behavioral finance bachelor thesis

Chair of Behavioral Finance

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If you are interested in writing your Bachelor or Master thesis at our chair, please contact us in order to make an appointment for a discussion of a possible topic.

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Overconfidence and active management by pension fund managers., customer segmentation in swiss retail banking., auch in china werden die schweizer banken lernen müssen, nein zu sagen., märkte für derivate und strukturierte produkte - ein globaler vergleich., hat finance eine kulturelle dimension, relation between equity home bias and ambiguity aversion: an international study., behavioral determinants of home bias-theory and experiment. ssrn working paper., ambuigity aversion, information unvertainty and momentum around the world., are top-tens better a study on investor attention and ranking lists., how time preferences differ: evidence from 52 countries., does finance have cultural dimension, prospect theory in behavioral finance. ., economic policy uncertainty and the bitcoin market.

Huynh, T. L. D., Wang, M., Vo, V. X. (accepted pre-print), Economic policy uncertainty and the Bitcoin market: an investigation in the COVID-19 pandemic with transfer entropy, The Singapore Economic Review .

Earnings expectations of grey and green energy firms

Liu, Y., Blankenburg, M., Wang, M. (2023), Earnings expectations of grey and green energy firms: analysis against the background of global climate change mitigation, Energy Economics , Vol. 121, 106692.

Energy structure and carbon emission

Liu, Y., Xie, X., Wang, M. (2023), Energy structure and carbon emission: analysis against the background of the current energy crisis in the EU, Energy Economics , Vol. 280, 128129.

Economic individualism, perceived fairness, and policy preference

Wang, M. (2023), Economic individualism, perceived fairness, and policy preference: a cross-cultural comparison, Review of Behavioral Economics , Vol. 10 (1), pp. 3-26.

New experimental evidence on the relationship between home bias, ambiguity aversion and familiarity heuristics

Dlugosch, D., Horn, K., Wang, M. (2023), New experimental evidence on the relationship between home bias, ambiguity aversion and familiarity heuristics, Journal of Economics and Business , Vol. 125-126, 106131.

Trust and the stock market reaction to lockdown and reopening announcements

Xie, L., Wang, M., Huynh, T. L. D. (2022), Trust and the stock market reaction to lockdown and reopening announcements: a cross-country evidence, Finance Research Letters , Vol. 46, Part A, 102361.

Trust in government actions during the COVID-19 crisis

Rieger, M., Wang, M. (2022), Trust in government actions during the COVID-19 crisis, Social Indicators Research , Vol. 159 (3), pp. 967–989.

COVID-19 and the Wuhan diary –how does the overseas Chinese community react to group criticism?

Wang, M., Rieger, M. (2022), COVID-19 and the Wuhan diary –how does the overseas Chinese community react to group criticism?, Journal of Chinese Political Science , Vol. 37 (4), pp. 637–659.

Ambiguity, ambiguity aversion and foreign bias

Dlugosch, D., Wang, M. (2022), Ambiguity, ambiguity aversion and foreign bias: new evidence from international panel data, Journal of Banking & Finance , Vol. 140, 106509.

Cross-country comparison in dishonest behaviour

Huynh, T. L. D., Rieger, M. O., Wang, M. (2022), Cross-country comparison in dishonest behaviour: Germany and East Asian countries, Economics Letters , Vol. 215, 110480.

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STUDY ON BEHAVIORAL FINANCE, BEHAVIORAL BIASES, AND INVESTMENT DECISIONS

Behavioral finance is an open-minded finance which includes the study of psychology, sociology, and finance. Behavioral finance micro examines behavior or biases of investors and behavioral finance macro describe anomalies in the efficient market. Nowadays, behavioral finance is not a new concept, the existence, and impact of behavioral biases in investor's behavior and human judgment are huge. In this paper, we will review various studies in this area so as to have a clear understanding of the behavioral finance and its significance in the financial decision making of investors. JEL CLASSIFICATION: G11, G14

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Traditional finance theory is based on the principle of maximization of utility and explains how choices are made by rational people. Although the theory provides numerous insights, observation of actual behavior of people was seen to be different from what the theory predicted. The homo economicus is in reality a homo sapien who has emotions and beliefs that help to filter the content from his or her environment. These beliefs and preferences that arise due to cogni-tive limitations, presence of emotions, and various psychological motives guide or bias his or her decisions. Much literature states that the biases should be corrected as they negatively impact financial behaviour and individual's well‐being. However, evolutionary psychology considers biases as design features of human mind. Thus, biases are not always bad, as at times, these biases can help the individual investor to choose the best course of action from the multiple possibilities and enable committing the less costly mistakes, thereby helping the individual to achieve satisficing behaviour. This paper aims to explore the investor biases and see whether they are related to the financial satisfaction of the individuals. Financial satisfaction is the measure of satisfaction with one's financial situation. The results showed that overconfidence bias, reliance on expert bias, and self‐control bias have a positive and significant association with financial satisfaction levels. Association of a few other biases with financial satisfaction was also observed under certain control conditions. This study provides further insights on investor behavior and paves the way for various possibilities for future research.

