empirical research finance

Empirical Research in Accounting and Finance

Empirical Research in Accounting and Finance:

Methods and Current Topics

Teaching Assistant: Vivian Wang E-mail: [email protected]

Course Description : This doctoral-level course introduces students to topics and methods of empirical research in Accounting and Finance. It addresses a set of current research topics in the field through reading and analysis of academic papers and active empirical analysis. These topics include the risk and return of capital markets, the quantitative study of the equity risk premium, forecasting markets and security prices with financial and accounting information, valuation and the analysis of the cost of capital and required rates of return on investment, construction of equity portfolios, performance evaluation of investment managers. It is recommended that doctoral students have Financial Economics I before taking this course. A strong background in statistics or econometrics is useful. MBA’s and undergraduates with statistical and/or econometric skills and an interest in the course should contact one of the instructors.

Prerequisites: Financial Economics I, or by permission of instructor. The course is open to Ph.D.’s from all departments, MBA candidates, Law School students and Yale undergraduates. The assignments will require economic skills and knowledge as well as a willingness to work intensively with financial and accounting data. The course demands a lot of time and students should plan accordingly.

Statistical Packages : Students should use the statistical or econometric package that they are already familiar with. If you are starting from scratch, a good package to use is the open sourceware R . It works on all platforms. Much of the data for analysis can be accessed through the ICF website and the WRDS system to which the ICF subscribes. The Ibbotson software system is an important tool and can be accessed only through the SOM Citrix server. This will require an SOM computer account for non-SOM students. Please contact SOM student services bout this.

Papers as specified in the syllabus. Bold papers are expected to be thoroughly read for the class meeting. A useful text for the course is: Campbell, John Y., Andrew W. Lo and A. Craig MacKinlay, 1997, The Econometrics of Financial Markets .

Assignments :

Assignments: three of the assignments may be done in a group of three or less. three assignments may be done individually in place of a research paper. the three additional assignments or the term paper will be due one week following the last class meeting. for each assignment we expect only one or two pages of clear explanation of what you did, and a table or two of results, plus one figure to illustrate the salient finding. clarity and brevity are appreciated – these are not research papers, although you may find something in the analysis that could lead to a term paper. come prepared with your group to discuss the results of your analysis to class with a table or a figure..

Welcome to Tidy Finance

Tidy Finance is an opinionated approach to empirical research in financial economics - a fully transparent, open-source code base in multiple programming languages.

Join Tidy Finance

• Learn about empirical applications based on a fully transparent code base
• Teach students the importance of reproducible research using tidy principles
• Start your next finance research project one step ahead with a Tidy Finance basis
• Support the maintenance of our open-source project
• Contribute to mission of reproducible finance via our blog
• Reach out with ideas, suggestions, and feedback via [email protected]

Choose your language

What experts say about tidy finance.

A clean coding environment is a prerequisite for building a relevant investment platform and conducting meaningful factor research. Tidy Finance is the name of the game, giving aspiring academics and finance practitioners just what they need to perform clean and reproducible research. Highly recommended.

Harald Lohre

Executive Director at Robeco

Honorary Researcher at Lancaster University Management School

From our crowd-sourced paper on non-standard errors, I learned how important clean coding is. Tidy Finance is a rich resource for empirical finance researchers, offering clean coding techniques that benefit both beginners and experts.

Albert J. Menkveld

Professor of Finance at Vrije Universiteit Amsterdam

Fellow at Tinbergen Institute

A fantastic book bringing together financial theory, sound econometrics, thorough data processing and powerful programming techniques using R. An absolute must for every student and scholar in empirical finance.

Nikolaus Hautsch

Professor of Finance & Statistics at University of Vienna

Tidy Finance is a fantastic resource that lowers the threshold for entry into empirical finance, all in the spirit of open and reproducible science.

Björn Hagströmer

Professor of Finance at Stockholm Business School

To have a deep understanding of empirical asset pricing, one needs to write code using actual data. To learn how to do this, there is no better starting point than Tidy Finance. [...] I strongly recommend Tidy Finance to both beginners and experts.

Raman Uppal

Professor of Finance at EDHEC Business School

Students and professionals alike are led step by step until they suddenly find themselves coding on their own. A brilliant and required resource!

Mark Salmon

Professor of Economics at University of Cambridge

Who maintains this website

Christoph Scheuch

Head of Artificial Intelligence at wikifolio.com

Stefan Voigt

Assistant Professor of Finance at University of Copenhagen

Patrick Weiss

Assistant Professor of Finance at Reykjavik University

Christoph Frey

Quantitative Researcher at Pinechip Capital

• Bridging Theory and Empirical Research in Finance
• Finance Theory Group

Booth School of Business, University of Chicago

The goal of this special FTG conference, hosted by the University of Chicago Booth School of Business, is to bridge theory and empirical research in finance by bringing together theorists and empirical researchers working on corporate finance, financial intermediation, and financial markets. It is part of a broader initiative undertaken by the FTG to foster closer ties and interactions between theoretical and empirical finance research. The conference welcomes submissions of theoretical papers that provide empirically testable hypotheses, empirical papers that are guided by theory, as well as papers that integrate theory models and empirical tests.  The conference welcomes papers broadly related to corporate finance, corporate governance and control, financial intermediation, agency and delegation, information and securities, and financial markets.

Papers that are primarily theoretical (empirical) will be discussed by empirically (theoretically) oriented researchers.  The conference will also feature a panel discussion on integrating theory and empirical research in finance with the following panelists:  Philip Bond (University of Washington), Peter DeMarzo (Stanford University), John Graham (Duke University), and Michael Roberts (University of Pennsylvania).  The paper presenters are expected to attend all sessions of the conference.

Friday, December 9, 2022 : All sessions take place in room 601

2:30 -3:15        Registration (Kapani Lounge, 6th floor)

3:15-4:15         Financial Resilience in Labor Negotiations

Presenter: Alessio Piccolo, Indiana University

Discussant: Effi Benmelech , Northwestern University

4:15-4:30         break (Kapani Lounge, 6th floor)

4:30-5:30         Debt Maturity Choice and Aggregate Growth

Presenter: Jacob Sagi, University of North Carolina at Chapel Hill

Discussant: Mitchell Peterson, Northwestern University

5:30 - 6:30       Reception (City Room, 7th floor)

(Poster session starts at 5:55 in the same room)

6:30 - 8:30       Dinner including keynote address (City Room, 7th floor)

Saturday, December 10, 2022 : All sessions take place in room 601

8:00-8:30         Breakfast (Kapani Lounge, 6th floor)

8:30-9:30         A Theory of Fair CEO Pay

Presenter: Alex Edmans, London Business School

Discussant: Dirk Jenter, LSE

9:30-10:30       Strategic Learning and Corporate Investment

Presenter: Paul Décaire, Arizona State University

Discussant: Christian Opp, University of Rochester

10:30-10:45     Break (Kapani Lounge, 6th floor)

10:45-12:15     Panel Discussion

Philip Bond, Foster School of Business, University of Washington Peter DeMarzo, Stanford Graduate School of Business John Graham, Duke University Fuqua School of Business Michael Roberts, The Wharton School, University of Pennsylvania

Moderator: Wei Jiang, Emory University, Goizueta Business School

12:15- 1:15      Lunch (Room 504)

1:15-2:15         Bank Loan Markups and Adverse Selection

Presenter: Gregory Weitzner, McGill University

Discussant: Uday Rajan, University of Michigan

2:15-2:30         break (Kapani Lounge, 6th floor)

2:30-3:30         The Shadow Cost of Collateral

Presenter: Kairong Xiao, Columbia University

Discussant: Martin Oehmke, LSE

Time allocation: (25 present, 20-25 discuss, 10 audience)

Organizers :

Zhiguo He , University of Chicago  

Wei Jiang , Emory University  

Ron Kaniel , University of Rochester  

Nadya Malenko , University of Michigan  

Program committee co-chairs :

Konstantin Milbradt , Northwestern University

Submit Paper

If you are a member, please log in to register. if you are not a member, please complete this form to register.

