according to the exchange rate of the investment’s initial date. This procedure eliminated the impact of any nominal exchange rate and inflation fluctuations on transactions.

We elaborated and compiled the algorithm in the R software, which allowed handling a large mass of data in an uncomplicated way. In general, the execution flow of the automated trading system can be summarized by the pseudo–code presented in Algorithm 1.

The automated trading system had a graphical user interface (shown in Fig.  1 ), also elaborated in R to facilitate the collection of input data that came from tables containing the closing price history of traded assets and the set of parameters. The latter included the specification of the moving average type, the range of each MA, and the initial capital to be applied.

Interface of Automated Trading System

The use of the automated trading system generated a summary of the performance of each asset in each country. Concerning the profitability of the operations, the proportion of the assets of each country was identified for each strategy. Our approach was able to surpass the profit obtained through buy and hold, which is a lower risk strategy. Buy and hold is a long-term investment approach in which the investor creates a portfolio of assets, and sells only when the valuation of the assets is considered satisfactory, providing above-market average returns.

Table  3 shows the average returns per country when buy and hold was implemented. In short, we applied the buy and hold strategy for each asset of the same country, and we extracted the average profitability of the operations for each country.

The data available in Table 2 supports Table  4 , which shows the proportion of assets in each country that surpassed the average buy and hold return for the same country. We chose to compare the returns of each asset obtained by the automated trading system with the average market return of the risk-free strategy to identify groups of assets that offered good, consistent performance and were issued by dynamic companies in the market.

In general, dynamic strategies for the purchase and sale of assets are studied to determine whether it is possible to obtain above-market average returns in the short term. According to Table 4 , a tiny group of assets surpassed the buy and hold returns using the automated trading system. However, the main conclusion here is that there was a group of assets in each country that could outperform the passive buying strategy.

As shown in Fig.  2 , the average return was very high in India and Russia. Because their stock markets are younger, efficiency may be related to market maturity, indicating that technical analysis performs well and sustains the results of Chong et al. ( 2010 ). However, this argument could be a topic for further study. Moreover, in these same markets, the increase in transaction costs shifted significantly the range of the short-term MAs that were better, as presented by Tables  5 , 6 , and 7 .

Example of the graphic representations

Results for India and Russia indicated higher returns, but our study did not focus on potential explanations for the different results among the countries. TA explores information from past data only, without consideration of macro or micro elements that could explain the future price behavior of specific stocks. Consequently, the results of the analysis indicated potential violations of the weak form of market efficiency, but could not be used to explain potential fundamental rationales for the profitability of trading strategies.

For the South African market, one of the most consolidated of the samples, the most attractive returns were stable. For the three categories of MA crossovers, and for all simulated types of cost, the short-term MA crossover at the interval [37; 40] with the long-term MA of the range [116; 120] proved to be profitable in all simulations. Thus, more efficient markets showed more conservative, but more stable, returns.

This paper investigated the efficiency and profitability of applying technical analysis to the stock markets of BRICS member countries. We analyzed whether investors could obtain above-average returns, as suggested by the recent research of Stanković et al. ( 2015 ) and others. For this research, we assembled a comprehensive portfolio of stocks from the BRICS countries that contained all the assets traded in the markets of each BRICS member. We developed an automated trading system that simulated transactions in this portfolio using technical analysis techniques.

While this system was developed carefully, the study had some limitations. For example, we assumed that the stocks had high liquidity, and that transactions could be traded at specific market prices. Nonetheless, the results indicated that our automated trading system, using technical analysis, could surpass the profitability of a buy and hold strategy for a small portion of the traded assets, calculated by country. Although small, this portion presented returns well above the amount invested, because the gains were from assets related to dynamic companies in the stock market.

Our findings demonstrated the feasibility and value of applying technical analysis in this context. On average, the returns obtained using TA surpassed the value invested. Since some assets performed very well, they covered the losses incurred by other low-performing assets. However, few combinations of moving averages were able to outperform the returns from a buy and hold strategy.

In addition, our study suggests that technical analysis and fundamental analysis can complement each other. We proposed that TA could foster the search for groups of companies listed on the stock market that have a dynamic level of capitalization and present a strong profit opportunity for investors. For this portion of our work, we analyzed combinations of moving averages that were persistently profitable within the BRICS markets. Table 4 indicates that some assets could surpass the returns obtained by a risk-free strategy. Tables 5 , 6 , and 7 display pairs of MAs with a higher density of positive results, i.e., combinations of MAs in which the returns obtained by good performing assets raised the average return, even though there were many low-performing assets.