IJAR Indexing

Research has proved that investors in the equity market are not consistently rational. Emotions influence their decision making process in the complex environment of equity market, in the form of behavioral biases. This paper reviews five important behavioral biases exhibited by investors in the equity market. The behavioral biases reviewed include, representativeness, anchoring, gambler?s fallacy, availability and optimism. The literature available for each of the biases is reviewed and hence this paper draws attention to a new dimension in finance.

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Asian Journal of Economics, Business and Accounting

This study investigates the existence of heuristics biases in Colombo Stock Exchange and their effect on investment performance from individual investor's point of view. In specific, the effects of anchoring, availability bias, gamblers fallacy, overconfidence and representativeness are investigated. Further, the study inspects whether the heuristics biases differ between younger and older investors. The primary data were collected by survey from 425 individual investors. The data were analyzed using multivariate analysis such as Confirmatory Factor Analysis (CFA) and Structure Equation Modeling (SEM). The results show that there is a statistically significant effect of anchoring, availability bias, overconfidence and representativeness bias on investment performance. However, gamblers fallacy not significantly affects investment performance. Furthermore, statistically significant differences are found between the answers of younger and older investors. This study, hopefully, will help investors to be aware of the impact of their own heuristics bias on their decision making in the stock market, thus increasing the rationality of investment decisions for enhanced market efficiency.

IJREAM EDITOR

Finance is the system that includes the granting of money and credit, making of investments and provision of banking facilities. Behavioral finance is a new academic discipline which seeks to apply the insights of the psychologists to understand the behavior of both investors and financial markets. This study analyse the Investors behavior through 600 respondents using Factor analysis test. The results of the study show that the 16 variables selected for the study had been reduced to 5 factor models using the principle component analysis such as Market Dynamics, Logical Analysis , Herding Bias, Regret Aversion and Heuristic Bias. Thus, Behavioral finance is becoming a primary part of the decision making process, since it influences investors' behavior greatly.

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This study investigates the existence of behavioral biases in Amman Stock Exchange and their effect on investment performance from investor's point of view. In specific, the effects of overconfidence bias, familiarity bias, loss aversion bias, disposition bias, availability bias, representativeness bias, confirmation bias and herding bias are investigated. Moreover, the study inspects whether the behavioral biases differ between males and females. The results show that there is a statistically significant effect of overconfidence bias, familiarity bias, availability bias, representativeness bias and herding bias on investment performance (p≤5%). Moreover, disposition bias, confirmation bias and loss aversion bias significantly affect investment performance but at a critical level of (p≤10%). No statistically significant differences are found between the answers of males and females.

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Finance has been studied around the globe from ages but the dimensions of behavioral science have been related with finance only a few decades before. This led to evolution of behavioral finance, where effect of human emotions, cognitive errors and psychology on investment decision is studied. The main objective of this study was to explore the individual investors’ investment preference i.e., utilitarian or value-expressive. Moreover, the extent to which their investment decision is dominated by their investment preference has been studied. The relationship between demographic factors and investment preference of an individual has also been examined. The results show that the individual investors at Indian stock exchange, in general, are more value-expressive than utilitarian. Their investment decisions are affected by many behavioral biases as well as with certain demographic factors.

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Investors exhibit irrational behaviour in their decision-making. The decision-making process itself is considered to be a cognitive process as the investors have to make a decision based on various alternatives available to them. The researchers have found that the investors’ decision-making was adversely affected by the various psychological/behavioural factors. The current study was carried forward to identify the effect of the behavioural factors affecting the investment decision of the investors. Five behavioural factors, namely overconfidence bias, representative bias, regret aversion, mental accounting, and herd behaviour, were considered to study the behavioural biases of the investors. The study sample was taken from investors of Kerala, and the analytical hierarchy process (AHP) method was used to analyse the intensity of behavioural factors affecting the investment decision. Based on the priority vector, it was found that the investors of Kerala were highly influenced with overconfidence bias and regret aversion. Herd behaviour had less effect on their decision-making.