Thank you. We have registered your selection.

Please complete this form to register

• Staff intranet
• Student intranet

What do you want to do?

Empirical finance, the empirical finance research group aims to produce innovative research that makes a material contribution to improving our understanding of international monetary and financial systems..

Working closely with other research groups, we use state-of-the-art econometrics to analyse issues with crucial business or policy implications, providing assistance to policy makers and market participants.

Research in this area includes:

• Financial econometrics
• Risk modelling
• Micro finance
• International finance
• Computational finance

The 2024 RCEA International Conference in Economics, Econometrics, and Finance

The Rimini Centre for Economic Analysis (RCEA), joint with the Department of Economics and Finance at Brunel University London invites papers to be considered for the 2024 RCEA International Conference in Economics, Econometrics, and Finance (ICEEF2024).

Group members:

• Skip to main content
• Skip to primary sidebar
• Skip to footer
• QuestionPro

• Solutions Industries Gaming Automotive Sports and events Education Government Travel & Hospitality Financial Services Healthcare Cannabis Technology Use Case NPS+ Communities Audience Contactless surveys Mobile LivePolls Member Experience GDPR Positive People Science 360 Feedback Surveys
• Resources Blog eBooks Survey Templates Case Studies Training Help center

Home Market Research

Empirical Research: Definition, Methods, Types and Examples

Content Index

Empirical research: Definition

Empirical research: origin, quantitative research methods, qualitative research methods, steps for conducting empirical research, empirical research methodology cycle, advantages of empirical research, disadvantages of empirical research, why is there a need for empirical research.

Empirical research is defined as any research where conclusions of the study is strictly drawn from concretely empirical evidence, and therefore “verifiable” evidence.

This empirical evidence can be gathered using quantitative market research and  qualitative market research  methods.

For example: A research is being conducted to find out if listening to happy music in the workplace while working may promote creativity? An experiment is conducted by using a music website survey on a set of audience who are exposed to happy music and another set who are not listening to music at all, and the subjects are then observed. The results derived from such a research will give empirical evidence if it does promote creativity or not.

LEARN ABOUT: Behavioral Research

You must have heard the quote” I will not believe it unless I see it”. This came from the ancient empiricists, a fundamental understanding that powered the emergence of medieval science during the renaissance period and laid the foundation of modern science, as we know it today. The word itself has its roots in greek. It is derived from the greek word empeirikos which means “experienced”.

In today’s world, the word empirical refers to collection of data using evidence that is collected through observation or experience or by using calibrated scientific instruments. All of the above origins have one thing in common which is dependence of observation and experiments to collect data and test them to come up with conclusions.

LEARN ABOUT: Causal Research

Types and methodologies of empirical research

Empirical research can be conducted and analysed using qualitative or quantitative methods.

• Quantitative research : Quantitative research methods are used to gather information through numerical data. It is used to quantify opinions, behaviors or other defined variables . These are predetermined and are in a more structured format. Some of the commonly used methods are survey, longitudinal studies, polls, etc
• Qualitative research:   Qualitative research methods are used to gather non numerical data.  It is used to find meanings, opinions, or the underlying reasons from its subjects. These methods are unstructured or semi structured. The sample size for such a research is usually small and it is a conversational type of method to provide more insight or in-depth information about the problem Some of the most popular forms of methods are focus groups, experiments, interviews, etc.

Data collected from these will need to be analysed. Empirical evidence can also be analysed either quantitatively and qualitatively. Using this, the researcher can answer empirical questions which have to be clearly defined and answerable with the findings he has got. The type of research design used will vary depending on the field in which it is going to be used. Many of them might choose to do a collective research involving quantitative and qualitative method to better answer questions which cannot be studied in a laboratory setting.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

Quantitative research methods aid in analyzing the empirical evidence gathered. By using these a researcher can find out if his hypothesis is supported or not.

• Survey research: Survey research generally involves a large audience to collect a large amount of data. This is a quantitative method having a predetermined set of closed questions which are pretty easy to answer. Because of the simplicity of such a method, high responses are achieved. It is one of the most commonly used methods for all kinds of research in today’s world.

Previously, surveys were taken face to face only with maybe a recorder. However, with advancement in technology and for ease, new mediums such as emails , or social media have emerged.

For example: Depletion of energy resources is a growing concern and hence there is a need for awareness about renewable energy. According to recent studies, fossil fuels still account for around 80% of energy consumption in the United States. Even though there is a rise in the use of green energy every year, there are certain parameters because of which the general population is still not opting for green energy. In order to understand why, a survey can be conducted to gather opinions of the general population about green energy and the factors that influence their choice of switching to renewable energy. Such a survey can help institutions or governing bodies to promote appropriate awareness and incentive schemes to push the use of greener energy.

Learn more: Renewable Energy Survey Template Descriptive Research vs Correlational Research

• Experimental research: In experimental research , an experiment is set up and a hypothesis is tested by creating a situation in which one of the variable is manipulated. This is also used to check cause and effect. It is tested to see what happens to the independent variable if the other one is removed or altered. The process for such a method is usually proposing a hypothesis, experimenting on it, analyzing the findings and reporting the findings to understand if it supports the theory or not.

For example: A particular product company is trying to find what is the reason for them to not be able to capture the market. So the organisation makes changes in each one of the processes like manufacturing, marketing, sales and operations. Through the experiment they understand that sales training directly impacts the market coverage for their product. If the person is trained well, then the product will have better coverage.

• Correlational research: Correlational research is used to find relation between two set of variables . Regression analysis is generally used to predict outcomes of such a method. It can be positive, negative or neutral correlation.

LEARN ABOUT: Level of Analysis

For example: Higher educated individuals will get higher paying jobs. This means higher education enables the individual to high paying job and less education will lead to lower paying jobs.

• Longitudinal study: Longitudinal study is used to understand the traits or behavior of a subject under observation after repeatedly testing the subject over a period of time. Data collected from such a method can be qualitative or quantitative in nature.

For example: A research to find out benefits of exercise. The target is asked to exercise everyday for a particular period of time and the results show higher endurance, stamina, and muscle growth. This supports the fact that exercise benefits an individual body.

• Cross sectional: Cross sectional study is an observational type of method, in which a set of audience is observed at a given point in time. In this type, the set of people are chosen in a fashion which depicts similarity in all the variables except the one which is being researched. This type does not enable the researcher to establish a cause and effect relationship as it is not observed for a continuous time period. It is majorly used by healthcare sector or the retail industry.

For example: A medical study to find the prevalence of under-nutrition disorders in kids of a given population. This will involve looking at a wide range of parameters like age, ethnicity, location, incomes  and social backgrounds. If a significant number of kids coming from poor families show under-nutrition disorders, the researcher can further investigate into it. Usually a cross sectional study is followed by a longitudinal study to find out the exact reason.

• Causal-Comparative research : This method is based on comparison. It is mainly used to find out cause-effect relationship between two variables or even multiple variables.

For example: A researcher measured the productivity of employees in a company which gave breaks to the employees during work and compared that to the employees of the company which did not give breaks at all.

LEARN ABOUT: Action Research

Some research questions need to be analysed qualitatively, as quantitative methods are not applicable there. In many cases, in-depth information is needed or a researcher may need to observe a target audience behavior, hence the results needed are in a descriptive analysis form. Qualitative research results will be descriptive rather than predictive. It enables the researcher to build or support theories for future potential quantitative research. In such a situation qualitative research methods are used to derive a conclusion to support the theory or hypothesis being studied.

LEARN ABOUT: Qualitative Interview

• Case study: Case study method is used to find more information through carefully analyzing existing cases. It is very often used for business research or to gather empirical evidence for investigation purpose. It is a method to investigate a problem within its real life context through existing cases. The researcher has to carefully analyse making sure the parameter and variables in the existing case are the same as to the case that is being investigated. Using the findings from the case study, conclusions can be drawn regarding the topic that is being studied.