This study also contributed to the evidence that market age is directly related to market efficiency, as suggested by Chong et al. ( 2010 ). Thus, the assumption that markets become more efficient over time was supported, even when the automated trading system included transaction costs. This result was linked to the fact that the Brazilian stock market, the second oldest within the sample, generated one of the lowest average returns. This evidence suggests that the markets become more efficient as time goes by, implying that for older stock markets, historical prices may contain less information that can be used to generate above-average returns. However, since there is not a definitive a priori hypothesis that links stock market age and market efficiency, the outcome of the study cannot support this relationship decisively.

Our findings indicated further that even though the sample countries are classified as emerging, and they are part of the same economic group, their respective stock markets are not necessarily close to each other in terms of their behavior. This conclusion is based on the difficulty identifying a single combination of moving averages common to all the countries analyzed that could generate a consistent return. Moreover, the average return obtained diverged considerably among the BRICS stock exchanges, showing that the efficiency of a market and the opportunities for profitability are more closely related to the age of the market than to whether the country is emerging.

Our study suggested that even though the BRICS markets may share similar characteristics, the trading systems lead to very heterogeneous results. In some countries, trading based on moving averages could not exceed the buy and hold strategy. Therefore, there is no clear pattern in the historical data that could be used generally across the markets. Although results support that the weak form of the efficient market hypothesis could be rejected, the trading strategy did not lead universally to better results than the gains generated by the buy and hold strategy.

Based on this study, we can point out strategies that result in above-average profitability, raising questions about the EMH in emerging markets. A question that remains to be answered, however, is why some combinations of moving averages perform better than others. For example, in South Africa the most profitable short-term MAs belonged to a very specific range. Another area for future research is analysis of the role played by small cap assets in the performance of moving average strategies in emerging markets.

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Matheus José Silva de Souza

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All authors participated in the development of the research. MJSS, DGFR and MGP conducted the study and the results were discussed initially with VAS and HK. Following the all authors developed the initial version of the manuscript. Then, VAS revised and improvement in the paper and its graphical content. Finally, all authors read and approved the final manuscript.

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Matheus José Silva de Souza holds a Bachelor’s degree in Economics from the University of Brasília.

Danilo Guimarães Franco Ramos holds a Bachelor’s degree in Statistic from the University of Brasília.

Marina Garcia Pena holds a Bachelor’s degree in Statistic from the University of Brasília.

Vinicius Amorim Sobreiro is an Adjunct Professor at the Department of Management at the University of Brasília. He holds a PhD in Production Engineering. He received his Bachelor’s degree in Economics from the Antônio Eufrásio Toledo College.

Herbert Kimura is a Full Professor at the Department of Management at the University of Brasília. He holds a PhD in Statistic. He received his Bachelor’s degree in Electronic Engineering from the Institute of Aeronautical Technology.

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de Souza, M.J.S., Ramos, D.G.F., Pena, M.G. et al. Examination of the profitability of technical analysis based on moving average strategies in BRICS. Financ Innov 4 , 3 (2018). https://doi.org/10.1186/s40854-018-0087-z

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Title: text representation enrichment utilizing graph based approaches: stock market technical analysis case study.

Abstract: Graph neural networks (GNNs) have been utilized for various natural language processing (NLP) tasks lately. The ability to encode corpus-wide features in graph representation made GNN models popular in various tasks such as document classification. One major shortcoming of such models is that they mainly work on homogeneous graphs, while representing text datasets as graphs requires several node types which leads to a heterogeneous schema. In this paper, we propose a transductive hybrid approach composed of an unsupervised node representation learning model followed by a node classification/edge prediction model. The proposed model is capable of processing heterogeneous graphs to produce unified node embeddings which are then utilized for node classification or link prediction as the downstream task. The proposed model is developed to classify stock market technical analysis reports, which to our knowledge is the first work in this domain. Experiments, which are carried away using a constructed dataset, demonstrate the ability of the model in embedding extraction and the downstream tasks.

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Data Analytics Case Study Guide 2024

by Sam McKay, CFA | Data Analytics

Data analytics case studies reveal how businesses harness data for informed decisions and growth.

For aspiring data professionals, mastering the case study process will enhance your skills and increase your career prospects.

So, how do you approach a case study?

Use these steps to process a data analytics case study:

Understand the Problem: Grasp the core problem or question addressed in the case study.

Collect Relevant Data: Gather data from diverse sources, ensuring accuracy and completeness.

Apply Analytical Techniques: Use appropriate methods aligned with the problem statement.