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The role of risk in investment behaviour and the manifestation of behavioural biases by individual investors

Suhobokov, Alexander (2021) The role of risk in investment behaviour and the manifestation of behavioural biases by individual investors. PhD thesis, University of Glasgow.

I use a novel dataset based on 8,000 retail clients of a large brokerage house over four years to evaluate if individual investors take decisions according to one of the dominating decision-making theories – traditional Expected Utility Theory or behavioural Prospect Theory. Another key question of my research is the role of affect in judgements and its impact on investment results and behaviour. The thesis includes three related empirical chapters. In the first empirical chapter, I explore how (ir)rational are retail investors and what are the boundaries of their rationality proxied with the relation between realised risk and return. In the second empirical chapter, I examine how the correlation between risk and return for the same group of investors varies in Live trading environment versus virtual Contest environment highlighting the role of emotions in correlation dynamics. In the third empirical chapter, I keep the emphasis on comparing Live and Contest investment settings, but now I evaluate the impact of emotions on profitability and various manifestations of risk behaviour. My research contributes to the academic literature in the domain of finance and investments that is trying to establish the positioning and the role of emotional account in the judgement and decision-making of economic agents. I provide empirical evidence that feelings have a substantial impact on investment results and risk behaviour of individual traders. The empirical nature of my analysis involving a large group of private investors grants significant support to prior findings that predominantly developed using neuro-physiological, interview-type and experimental methodologies. Besides, I present empirical support for the long-lasting debate concerning traditional and behavioural financial theories. Analysing the relation between risk and return, I manage to validate that investors in my sample manifest all behavioural patterns implied by Prospect Theory: they are risk-averse in the gains domain, risk-seeking in the losses domain and exposed to the loss aversion bias.

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Main Content

Bachelor's and master's thesis.

This section will inform you about the possibility of having your thesis supervised by the Behavioral Finance Research Group.

Building on our contents in teaching, we offer the opportunity to write a thesis at the Behavioral Finance Research Group.

Department's best thesis of 2019 supervised at the Behavioral Finance Research Group

Admission requirements 

Bachelor's thesis:

• Major "Accounting & Finance"
• Successful attendance of  Entrepreneurial Finance.  

Master's thesis :

• Successful attendance of one of the institute’s seminars .
• Attendance of  Behavioral Finance is strongly recommended.
• Attendance of  Quantitative Methods in Empirical Finance is strongly recommended.

Application deadline

Summer term: March 1st Winter term: September 1st  

Application procedure

Inhalt ausklappen Inhalt einklappen Application

The Application should consist of: - Letter of Motivation (max. one page) - CV - Current transcript of records (grades)

Inhalt ausklappen Inhalt einklappen Topic assignment

The assignment of the topics takes place in a personal meeting with a member of the chair The topics are oriented towards research topics of the chair: - Behavioral Finance - Entrepreneurial Finance - Household Finance Accepted thesis topics include a self-directed empirical analysis.

Inhalt ausklappen Inhalt einklappen Exposé creation

After the assignment of the topic an exposé (1-2 pages) has to be prepared. It should cover the following content: - Problem definition - Objectives - Procedure - Provisional structure - Basic literature

Inhalt ausklappen Inhalt einklappen Registration at the examination office

The application is done by submitting the application for admission to the Examination Office. The filing date is April 1st, or October 1st.

Please send your application to [email protected]  within the stated deadlines.

General Remarks:

• For Application forms go to the Download center of the examination office .
• Formal guideline for writing term papers and theses  and Write your best paper .

"The practical relevance of my thesis topic is particularly valuable"

"Fascinating question on a current topic"

"Theme requests will be taken into account when the works are allocated"

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Department of Finance

Quicklinks und sprachwechsel, main navigation.

The preparation for the application as well as the writing of the Bachelor or Master's thesis at the Department of Finance entails different steps, whereby the application process is described in detail here.

• You find your own topic, write a Research Proposal and submit it via DF Thesis Market.
• Choose one of the provided Topic Proposals and apply for the corresponding topic on the DF Thesis Market including a letter of motivation.
• Supervisor: If your Research Proposal has been accepted or your application for an existing topic has been successful, you will be contacted by the person who will supervise you in your thesis. Based on your Research Proposal inputs, your supervisor will formulate the thesis assignment.
• Thesis assignment on OLAT: The task assignment includes your official thesis assignment for the written thesis. It can be collected on OLAT from the moment you have been informed by your supervisor. Once you collect the thesis assignment on OLAT, the time limit of six months starts.
• Time limit: Start working on your thesis early and discuss problems with your supervisor. Nevertheless, remember that a Bachelor or Master’s thesis is to be written independently.
• Submission: You must submit your thesis via OLAT i.e. you upload your thesis and any additional documents/attachments as a ZIP-file to OLAT.
• Grading of task: You receive your grade within a month after submission.