For example: A report mentioning the solution provided by a company to its client. The challenges they faced during initiation and deployment, the findings of the case and solutions they offered for the problems. Such case studies are used by most companies as it forms an empirical evidence for the company to promote in order to get more business.

• Observational method:   Observational method is a process to observe and gather data from its target. Since it is a qualitative method it is time consuming and very personal. It can be said that observational research method is a part of ethnographic research which is also used to gather empirical evidence. This is usually a qualitative form of research, however in some cases it can be quantitative as well depending on what is being studied.

For example: setting up a research to observe a particular animal in the rain-forests of amazon. Such a research usually take a lot of time as observation has to be done for a set amount of time to study patterns or behavior of the subject. Another example used widely nowadays is to observe people shopping in a mall to figure out buying behavior of consumers.

• One-on-one interview: Such a method is purely qualitative and one of the most widely used. The reason being it enables a researcher get precise meaningful data if the right questions are asked. It is a conversational method where in-depth data can be gathered depending on where the conversation leads.

For example: A one-on-one interview with the finance minister to gather data on financial policies of the country and its implications on the public.

• Focus groups: Focus groups are used when a researcher wants to find answers to why, what and how questions. A small group is generally chosen for such a method and it is not necessary to interact with the group in person. A moderator is generally needed in case the group is being addressed in person. This is widely used by product companies to collect data about their brands and the product.

For example: A mobile phone manufacturer wanting to have a feedback on the dimensions of one of their models which is yet to be launched. Such studies help the company meet the demand of the customer and position their model appropriately in the market.

• Text analysis: Text analysis method is a little new compared to the other types. Such a method is used to analyse social life by going through images or words used by the individual. In today’s world, with social media playing a major part of everyone’s life, such a method enables the research to follow the pattern that relates to his study.

For example: A lot of companies ask for feedback from the customer in detail mentioning how satisfied are they with their customer support team. Such data enables the researcher to take appropriate decisions to make their support team better.

Sometimes a combination of the methods is also needed for some questions that cannot be answered using only one type of method especially when a researcher needs to gain a complete understanding of complex subject matter.

We recently published a blog that talks about examples of qualitative data in education ; why don’t you check it out for more ideas?

Since empirical research is based on observation and capturing experiences, it is important to plan the steps to conduct the experiment and how to analyse it. This will enable the researcher to resolve problems or obstacles which can occur during the experiment.

Step #1: Define the purpose of the research

This is the step where the researcher has to answer questions like what exactly do I want to find out? What is the problem statement? Are there any issues in terms of the availability of knowledge, data, time or resources. Will this research be more beneficial than what it will cost.

Before going ahead, a researcher has to clearly define his purpose for the research and set up a plan to carry out further tasks.

Step #2 : Supporting theories and relevant literature

The researcher needs to find out if there are theories which can be linked to his research problem . He has to figure out if any theory can help him support his findings. All kind of relevant literature will help the researcher to find if there are others who have researched this before, or what are the problems faced during this research. The researcher will also have to set up assumptions and also find out if there is any history regarding his research problem

Step #3: Creation of Hypothesis and measurement

Before beginning the actual research he needs to provide himself a working hypothesis or guess what will be the probable result. Researcher has to set up variables, decide the environment for the research and find out how can he relate between the variables.

Researcher will also need to define the units of measurements, tolerable degree for errors, and find out if the measurement chosen will be acceptable by others.

Step #4: Methodology, research design and data collection

In this step, the researcher has to define a strategy for conducting his research. He has to set up experiments to collect data which will enable him to propose the hypothesis. The researcher will decide whether he will need experimental or non experimental method for conducting the research. The type of research design will vary depending on the field in which the research is being conducted. Last but not the least, the researcher will have to find out parameters that will affect the validity of the research design. Data collection will need to be done by choosing appropriate samples depending on the research question. To carry out the research, he can use one of the many sampling techniques. Once data collection is complete, researcher will have empirical data which needs to be analysed.

LEARN ABOUT: Best Data Collection Tools

Step #5: Data Analysis and result

Data analysis can be done in two ways, qualitatively and quantitatively. Researcher will need to find out what qualitative method or quantitative method will be needed or will he need a combination of both. Depending on the unit of analysis of his data, he will know if his hypothesis is supported or rejected. Analyzing this data is the most important part to support his hypothesis.

Step #6: Conclusion

A report will need to be made with the findings of the research. The researcher can give the theories and literature that support his research. He can make suggestions or recommendations for further research on his topic.

A.D. de Groot, a famous dutch psychologist and a chess expert conducted some of the most notable experiments using chess in the 1940’s. During his study, he came up with a cycle which is consistent and now widely used to conduct empirical research. It consists of 5 phases with each phase being as important as the next one. The empirical cycle captures the process of coming up with hypothesis about how certain subjects work or behave and then testing these hypothesis against empirical data in a systematic and rigorous approach. It can be said that it characterizes the deductive approach to science. Following is the empirical cycle.

• Observation: At this phase an idea is sparked for proposing a hypothesis. During this phase empirical data is gathered using observation. For example: a particular species of flower bloom in a different color only during a specific season.
• Induction: Inductive reasoning is then carried out to form a general conclusion from the data gathered through observation. For example: As stated above it is observed that the species of flower blooms in a different color during a specific season. A researcher may ask a question “does the temperature in the season cause the color change in the flower?” He can assume that is the case, however it is a mere conjecture and hence an experiment needs to be set up to support this hypothesis. So he tags a few set of flowers kept at a different temperature and observes if they still change the color?
• Deduction: This phase helps the researcher to deduce a conclusion out of his experiment. This has to be based on logic and rationality to come up with specific unbiased results.For example: In the experiment, if the tagged flowers in a different temperature environment do not change the color then it can be concluded that temperature plays a role in changing the color of the bloom.
• Testing: This phase involves the researcher to return to empirical methods to put his hypothesis to the test. The researcher now needs to make sense of his data and hence needs to use statistical analysis plans to determine the temperature and bloom color relationship. If the researcher finds out that most flowers bloom a different color when exposed to the certain temperature and the others do not when the temperature is different, he has found support to his hypothesis. Please note this not proof but just a support to his hypothesis.
• Evaluation: This phase is generally forgotten by most but is an important one to keep gaining knowledge. During this phase the researcher puts forth the data he has collected, the support argument and his conclusion. The researcher also states the limitations for the experiment and his hypothesis and suggests tips for others to pick it up and continue a more in-depth research for others in the future. LEARN MORE: Population vs Sample

LEARN MORE: Population vs Sample

There is a reason why empirical research is one of the most widely used method. There are a few advantages associated with it. Following are a few of them.

• It is used to authenticate traditional research through various experiments and observations.
• This research methodology makes the research being conducted more competent and authentic.
• It enables a researcher understand the dynamic changes that can happen and change his strategy accordingly.
• The level of control in such a research is high so the researcher can control multiple variables.
• It plays a vital role in increasing internal validity .

Even though empirical research makes the research more competent and authentic, it does have a few disadvantages. Following are a few of them.

• Such a research needs patience as it can be very time consuming. The researcher has to collect data from multiple sources and the parameters involved are quite a few, which will lead to a time consuming research.
• Most of the time, a researcher will need to conduct research at different locations or in different environments, this can lead to an expensive affair.
• There are a few rules in which experiments can be performed and hence permissions are needed. Many a times, it is very difficult to get certain permissions to carry out different methods of this research.
• Collection of data can be a problem sometimes, as it has to be collected from a variety of sources through different methods.

LEARN ABOUT:  Social Communication Questionnaire

Empirical research is important in today’s world because most people believe in something only that they can see, hear or experience. It is used to validate multiple hypothesis and increase human knowledge and continue doing it to keep advancing in various fields.