Visualize Insights: Utilize visual aids to showcase patterns and key findings.

Derive Actionable Insights: Focus on deriving meaningful actions from the analysis.

This article will give you detailed steps to navigate a case study effectively and understand how it works in real-world situations.

By the end of the article, you will be better equipped to approach a data analytics case study, strengthening your analytical prowess and practical application skills.

Let’s dive in!

Table of Contents

What is a Data Analytics Case Study?

A data analytics case study is a real or hypothetical scenario where analytics techniques are applied to solve a specific problem or explore a particular question.

It’s a practical approach that uses data analytics methods, assisting in deciphering data for meaningful insights. This structured method helps individuals or organizations make sense of data effectively.

Additionally, it’s a way to learn by doing, where there’s no single right or wrong answer in how you analyze the data.

So, what are the components of a case study?

Key Components of a Data Analytics Case Study

A data analytics case study comprises essential elements that structure the analytical journey:

Problem Context: A case study begins with a defined problem or question. It provides the context for the data analysis , setting the stage for exploration and investigation.

Data Collection and Sources: It involves gathering relevant data from various sources , ensuring data accuracy, completeness, and relevance to the problem at hand.

Analysis Techniques: Case studies employ different analytical methods, such as statistical analysis, machine learning algorithms, or visualization tools, to derive meaningful conclusions from the collected data.

Insights and Recommendations: The ultimate goal is to extract actionable insights from the analyzed data, offering recommendations or solutions that address the initial problem or question.

Now that you have a better understanding of what a data analytics case study is, let’s talk about why we need and use them.

Why Case Studies are Integral to Data Analytics

Case studies serve as invaluable tools in the realm of data analytics, offering multifaceted benefits that bolster an analyst’s proficiency and impact:

Real-Life Insights and Skill Enhancement: Examining case studies provides practical, real-life examples that expand knowledge and refine skills. These examples offer insights into diverse scenarios, aiding in a data analyst’s growth and expertise development.

Validation and Refinement of Analyses: Case studies demonstrate the effectiveness of data-driven decisions across industries, providing validation for analytical approaches. They showcase how organizations benefit from data analytics. Also, this helps in refining one’s own methodologies

Showcasing Data Impact on Business Outcomes: These studies show how data analytics directly affects business results, like increasing revenue, reducing costs, or delivering other measurable advantages. Understanding these impacts helps articulate the value of data analytics to stakeholders and decision-makers.

Learning from Successes and Failures: By exploring a case study, analysts glean insights from others’ successes and failures, acquiring new strategies and best practices. This learning experience facilitates professional growth and the adoption of innovative approaches within their own data analytics work.

Including case studies in a data analyst’s toolkit helps gain more knowledge, improve skills, and understand how data analytics affects different industries.

Using these real-life examples boosts confidence and success, guiding analysts to make better and more impactful decisions in their organizations.

But not all case studies are the same.

Let’s talk about the different types.

Types of Data Analytics Case Studies

Data analytics encompasses various approaches tailored to different analytical goals:

Exploratory Case Study: These involve delving into new datasets to uncover hidden patterns and relationships, often without a predefined hypothesis. They aim to gain insights and generate hypotheses for further investigation.

Predictive Case Study: These utilize historical data to forecast future trends, behaviors, or outcomes. By applying predictive models, they help anticipate potential scenarios or developments.

Diagnostic Case Study: This type focuses on understanding the root causes or reasons behind specific events or trends observed in the data. It digs deep into the data to provide explanations for occurrences.

Prescriptive Case Study: This case study goes beyond analytics; it provides actionable recommendations or strategies derived from the analyzed data. They guide decision-making processes by suggesting optimal courses of action based on insights gained.

Each type has a specific role in using data to find important insights, helping in decision-making, and solving problems in various situations.

Regardless of the type of case study you encounter, here are some steps to help you process them.

Roadmap to Handling a Data Analysis Case Study

Embarking on a data analytics case study requires a systematic approach, step-by-step, to derive valuable insights effectively.

Here are the steps to help you through the process:

Step 1: Understanding the Case Study Context: Immerse yourself in the intricacies of the case study. Delve into the industry context, understanding its nuances, challenges, and opportunities.

Identify the central problem or question the study aims to address. Clarify the objectives and expected outcomes, ensuring a clear understanding before diving into data analytics.

Step 2: Data Collection and Validation: Gather data from diverse sources relevant to the case study. Prioritize accuracy, completeness, and reliability during data collection. Conduct thorough validation processes to rectify inconsistencies, ensuring high-quality and trustworthy data for subsequent analysis.