The following Video in German shows the recording of a presentation (PDF, 276 KB) in which the students were informed. It goes more into detail about the individual steps.

The prerequisite for writing a Bachelor and Master's thesis at the Department of Finance is relevant prior knowledge in the corresponding subject area. In particular, the relevant lectures must have been attended and passed. Provided Topic Proposals may contain further requirements, which you will find in the respective proposal.

It is also important that the rules and instructions of the Dean's Office ( study and graduation ) are generally to be noted. The responsibility regarding compliance with these regulations lies with you.

Application

In order to write a thesis at the Department of Finance, a digital application via the DF Thesis Market is required. You will need the following documents for the application:

• Curriculum Vitae (as PDF)
• Transcript of records (as PDF, current export from the Module Booking)
• Research Proposal (if you propose a topic of your own)

For the application via the Department of Finance Thesis Market, you also need your UZH login credentials (shortname and password).

There are two ways to apply for a thesis at the Department of Finance:

Option A: Own topic

Elaborate your own suggested topic and write a Research Proposal. On two to three pages, the Research Proposal summarises your motivation, the objectives, the planned procedure and the expected results. The following documents serve as a guide:

• Instructions for writing a Research Proposal (PDF, 114 KB)
• Example of a Research Proposal (PDF, 205 KB)

If you would like to write an empirical paper, check before submitting your application whether the data you need can be found in the available databases . It is advisable to check with a concrete example whether the data quality is sufficient (e.g., availability of time series).

It is important that you assign your Research Proposal to the correct research area so that it can be made available to appropriate supervisors. The research areas and fields of interest listed in the table below can serve as a decision-making aid. The links of the Professors lead to the Bachelor and Master's theses that they supervised so far. They can give an intuition on typical topics for writing a thesis.

Based on your Research Proposal, the final thesis assignment will be issued. However, the Department of Finance reserves the right to ask for improvements to the proposal, to make changes, to provide a different topic or to reject the application.

Option B: Provided topic

Apply for a topic provided by a supervisor via DF Thesis Market . Look at the topics on the marketplace, choose one and apply. Note that in addition to a CV and transcript of records, a short letter of motivation is also required for the application. Describe how the provided topic matches your skills and interests, and how the topic fits into your course of study.

Useful documents

As guidance to help you estimate the length of a thesis, we provide you with two sample theses:

• Bachelor’s thesis: The different theories of the 2010 Flash Crash with main focus on high-frequency trading (PDF, 1 MB)
• Master’s thesis: Strategic Allocation to Return Factors (PDF, 1 MB)

Additionally you can find a template for LaTeX (ZIP, 414 KB) .

Further notes

The Department of Finance strongly recommends that you start finding a topic for your Bachelor or Master's thesis at an early stage. The application must be submitted at least one month before the desired starting month. For example if you want to start writing your thesis at the beginning of May, you must apply by the end of March. 

The matching process usually takes about a month, sometimes longer (especially for your own proposals), since all supervisors supervise several theses, and your own proposal must match the interests of your supervisor in terms of content. We will inform you as soon as the matching process is completed, or if we need further information or adjustments from you. If you want/need to start as soon as possible, you also have the option to apply to one of the posted proposals.

The DF endeavours to offer all applicants the opportunity to write a thesis at the Department of Finance, but we cannot guarantee a specific topic or a specific supervisor. Temporary bottlenecks may occur in the supervision. If you have any questions or problems in connection with your application, please contact the study coordinator and Managing Director of the DF, Dr. Benjamin Wilding, at [email protected] .

Research interest

Bereichs-navigation, unterseiten von theses.