For example: Pharmaceutical companies use empirical research to try out a specific drug on controlled groups or random groups to study the effect and cause. This way, they prove certain theories they had proposed for the specific drug. Such research is very important as sometimes it can lead to finding a cure for a disease that has existed for many years. It is useful in science and many other fields like history, social sciences, business, etc.

LEARN ABOUT: 12 Best Tools for Researchers

With the advancement in today’s world, empirical research has become critical and a norm in many fields to support their hypothesis and gain more knowledge. The methods mentioned above are very useful for carrying out such research. However, a number of new methods will keep coming up as the nature of new investigative questions keeps getting unique or changing.

Create a single source of real data with a built-for-insights platform. Store past data, add nuggets of insights, and import research data from various sources into a CRM for insights. Build on ever-growing research with a real-time dashboard in a unified research management platform to turn insights into knowledge.

LEARN MORE         FREE TRIAL

MORE LIKE THIS

Top 10 Employee Development Software for Talent Growth

Apr 3, 2024

Top 5 Insight Community Platforms to Elevate Your Research

Choose The Right Concept Testing Platform to Boost Your Ideas

Apr 2, 2024

Top 15 NPS Software for Customer Feedback in 2024

Other categories.

• Academic Research
• Artificial Intelligence
• Assessments
• Brand Awareness
• Case Studies
• Communities
• Consumer Insights
• Customer effort score
• Customer Engagement
• Customer Experience
• Customer Loyalty
• Customer Research
• Customer Satisfaction
• Employee Benefits
• Employee Engagement
• Employee Retention
• Friday Five
• General Data Protection Regulation
• Insights Hub
• Life@QuestionPro
• Market Research
• Mobile diaries
• Mobile Surveys
• New Features
• Online Communities
• Question Types
• Questionnaire
• QuestionPro Products
• Release Notes
• Research Tools and Apps
• Revenue at Risk
• Survey Templates
• Training Tips
• Uncategorized
• Video Learning Series
• What’s Coming Up
• Workforce Intelligence

Empirical Research in Banking and Corporate Finance: Volume 21

Table of contents, the value of corporate culture: relationships among values, industry, and performance.

Using corporate value statements of the top Fortune 300 firms for the year 2012, we examine relationships among the stated values of these companies, their industries, and their Corporate Social Responsibility (CSR) performance measures. We classify stated values into 21 broad categories. We find that corporate values exhibit strong industry affiliations. Correspondence analysis and regression models indicate that 19 out of 21 values are related to at least one performance measure and while some values are associated with improved performance (e.g., ethics), others (e.g., safety) have a negative impact. Further, while some values have the anticipated impact on performance (e.g., the shareholder value is positively associated with financial performance), some show no relationship (e.g., the environment value is not associated with environmental performance). Finally, our findings also suggest possible CSR washing in some cases. Overall, the study finds corporate values do affect their performance.

Management Capability and Innovation

This chapter investigates whether core competence of managers and their expansive (vs. specialized) managerial style affects firms' innovative ability, capacity, and efficiency. Using exogenous CEO departures as a natural experiment, it establishes a causal link between managerial capability and innovation. Importantly, it reveals that firms with talented managers receive significantly more nonself citations; make significantly lower self-citations and lesser citations to the others, indicating novel and explorative innovation achievements. Also, managers with higher general (specialized) ability are cited more (less) by patents from a wider range of fields. Lastly, career concern is identified as a mechanism linking higher ability and innovation.

State Tax Rate Changes and Leveraged Buyouts: US Evidence

This chapter investigates whether and to what extent tax benefits affect the likelihood of firms undertaking leveraged buyout (LBO) transactions.

Design/Methodology/Approach

With an identified sample of LBO firms and similar non-LBO counterparts, this chapter utilizes staggered changes in state corporate income tax rates as exogenous shocks and adopts a Logistic regression to analyze how these tax changes affect firms' probability of engaging in LBOs.

Firms are more likely to engage in LBOs after increases in corporate income tax rates. Specifically, the increase in the likelihood of firms undertaking LBOs following tax increases is between 6.9% and 12.9%. We also find that this positive relation is more pronounced for firms with higher levels of return on assets (ROA) and marginal tax rates (MTR). Finally, we report that the mean value of tax benefits accounts for between 28.5% and 170% of the premium paid to pre-buyout shareholders.

Originality/Value

This chapter provides strong evidence that tax benefits constitute an important source of value creation in LBOs and adds to the debate regarding the role of tax benefits in LBOs.

Does Effort Pay Off? Evidence From Internal Audit in Israel

The research question we address in this paper is whether the effort invested by the internal auditor in the firm is associated with better firm performance. Our measure of effort is the number of audit hours invested in the firm, and firm performance is measured by the likelihood of a restatement of the firm's financial results. This study is the first to analyze this question, an endeavor made possible by a difference in disclosure requirements regarding internal audit effort between the US and Israel. Our analysis is conducted using hand-collected data on firms traded on Tel Aviv Stock Exchange (TASE) during the period 2010–2014. We expect that auditor effort is negatively associated with the likelihood of restatements of the firm's financial results. Indeed, our findings support this hypothesis. We also consider the association between restatements and two audit committee characteristics – the degree of independence and the degree of expertise of its members. However, these associations are not upheld by the data.

Stock Liquidity and Cost of Private Debt

The paper investigates whether stock liquidity of firms is valued by lending banks revealing that firms with higher liquidity in the capital market pay lower spreads for the loans they obtain. This relationship is causal as evidenced by using the decimalization of tick size as an exogenous shock-to-stock liquidity in a difference-in-differences setting. Reduction in financial constraint and improvement in corporate governance induced by higher stock liquidity are potential mechanisms through which liquidity impacts loan spreads. These higher liquidity firms also receive less stringent nonprice loan terms, for example, longer loan maturity and less required collateral.

The Status of Environmental, Social, and Governance Voluntary Disclosure in the GCC Banking Industry: Does It Pay to Be Socially Responsible?

This research examines the bidirectional relationship between Environmental, Social and Governance (ESG) voluntary disclosure engagement and financial performance of a panel of banks extracted from the Gulf Cooperation Council (GCC) banking industry, covering a period of 11 years (2007–2017). We find that GCC banks, and in particular Islamic banks, voluntarily disclose low level of information related to ESG activities. Using system GMM methodology, we provide evidence that ESG disclosure adversely affects bank performance, regardless of the bank performance measure used. Thus spending on ESG turns out to be costly for GCC banks, a result that is consistent with the agency problem, where managers are likely to reduce long-term expenditures related to ESG actions in order to boost short-term profits. As managers' compensations often relate to short-term financial performance, managers tend to reduce their spending on ESG activities. Furthermore, contrary to previous research, our results indicate that the relationship between ESG and financial performance is bidirectional and dynamic. We also find evidence that ESG disclosure positively affects performance only for well-diversified banks. Finally, although conventional banks disclose significantly more information related to ESG activities, we do not find any significant differences between the two types of banks in the relationship between ESG disclosure and performance. Our suggestion is that these results are consistent with what we call “clientele” and “gravitation” effects, where a customer tends to choose to deal with the bank that reflects his religious beliefs (gravitation effect) and with the bank that provides him with the best services (clientele effect) regardless of its ESG disclosure.

Securitization and Bank Efficiency

We examine whether loan securitization has an impact on bank efficiency. Using a sample of large US commercial banks from 2002 to 2012, we find that bank loan securitization has a significant and positive impact on bank efficiency, and this relationship is stronger for banks with higher capital ratios, higher default risk, and lower level of liquidity and diversification. Our results are robust to Heckman self-selection correction and difference-in-difference (DID) analysis. In addition, these results are found mainly in non-mortgage loan securitizations but not in mortgage loan securitizations. Finally, we show that loan sales also have a positive impact on bank efficiency.

• Stephen P. Ferris
• Anil K. Makhija

We’re listening — tell us what you think

Something didn’t work….