Step 3: Problem Definition and Scope: Define the problem statement precisely. Articulate the objectives and limitations that shape the scope of your analysis. Identify influential variables and constraints, providing a focused framework to guide your exploration.

Step 4: Exploratory Data Analysis (EDA): Leverage exploratory techniques to gain initial insights. Visualize data distributions, patterns, and correlations, fostering a deeper understanding of the dataset. These explorations serve as a foundation for more nuanced analysis.

Step 5: Data Preprocessing and Transformation: Cleanse and preprocess the data to eliminate noise, handle missing values, and ensure consistency. Transform data formats or scales as required, preparing the dataset for further analysis.

Step 6: Data Modeling and Method Selection: Select analytical models aligning with the case study’s problem, employing statistical techniques, machine learning algorithms, or tailored predictive models.

In this phase, it’s important to develop data modeling skills. This helps create visuals of complex systems using organized data, which helps solve business problems more effectively.

Understand key data modeling concepts, utilize essential tools like SQL for database interaction, and practice building models from real-world scenarios.

Furthermore, strengthen data cleaning skills for accurate datasets, and stay updated with industry trends to ensure relevance.

Step 7: Model Evaluation and Refinement: Evaluate the performance of applied models rigorously. Iterate and refine models to enhance accuracy and reliability, ensuring alignment with the objectives and expected outcomes.

Step 8: Deriving Insights and Recommendations: Extract actionable insights from the analyzed data. Develop well-structured recommendations or solutions based on the insights uncovered, addressing the core problem or question effectively.

Step 9: Communicating Results Effectively: Present findings, insights, and recommendations clearly and concisely. Utilize visualizations and storytelling techniques to convey complex information compellingly, ensuring comprehension by stakeholders.

Step 10: Reflection and Iteration: Reflect on the entire analysis process and outcomes. Identify potential improvements and lessons learned. Embrace an iterative approach, refining methodologies for continuous enhancement and future analyses.

This step-by-step roadmap provides a structured framework for thorough and effective handling of a data analytics case study.

Now, after handling data analytics comes a crucial step; presenting the case study.

Presenting Your Data Analytics Case Study

Presenting a data analytics case study is a vital part of the process. When presenting your case study, clarity and organization are paramount.

To achieve this, follow these key steps:

Structuring Your Case Study: Start by outlining relevant and accurate main points. Ensure these points align with the problem addressed and the methodologies used in your analysis.

Crafting a Narrative with Data: Start with a brief overview of the issue, then explain your method and steps, covering data collection, cleaning, stats, and advanced modeling.

Visual Representation for Clarity: Utilize various visual aids—tables, graphs, and charts—to illustrate patterns, trends, and insights. Ensure these visuals are easy to comprehend and seamlessly support your narrative.

Highlighting Key Information: Use bullet points to emphasize essential information, maintaining clarity and allowing the audience to grasp key takeaways effortlessly. Bold key terms or phrases to draw attention and reinforce important points.

Addressing Audience Queries: Anticipate and be ready to answer audience questions regarding methods, assumptions, and results. Demonstrating a profound understanding of your analysis instills confidence in your work.

Integrity and Confidence in Delivery: Maintain a neutral tone and avoid exaggerated claims about findings. Present your case study with integrity, clarity, and confidence to ensure the audience appreciates and comprehends the significance of your work.

By organizing your presentation well, telling a clear story through your analysis, and using visuals wisely, you can effectively share your data analytics case study.

This method helps people understand better, stay engaged, and draw valuable conclusions from your work.

We hope by now, you are feeling very confident processing a case study. But with any process, there are challenges you may encounter.

Key Challenges in Data Analytics Case Studies

A data analytics case study can present various hurdles that necessitate strategic approaches for successful navigation:

Challenge 1: Data Quality and Consistency

Challenge: Inconsistent or poor-quality data can impede analysis, leading to erroneous insights and flawed conclusions.

Solution: Implement rigorous data validation processes, ensuring accuracy, completeness, and reliability. Employ data cleansing techniques to rectify inconsistencies and enhance overall data quality.

Challenge 2: Complexity and Scale of Data

Challenge: Managing vast volumes of data with diverse formats and complexities poses analytical challenges.

Solution: Utilize scalable data processing frameworks and tools capable of handling diverse data types. Implement efficient data storage and retrieval systems to manage large-scale datasets effectively.

Challenge 3: Interpretation and Contextual Understanding

Challenge: Interpreting data without contextual understanding or domain expertise can lead to misinterpretations.