Report bugs here

All feedback is valuable

Please share your general feedback

Join us on our journey

Platform update page.

Visit emeraldpublishing.com/platformupdate to discover the latest news and updates

Questions & More Information

Answers to the most commonly asked questions here

Bridging Theory and Empirical Research in Accounting

Formal theory and empirical research are complementary in building and advancing the body of knowledge in accounting in order to understand real-world phenomena. We offer thoughts on opportunities for empiricists and theorists to collaborate, build on each other’s work, and iterate over models and data to make progress. For empiricists, we see room for more descriptive work, more experimental work on testing formal theories, and more work on quantifying theoretical parameters. For theorists, we see room for theories explicitly tied to descriptive evidence, new theories on individuals’ decision-making in a data-rich world, theories focused on accounting institutions and measurement issues, and richer theories for guiding empirical work and providing practical insights. We also encourage explicitly combining formal theory and empirical models by having both in one paper and by structural estimation.

More Research From These Scholars

How important is corporate governance evidence from machine learning, information versus investment, non-gaap reporting and investment.

• Senior Leadership
• Orange County
• Diversity, Equity and Inclusion
• Distinguished Speakers
• Undergraduate
• Full-Time MBA
• Executive MBA
• Master of Finance
• Master of Innovation and Entrepreneurship
• Master of Professional Accountancy
• Master of Science in Business Analytics
• Master of Science in Biotechnology Management
• Experiences
• Faculty Advisors
• Student Directory
• Leadership Development
• Future Leaders Initiative Program
• Investments, Financial Planning and You
• Digital Learning
• Academic Areas
• Research Abstracts
• Research Colloquia
• Research in Action
• Beall Center for Innovation and Entrepreneurship
• Center for Digital Transformation
• Center for Global Leadership
• Center for Health Care Management and Policy
• Todd and Lisa Halbrook Center for Investment and Wealth Management
• Center for Real Estate
• Faculty Directory
• Centers of Excellence
• Research - Research in Action
• Academic - Accounting
• Academics - Economics and Public Policy
• Academic - Finance
• Academics - Information Systems
• Academics - Interdisciplinary
• Academic - Marketing
• Academic - Operations and Decision Technologies
• Academic - Organization and Management
• Academic - Strategy and Entrepreneurship

Finance covers a broad range of topics including corporate investments and financing decisions, security valuation, portfolio management, and the behavior of asset prices and investors. In addition to providing students with wide base of knowledge, Finance at Merage helps students develop new skills to unlock the potential of finance in the digital world. To support this broad objective, the members of the finance faculty are renowned for their expertise in investments, money management, behavioral finance, risk management and financial economic theory.

The Finance faculty is committed to excellence in teaching. The Finance Area offers courses in the undergraduate, MBA, Master of Finance (MFin) and PhD programs. These courses offer broad exposure to finance theory and reflect latest market practices and research findings. The School’s Bachelor of Arts in Business Administration includes basic courses in financial management and investments as well as elective courses. The Merage School’s MBA programs include the core course in financial management, which reflects the School’s digitally-driven focus, as well as elective courses. The School also offers the Master of Finance (MFin) degree that comprises a wide range of graduate finance courses. The PhD program offers an advanced, research oriented degree in financial economics and prepares students for successful academic careers.

The Merage School Finance Area frequently hosts Finance Colloquia .

Nai-fu Chen Professor Emeritus Research Interests: Stability of currency and banking systems, Macroeconomic impact on investing, GDP growth, inflation, interest rates, credit risk and the financial market, Hedge funds: asset allocations and portfolio management

David Hirshleifer Distinguished Professor Emeritus Research Interests: Psychology, Social interactions, and Markets, Investments, Corporate finance, Risk management

Chong Huang Associate Professor

Michael Imerman Assistant Professor of Teaching Research Interests: Credit Risk, Banking, FinTech, Financial Data Science

Philippe Jorion Dean's Professor Research Interests: Empirical research in investments, Managing financial risks, Global portfolio investments, Derivatives markets

Byungwook Kim Assistant Professor Research Interests: Asset pricing, Investor behavior and asset management (ETFs, mutual funds, hedge funds).

Todd Richey Continuing Lecturer Research Interests: Corporate Finance, Corporate Strategy, M&A, Leveraged Buyouts, Private Equity, Corporate Restructurings, Debt/Equity Securities

Christopher Schwarz Professor Research Interests: Hedge Funds, Mutual Funds, Investments, Regulation and Money Management

Jinfei Sheng Assistant Professor Research Interests: Empirical Asset Pricing, FinTech, Labor Finance, Financial Intermediation

Zheng Sun Professor Research Interests: Empirical Asset Pricing, Investments, Market Microstructure, Banking

Yuhai Xuan Dean's Professor and Associate Dean of Masters Programs Research Interests: Corporate Finance, Corporate Governance and Behavioral Finance

Lu Zheng Professor

• Blake Clayton , Lecturer
• Lingwen Huang , Continuing Lecturer
• Yanbo Jin , Lecturer
• Sean Klein , Lecturer
• Laura Liu , Lecturer
• David Ochi , Lecturer
• Fredrick Park , Lecturer
• Todd Richey , Continuing Lecturer
• Edward Sheng , Lecturer

U ndergraduate business classes may be found through the  UCI course catalogue .

MBA Core Course Description

209A. Managerial Finance The course introduces students to the key finance concepts and theories that apply generally in making financial decisions such as time value of money, risk and return trade off. It also covers main stream methodologies used by CFOs when making capital budgeting and capital structure decisions. Specifically, net present value and the discounted cash flow analysis are the main methodologies the course focuses on. Through a combination of lectures, cases, and in-class mini cases/exercises, the students learn how to apply these methodologies to businesses that operate in both traditional and technology-driven industries.

In a disruptive industry where technology is rapidly changing, the valuation models need to become more dynamic. Upon building a solid understanding of the fundamental methodologies, the course will survey extensions of the basic methodologies and alternative valuation models. It will also discuss how strategic issues interact with finance. Through these discussions, the students will form a dynamic view in project and corporate valuations.

MBA Elective Descriptions

Students will learn about the strengths and limitations of computer-based portfolio optimization, as the role of technology in the investment management business has become widespread. The course will examine how to use data to evaluate asset pricing models. Students will also explore how innovations and technologies based on modern finance ideas, such as index funds, smart beta, and other quantitative investment strategies, have disrupted traditional investment management. These innovations have enabled investment managers to manage large sums of money with less people, compared to the traditional "stock picker" approach.

244. Multinational Finance This course provides comprehensive and systematic coverage of topics in multinational finance, enabling the leaders of tomorrow’s multinational enterprises to optimally make financial decisions in an international setting. We will examine the history of the world’s foreign exchange markets, the macroeconomics of foreign exchange rate, major parity conditions, and hedging of currency exposure. We will also discuss how currency exchange and risk management could be affected by the disruptive innovations, such as block chain. The scope and content of international finance have been fast evolving due to deregulation of financial markets, product innovations, and technological advancement. As capital markets of the world are becoming more integrated, an important goal of this course is to help students to understand the uncertainty of international finance and the interdependence of economies and markets over the world.

248. Corporate Valuation This course focuses on estimating the value of firms and projects in diverse settings, paying special attention to the digitalization and globalization of the economy. Valuation is an essential aspect of most corporate and investment decisions such as financing policies, acquisitions, project development, security valuation and so on. The course will re-enforce and expand on concepts covered in the introductory finance course. It will discuss the main valuation methods and their applications in real world problems. These situations were chosen to enhance students’ understanding of the value and limitations of finance theory and implementation of the valuation methods.

The course will use a combination of lectures and cases designed to confront students with a variety of real-world problems. It will discuss the cost of capital for new technology, valuation of technology firms, and valuation of emerging market companies. The course also provides ample opportunity to enhance and develop students’ problem solving and communications skills. Such knowledge is essential for students with an interest in Corporate Finance, Investment Banking, Financial Consulting, as well as Investments.

In today’s world where technology is disruptive, companies need to make decisions dynamically. The course will also introduce some principles of real options which will provide useful tools when companies make investment decisions taking into account of options to expand, reduce and remove.

253. Venture Capital & Private Equity & Finance of Innovation Venture Capital (VC) and Private Equity (PE) constitute the so-called “alternative” asset class as opposed to conventional investments in stocks, bonds and commodities. Many start-ups and entrepreneurial firms require substantial capital. Bank loans and other conventional debt financings are unlikely due to high risk and uncertain prospects. Venture Capital and Private Equity organizations finance these high-risk but potentially high reward companies. In this course, through a combination of lectures and case studies, students learn how VCs and PE firms raise funds, evaluate the potential opportunities and make investment decisions.

Almost all start-ups initially have negative cash flows. Further, these digital platform based companies keep their profit margins intentionally low, so as to gain market share and/or disrupt existing players and markets. This further compounds the negative cash flows. Often cash flows remain negative year-in year-out. Traditional valuation models such as discounted cash flows (DCFs) do not properly address these issues and evaluate the true potential of these businesses. In this course, we will explore the types of models VC firms use to analyze and value these businesses. We will also explore the leveraged buyout (LBO) models and how PE firms use mezzanine financings and use of warrants to make deals and investment decisions.

FinTech, the application of information technology to finance, is fast-growing and rapidly changing the landscape of financial industries. The purpose of this course is to provide students with a working knowledge to understand economic mechanisms in many areas of FinTech and cutting-edge skills to identify and analyze important issues in this subject. The course balances qualitative analysis and quantitative applications. You will gain experience in applying empirical tools through assignments and a group project. Topics covered in the course include a wide range of FinTech phenomena, such as Bitcoin, Blockchain, Crowdfunding, peer-to-peer lending, robo-advisor, and their economic and social impacts. Overall, this course aims to equip students with the skills and knowledge required to help their professional careers in the digital age.

MFin Course Descriptions

211. ProSeminar This course, Coordinated with the Merage Career Center, provides students with practical skills for success in the program and future careers in finance by teaching career planning, job search tactics and strategies, resume building, interviewing preparation, approaches to networking, salary negotiation, public speaking, and business writing skills.

203A. Financial Reporting Involves the development, analysis, and interpretation of financial accounting information for external reporting purposes.

210. Foundations of Finance This course teaches the foundations of finance. The course begins by teaching financial tools such as time value of money, capital budgeting techniques, and cash flow analysis. The course then examines the foundations of investment management. Theory and empirical evidence related to portfolio theory, market efficiency, asset pricing models, factor models, and option pricing theory.

240. Financial Research Methodology This course bridges the gap between theoretical financial models and financial econometrics and empirics. The course will explore the strengths and weaknesses of empirical finance and econometrics, as well as implement many of the methods based on data the individual has drawn from the market.

249. Derivatives Derivatives are new financial instruments whose value “derives” from the values of other, more basic, underlying assets such as stocks and bonds. The course will cover major derivative instruments including forward, futures, swaps and options. Derivatives are widely used for hedging, speculation, and arbitrage. The size of this market is several times larger than the global economy. The focus of the course is on understanding the instruments, how to use them, how to price them, and basic risk management principles. In particular, the course will show how to implement investment strategies with derivatives. Prerequisite:  MF 210

296. Capstone Course Students work in teams on an applied finance project, one that is associated with an internship or experiential project. This project should be associated with the area of focus of the student’s electives, as it will illustrate the student’s knowledge and expertise, which students can reference when speaking to potential employers.

PhD Course Descriptions

291-FN1. Advanced Topics in Corporate Finance  (4 units) The course helps students understand and develop their skills in analyzing, firms’ investment and financing decisions. It covers research topics in corporate finance to illustrate basic principles and to give students practice developing these skills. It examines a variety of topics, such as optimal debt and equity financing, managerial and firm reputation and investment decisions, motives for financial signaling, conflicts of interest and informational differences between managers, debtholders and equity-holders; the takeover process; the use of capital structure and compensation to strategically position the firm in product markets; and the significance of imperfect rationality for corporate policy. The style is a mixture of lecture and discussion. 

291-FN2. Advanced Topics in Investments  (4 units) The goal of this course is to give critical perspectives on the some of the most challenging problems related to investments, the problems that are most relevant for both academics and the applied world. It covers a variety of topics such as the basic asset pricing test framework, the common methodologies used in empirical asset pricing tests, empirical equity market regularities, and new directions in empirical finance. The idea of the course is not to go through in detail all of the assigned reading. It will be assumed that students have read each of the articles. The classroom discussion will be broad and will not necessarily focus on any technical details in the readings. We are more interested in extracting the big picture and what it means for the future of finance research.

291-FN3. Behavioral Finance  (4 units) Building upon traditional theories of investment choices and asset market equilibrium, this course examines how the psychology of investors and managers affects financing and investment choices. It reviews evidence from psychology that may be relevant for finance, relevant evidence from financial markets, and recent approaches to modeling how imperfect rationality affects financial markets. It covers recent research on how psychology influences the preferences and information processing of investors; how imperfect rationality and arbitrage interact to determine equilibrium asset prices; how firms can take advantage of market inefficiencies; and how psychological bias affects the behavior of managers. Our emphasis will be on approaches that reflect evidence or insight from the explicit study of human psychology. The course provides a preparation to take advantage of the rich set of opportunities for future research in this field.

291-FN4. Finance Research Methodology    (4 units) This course offers a survey of recent advances in finance research methodologies. In particular, we will discuss the pros and cons of the different approaches and the motivations behind their model designs.

291-FN5. Current Topics in Finance (2 units) Description: This course discusses recent advances in the finance literature. Participants are expected to: (1) Write summaries of papers covered in the class; (2) Present one paper; (3) Discuss one paper; (4) Develop a research proposal on an original research idea.

297V. Information, Psychology, and Social Processes In the marketplace for ideas, which succeed and which fail? A growing field of research studies how ideas and information spread between individuals, and how populations of ideas evolve. This course reviews recent research in this area and applications to business. A few examples give a hint of the range of applications of these ideas: viral marketing, information cascades and herd behavior in consumer behavior and in corporate strategy, the spread of behaviors across firms through social networks such as interlocking boards of directors, and what determines the success of innovative business methods.

• Follow us on LinkedIn
• Follow us on Facebook
• Follow us on Instagram
• Follow us on Twitter
• Follow us on YouTube
• Follow us on Flickr

• UCI COVID-19
• UCI Homepage
• UCI Outlook
• Privacy Policy
• Staff Directory
• Merage Student Association
• Merage School Store

• SUGGESTED TOPICS
• The Magazine
• Newsletters
• Managing Yourself
• Managing Teams
• Work-life Balance
• The Big Idea
• Data & Visuals
• Reading Lists
• Case Selections
• HBR Learning
• Topic Feeds
• Account Settings
• Email Preferences

Research: How Different Fields Are Using GenAI to Redefine Roles

• Maryam Alavi

Examples from customer support, management consulting, professional writing, legal analysis, and software and technology.

The interactive, conversational, analytical, and generative features of GenAI offer support for creativity, problem-solving, and processing and digestion of large bodies of information. Therefore, these features can act as cognitive resources for knowledge workers. Moreover, the capabilities of GenAI can mitigate various hindrances to effective performance that knowledge workers may encounter in their jobs, including time pressure, gaps in knowledge and skills, and negative feelings (such as boredom stemming from repetitive tasks or frustration arising from interactions with dissatisfied customers). Empirical research and field observations have already begun to reveal the value of GenAI capabilities and their potential for job crafting.