Solution: Collaborate with domain experts to contextualize data and derive relevant insights. Invest in understanding the nuances of the industry or domain under analysis to ensure accurate interpretations.

Challenge 4: Privacy and Ethical Concerns

Challenge: Balancing data access for analysis while respecting privacy and ethical boundaries poses a challenge.

Solution: Implement robust data governance frameworks that prioritize data privacy and ethical considerations. Ensure compliance with regulatory standards and ethical guidelines throughout the analysis process.

Challenge 5: Resource Limitations and Time Constraints

Challenge: Limited resources and time constraints hinder comprehensive analysis and exhaustive data exploration.

Solution: Prioritize key objectives and allocate resources efficiently. Employ agile methodologies to iteratively analyze and derive insights, focusing on the most impactful aspects within the given timeframe.

Recognizing these challenges is key; it helps data analysts adopt proactive strategies to mitigate obstacles. This enhances the effectiveness and reliability of insights derived from a data analytics case study.

Now, let’s talk about the best software tools you should use when working with case studies.

Top 5 Software Tools for Case Studies

In the realm of case studies within data analytics, leveraging the right software tools is essential.

Here are some top-notch options:

Tableau : Renowned for its data visualization prowess, Tableau transforms raw data into interactive, visually compelling representations, ideal for presenting insights within a case study.

Python and R Libraries: These flexible programming languages provide many tools for handling data, doing statistics, and working with machine learning, meeting various needs in case studies.

Microsoft Excel : A staple tool for data analytics, Excel provides a user-friendly interface for basic analytics, making it useful for initial data exploration in a case study.

SQL Databases : Structured Query Language (SQL) databases assist in managing and querying large datasets, essential for organizing case study data effectively.

Statistical Software (e.g., SPSS , SAS ): Specialized statistical software enables in-depth statistical analysis, aiding in deriving precise insights from case study data.

Choosing the best mix of these tools, tailored to each case study’s needs, greatly boosts analytical abilities and results in data analytics.

Final Thoughts

Case studies in data analytics are helpful guides. They give real-world insights, improve skills, and show how data-driven decisions work.

Using case studies helps analysts learn, be creative, and make essential decisions confidently in their data work.

Check out our latest clip below to further your learning!

Frequently Asked Questions

What are the key steps to analyzing a data analytics case study.

When analyzing a case study, you should follow these steps:

Clarify the problem : Ensure you thoroughly understand the problem statement and the scope of the analysis.

Make assumptions : Define your assumptions to establish a feasible framework for analyzing the case.

Gather context : Acquire relevant information and context to support your analysis.

Analyze the data : Perform calculations, create visualizations, and conduct statistical analysis on the data.

Provide insights : Draw conclusions and develop actionable insights based on your analysis.

How can you effectively interpret results during a data scientist case study job interview?

During your next data science interview, interpret case study results succinctly and clearly. Utilize visual aids and numerical data to bolster your explanations, ensuring comprehension.

Frame the results in an audience-friendly manner, emphasizing relevance. Concentrate on deriving insights and actionable steps from the outcomes.

How do you showcase your data analyst skills in a project?

To demonstrate your skills effectively, consider these essential steps. Begin by selecting a problem that allows you to exhibit your capacity to handle real-world challenges through analysis.

Methodically document each phase, encompassing data cleaning, visualization, statistical analysis, and the interpretation of findings.

Utilize descriptive analysis techniques and effectively communicate your insights using clear visual aids and straightforward language. Ensure your project code is well-structured, with detailed comments and documentation, showcasing your proficiency in handling data in an organized manner.

Lastly, emphasize your expertise in SQL queries, programming languages, and various analytics tools throughout the project. These steps collectively highlight your competence and proficiency as a skilled data analyst, demonstrating your capabilities within the project.

Can you provide an example of a successful data analytics project using key metrics?

A prime illustration is utilizing analytics in healthcare to forecast hospital readmissions. Analysts leverage electronic health records, patient demographics, and clinical data to identify high-risk individuals.

Implementing preventive measures based on these key metrics helps curtail readmission rates, enhancing patient outcomes and cutting healthcare expenses.

This demonstrates how data analytics, driven by metrics, effectively tackles real-world challenges, yielding impactful solutions.

Why would a company invest in data analytics?

Companies invest in data analytics to gain valuable insights, enabling informed decision-making and strategic planning. This investment helps optimize operations, understand customer behavior, and stay competitive in their industry.

Ultimately, leveraging data analytics empowers companies to make smarter, data-driven choices, leading to enhanced efficiency, innovation, and growth.

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