There is an expectation that implementing new and emerging Generative AI (GenAI) tools enhances the effectiveness and competitiveness of organizations. This belief is evidenced by current and planned investments in GenAI tools, especially by firms in knowledge-intensive industries such as finance, healthcare, and entertainment, among others. According to forecasts, enterprise spending on GenAI will increase by two-fold in 2024 and grow to $151.1 billion by 2027 .

• Maryam Alavi is the Elizabeth D. & Thomas M. Holder Chair & Professor of IT Management, Scheller College of Business, Georgia Institute of Technology .

Partner Center

Kellogg School of Management at Northwestern University

Finance & Accounting Apr 1, 2024

The hedge fund in your pantry, many households utilize excess cash to support shopping habits that generate high financial returns..

Scott R. Baker

Stephanie Johnson

Lorenz Kueng

If you had an extra $500 in your checking account, what would be a rational thing to do with it? All things being equal, many experts would recommend investing it in the stock market, where the average annual return of 10 percent handily beats the pitiful interest rates offered by most retail savings accounts. But few households actually do this —especially lower-income ones. Are they acting irrationally?

Hardly, says Scott Baker , an associate professor of finance at the Kellogg School. New research from Baker and his collaborators Stephanie Johnson and Lorenz Kueng (of Rice University and the Swiss Finance Institute, respectively) shows that many households “invest” surplus cash in an inventory of common household consumables—everything from canned goods and breakfast cereal to boxes of tissue and rolls of toilet paper.

By managing inventory in savvy ways, these households actually realize “returns” on their investment (in the form of cost savings) exceeding 20 percent, and sometimes as high as 50 percent. That’s competitive with top-performing hedge funds. In other words, when a household is able to put a dollar toward the cheaper cereal from the big-box store today, it can save them a lot compared with buying it at the corner store for a higher price next month.

Obviously, household inventory management won’t yield the kind of real returns that let people purchase yachts or endow universities. But according to Baker, such inventory does form a significant—and, until now, mostly invisible—“asset class” that contributes to a household’s measurable wealth.

“It’s not [worth] tremendous amounts of money—we estimate on the order of $1,000 or so,” Baker says. “But for many of these households, this is a very substantial chunk relative to their observable financial assets. So even if they’re not investing in stocks, they are acting in quite a sophisticated way to obtain high returns to maximize their consumption.”

Invisible trade-offs

The researchers began by examining a household’s total financial assets in the same way they would look at a firm’s assets. Much like a company relies on working capital—not just revenue and investments—to maintain its solvency, ordinary households have their own version. Baker and his colleagues defined this “household working capital” as the combination of liquid assets and inventories of consumer goods.

The former is straightforward enough to measure on a balance sheet, just like a firm’s. “It’s cash on hand—a buffer in my bank account such that if I go to the store and find some really good deals, or [decide] it would make sense to stockpile some stuff, I can do that,” Baker says.

“It moves the needle more for households that don’t have a lot of financial wealth.” — Scott Baker

However, all that stuff that people keep in their pantries and medicine cabinets—the consumer goods held as household inventory—isn’t as easy to observe as cash. “For firms, how much inventory they’re holding gets [reported] because it’s a big component of value. But for households, it’s been kind of invisible,” Baker says. “People don’t necessarily know. And it’s hard to say in surveys, ‘Go through your pantry and list everything.’”

Baker and his colleagues created estimates of household inventory by using Nielsen data, which tracks the purchasing patterns of roughly 60,000 households—“grocery items, pharmacy items, basically most things with a barcode,” he says. This data only directly measures what people purchase, not what happens to it after that. But by aggregating these consumption patterns over time, and by matching them against other financial data like household income, the researchers could construct a reasonable picture of which kinds of products were “invested” in as inventory.

“It’s not that households consume the same exact brand of cereal or kind of fruit every day, but they do always consume some fruit and they do always consume some cereal,” Baker explains. “Based on the flow of these products, we can basically [reconstruct] their inventory at any given time, while also allowing for reasonable depreciation. Naturally, you’re going to have more of a stockpile of canned goods or toiletries than fresh meat or produce.”

Next, the researchers had to define how managing this inventory generates a “return” for households in the form of cost savings. Their model, says Baker, assumes that households employ two inventory-management strategies.

The first relies on taking fewer shopping trips and buying in bulk in order to reduce prices. “You invest a bigger amount upfront, but you’re going to use those [goods] over time and therefore pay a lower price over time,” Baker explains.

In the second strategy, a household makes more-frequent trips to multiple stores in order to take advantage of any temporary sales or deals that may be in effect. “Lots and lots of types of goods will be subject to periodic price cuts,” says Baker, “so if you go to the store every day, you can just buy the things that are on sale and stockpile those. On average, you’ll be paying a lower price per product than if you only went to the store once a month.”

Each inventory-management strategy has downsides that constrain potential returns. Buying in bulk requires more upfront capital and makes households less able to take advantage of deals. “I can’t stockpile meat and vegetables for months and months, so if they’re not on sale the day I shop, it’s too bad for me,” Baker explains. Meanwhile, the deal-focused strategy incurs extra costs from more-frequent shopping trips. According to Baker, households “trade off between these mechanisms of savings” to optimize the return on their household inventory.

Beating the market

Baker and his colleagues found that on average, households are holding about $725 worth of inventory at any given time. Richer households tend to hold more quantities of inventory, but their financial assets also comprise a much bigger portion of their overall wealth. Lower-income households, meanwhile, “might hold basically no financial assets at all, but $1,000 worth of household inventory,” says Baker. “This is pretty sizable with respect to their total spending.”

In fact, some of the highest returns from household inventory management were achieved by those households with the lowest levels of working capital. For example, if a lower-income household spends about $5,000 per year on consumable goods and holds $250 worth of cash and inventory as their “household working capital” at any given time, the marginal return—that is, the return on investing additional money into inventory—is about 55 percent, according to Baker’s model.

“When that household buffer is really low, the return on loosening your budget constraints in order to [stockpile] is really high,” he explains. “Sometimes you’re at the store and there are really good deals, and if you spend a little bit more upfront to take advantage of them, it can pay off a lot.”

Average returns on household inventory are high, too—about 50 percent for the typical household in the researchers’ sample. Again, the effect was magnified in households with lower levels of working capital. According to the model, if a household that spends $7,500 a year on consumable goods maintains a working capital stock of $725 of cash and inventory, the annual savings would be equivalent to a 100 percent return on the investment.

“It moves the needle more for households that don’t have a lot of financial wealth,” Baker says. “You can think of it relative to what you get in the stock market. Would I rather take a thousand dollars of inventory and put in the stock market, where it would earn 8 percent? Or should I keep it here, ‘earning’ much more? We see that households are doing this, and that it really pays.”

Ordinary people

Baker notes that he and his collaborators didn’t actually survey anyone about making a choice between investing in stocks or in household inventory. Instead, he says their research provides empirical evidence that everyday households “are optimizing [their economic resources] in often quite sophisticated and fairly detailed ways,” even if those assets aren’t literally financial in nature.

“There are a lot of papers that show the mistakes that consumers are making with various sorts of financial products,” Baker says. “Our paper is pointing in the other direction. And maybe we can try to track some of this stuff better—especially for lower-income households, where it’s making a bigger difference in understanding their financial health in general.”

Associate Professor of Finance

Previously a member of the Finance Department faculty at Kellogg

John Pavlus is a writer and filmmaker focusing on science, technology, and design topics. He lives in Portland, Oregon.

Baker, Scott R., Stephanie G. Johnson, and Lorenz Kueng. 2023. “Financial Returns to Household Inventory Management.” Working paper.

Read the original

We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here . By continuing to use our site, you accept our use of cookies, revised Privacy Policy and Terms of Service .

New to Zacks? Get started here.

Member Sign In

Don't Know Your Password?

• Zacks #1 Rank
• Zacks Industry Rank
• Zacks Sector Rank
• Equity Research
• Mutual Funds
• Mutual Fund Screener
• ETF Screener
• Earnings Calendar
• Earnings Releases
• Earnings ESP
• Earnings ESP Filter
• Stock Screener
• Premium Screens
• Basic Screens
• Research Wizard
• Personal Finance
• Money Managing
• Real Estate
• Retirement Planning
• Tax Information
• My Portfolio
• Create Portfolio
• Style Scores
• Testimonials
• Zacks.com Tutorial

Services Overview

• Zacks Ultimate
• Zacks Investor Collection
• Zacks Premium

Investor Services

• ETF Investor
• Home Run Investor
• Income Investor
• Stocks Under $10
• Value Investor
• Top 10 Stocks

Other Services

• Method for Trading
• Zacks Confidential

Trading Services

• Black Box Trader
• Counterstrike
• Headline Trader
• Insider Trader
• Large-Cap Trader
• Options Trader
• Short Sell List
• Surprise Trader
• Alternative Energy

You are being directed to ZacksTrade, a division of LBMZ Securities and licensed broker-dealer. ZacksTrade and Zacks.com are separate companies. The web link between the two companies is not a solicitation or offer to invest in a particular security or type of security. ZacksTrade does not endorse or adopt any particular investment strategy, any analyst opinion/rating/report or any approach to evaluating individual securities.

If you wish to go to ZacksTrade, click OK . If you do not, click Cancel.

Image: Bigstock

Wells Fargo & Company (WFC) Is a Trending Stock: Facts to Know Before Betting on It

Wells Fargo ( WFC Quick Quote WFC - Free Report ) has been one of the most searched-for stocks on Zacks.com lately. So, you might want to look at some of the facts that could shape the stock's performance in the near term.

Shares of this biggest U.S. mortgage lender have returned +1.3% over the past month versus the Zacks S&P 500 composite's +1.5% change. The Zacks Banks - Major Regional industry, to which Wells Fargo belongs, has gained 7.1% over this period. Now the key question is: Where could the stock be headed in the near term?

While media releases or rumors about a substantial change in a company's business prospects usually make its stock 'trending' and lead to an immediate price change, there are always some fundamental facts that eventually dominate the buy-and-hold decision-making.

Revisions to Earnings Estimates

Here at Zacks, we prioritize appraising the change in the projection of a company's future earnings over anything else. That's because we believe the present value of its future stream of earnings is what determines the fair value for its stock.

We essentially look at how sell-side analysts covering the stock are revising their earnings estimates to reflect the impact of the latest business trends. And if earnings estimates go up for a company, the fair value for its stock goes up. A higher fair value than the current market price drives investors' interest in buying the stock, leading to its price moving higher. This is why empirical research shows a strong correlation between trends in earnings estimate revisions and near-term stock price movements.

Wells Fargo is expected to post earnings of $1.10 per share for the current quarter, representing a year-over-year change of -10.6%. Over the last 30 days, the Zacks Consensus Estimate has changed +0.1%.

The consensus earnings estimate of $4.72 for the current fiscal year indicates a year-over-year change of -13.1%. This estimate has changed -0.1% over the last 30 days.

For the next fiscal year, the consensus earnings estimate of $5.33 indicates a change of +12.9% from what Wells Fargo is expected to report a year ago. Over the past month, the estimate has changed -1.1%.

With an impressive externally audited track record , our proprietary stock rating tool -- the Zacks Rank -- is a more conclusive indicator of a stock's near-term price performance, as it effectively harnesses the power of earnings estimate revisions. The size of the recent change in the consensus estimate, along with three other factors related to earnings estimates , has resulted in a Zacks Rank #3 (Hold) for Wells Fargo.

The chart below shows the evolution of the company's forward 12-month consensus EPS estimate:

12 Month EPS

Projected Revenue Growth

Even though a company's earnings growth is arguably the best indicator of its financial health, nothing much happens if it cannot raise its revenues. It's almost impossible for a company to grow its earnings without growing its revenue for long periods. Therefore, knowing a company's potential revenue growth is crucial.

For Wells Fargo, the consensus sales estimate for the current quarter of $20.17 billion indicates a year-over-year change of -2.7%. For the current and next fiscal years, $80.25 billion and $81.57 billion estimates indicate -2.8% and +1.7% changes, respectively.

Last Reported Results and Surprise History

Wells Fargo reported revenues of $20.48 billion in the last reported quarter, representing a year-over-year change of +4.2%. EPS of $1.29 for the same period compares with $0.67 a year ago.

Compared to the Zacks Consensus Estimate of $20.31 billion, the reported revenues represent a surprise of +0.83%. The EPS surprise was +11.21%.

The company beat consensus EPS estimates in each of the trailing four quarters. The company topped consensus revenue estimates each time over this period.

No investment decision can be efficient without considering a stock's valuation. Whether a stock's current price rightly reflects the intrinsic value of the underlying business and the company's growth prospects is an essential determinant of its future price performance.

While comparing the current values of a company's valuation multiples, such as price-to-earnings (P/E), price-to-sales (P/S) and price-to-cash flow (P/CF), with its own historical values helps determine whether its stock is fairly valued, overvalued, or undervalued, comparing the company relative to its peers on these parameters gives a good sense of the reasonability of the stock's price.

The Zacks Value Style Score (part of the Zacks Style Scores system), which pays close attention to both traditional and unconventional valuation metrics to grade stocks from A to F (an An is better than a B; a B is better than a C; and so on), is pretty helpful in identifying whether a stock is overvalued, rightly valued, or temporarily undervalued.

Wells Fargo is graded A on this front, indicating that it is trading at a discount to its peers. Click here to see the values of some of the valuation metrics that have driven this grade.

Bottom Line

The facts discussed here and much other information on Zacks.com might help determine whether or not it's worthwhile paying attention to the market buzz about Wells Fargo. However, its Zacks Rank #3 does suggest that it may perform in line with the broader market in the near term.

See More Zacks Research for These Tickers

Normally $25 each - click below to receive one report free:.

Wells Fargo & Company (WFC) - free report >>

Published in

This file is used for Yahoo remarketing pixel add

Due to inactivity, you will be signed out in approximately:

Retraction Note: Digital finance, corporate financialization and enterprise operating performance: an empirical research based on Chinese A-share non-financial enterprises

• Retraction Note
• Published: 03 April 2024

Cite this article

• Yingyuan Liu   ORCID: orcid.org/0000-0003-2430-065X 1 ,
• Dandan Jin 1 ,
• Yuemin Liu 2 &
• Qian Wan 1  

The Original Article was published on 04 September 2022

Avoid common mistakes on your manuscript.

Electronic Commerce Research (2022) 23:231–256

https://doi.org/10.1007/s10660-022-09606-z

The Publisher has retracted this article in agreement with the Editor-in-Chief. The article was submitted to be part of a guest-edited issue. An investigation by the publisher found a number of articles, including this one, with a number of concerns, including but not limited to compromised editorial handling and peer review process, inappropriate or irrelevant references or not being in scope of the journal or guest-edited issue. Based on the investigation’s findings the publisher, in consultation with the Editor-in-Chief therefore no longer has confidence in the results and conclusions of this article.

The authors have not responded to correspondence regarding this retraction.

Author information

Authors and affiliations.

Hubei University of Technology, Wuhan, Hubei, 430000, China

Yingyuan Liu, Dandan Jin & Qian Wan

Zhongnan University of Economics and Law, Wuhan, Hubei, 430000, China

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Yingyuan Liu .

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The online version of the original article can be found at https://doi.org/10.1007/s10660-022-09606-z .

About this article

Liu, Y., Jin, D., Liu, Y. et al. Retraction Note: Digital finance, corporate financialization and enterprise operating performance: an empirical research based on Chinese A-share non-financial enterprises. Electron Commer Res (2024). https://doi.org/10.1007/s10660-024-09845-2

Download citation

Published : 03 April 2024

DOI : https://doi.org/10.1007/s10660-024-09845-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Find a journal
• Publish with us
• Track your research