• DF Thesis Market
• FAQ for Students

• Help & FAQ

Essays on behavioral finance and corporate finance

• Research Group: Finance
• Center Ph. D. Students

Research output : Thesis › Doctoral Thesis

Access to Document

• 10.26116/dd2e-g320
• TiU_220207_CeNTER_proefschrift Lingbo Shen_digitaal Final published version, 1.32 MB

Fingerprint

• Behavioral Finance Business & Economics 100%
• Corporate Finance Business & Economics 95%
• Analysts Business & Economics 45%
• Conference Calls Business & Economics 40%
• Inventor Business & Economics 31%
• Ethnic Groups Business & Economics 23%
• Innovation Business & Economics 20%
• Interaction Business & Economics 14%

T1 - Essays on behavioral finance and corporate finance

AU - Shen, Lingbo

N2 - This Ph.D. dissertation consists of three independent chapters in behavioral finance and corporate finance. The first chapter examines whether and how ethnicity similarity between analysts and executives affect their interactions in conference calls. The second chapter investigates firms' demand for inventor executives, executives with innovation experience, around firms' IPOs. The last chapter studies teams and individual analysts performance differences during the COVID-19 pandemic crisis time.

AB - This Ph.D. dissertation consists of three independent chapters in behavioral finance and corporate finance. The first chapter examines whether and how ethnicity similarity between analysts and executives affect their interactions in conference calls. The second chapter investigates firms' demand for inventor executives, executives with innovation experience, around firms' IPOs. The last chapter studies teams and individual analysts performance differences during the COVID-19 pandemic crisis time.

U2 - 10.26116/dd2e-g320

DO - 10.26116/dd2e-g320

M3 - Doctoral Thesis

SN - 978 90 5668 683 3

T3 - CentER Dissertation Series

PB - CentER, Center for Economic Research

CY - Tilburg

We appreciate that you are interested in writing a thesis at the Institute of Banking and Finance. The following sections provide information on potential areas for both Bachelor and Master theses. When conducting your thesis, you will have to critically review the relevant literature and to carry out your own quantitative analysis. This requires applying software for statistical analysis (R, Matlab, or Stata). To prepare you, we offer online courses in scientific writing and an introduction to R. We are looking forward to supervising your thesis!

Bachelor theses

Master theses, general information on final theses, contact for general questions about theses, registration.

After you have been assigned to the Institute of Banking and Finance through the central allocation procedure of the Faculty of Economics and Management, you can apply for one of the topics listed below. If you have any questions, please contact Brian von Knoblauch .

Please note: Bachelor theses at our institute are always related to empirical research questions. We there strongly (!) recommend to conduct a seminar thesis at our institute and to take finance related classes.

An information session that covers organizational aspects and introduces available topics will be held on Tuesday, February 13, (Warning: Changed Date!) 2024, from 2:30pm - 4:00pm via Cisco WebEx . To join the session (via browser or app), please click here . Further information is available via this link (in German).

To choose preferences and your preferred starting date, please click here: Application form

Please also note that - to register your thesis - it is mandatory to complete our introductions to Scientific Writing and R .

Bachelor theses not related to the central allocation prodecure (industrial engineers or second attempts) can be registered throughout the whole year.  Please note that we can only offer a limited number of Wi-Ing places at our institute in the upcoming summer semester 2023. Currently (as of 01.02.2024) four places are still open.

As soon as you have received your topic, you will have 2 weeks to prepare a proposal (please take into account time to revise the proposal!). On 2-3 pages, the proposal should cover the following elements:

• Problem setting and objective of the thesis
• Methodology and theoretical and/or conceptual approaches
• Necessary data and sources for data acquisition
• Expected knowledge gains for research and/or practice
• Basic literature (from international, peer-reviewed journals)

After the proposal has been accepted by your supervisor, your bachelor thesis will be registered immediately.

Bachelor theses in Behavioral Finance

Theoretical part of the task:

• Explain the "noise trader theory" according to De Long et al (1990).
• Define the term "investor sentiment" and outline approaches to measure sentiment.

Empirical part of the task:

• Investigate the impact of investor sentiment on stock market returns or anomalies.
• Test the robustness of your results with respect to combinations of selected control variables. Are you results robust to subperiods?

Basic literature:

• Baker, M. and Wurgler, J. (2006): Investor Sentiment and the Cross-Section of Stock Returns.  The Journal of Finance,  61(4), 1645–1680.
• Baker, M. and Wurgler, J. (2007): Investor Sentiment in the Stock Market. Journal of Economic Perspectives,  21(2), 129–152.
• De Long, J.B., Shleifer, A., Summers, L.H., and Waldmann, R.J. (1990): Noise Trader Risk in Financial Markets.  Journal of Political Economy,  98(4), 703–738.
• Fisher, K.L. and Statman, M. (2000): Investor Sentiment and Stock Returns.  Financial Analysts Journal,  56(2), 16–23.
• Frazzini, A. and Pedersen, L.H. (2014): Betting against beta.  Journal of Financial Economics,  111(1), 1-25.
• Jegadeesh, N. and Titman, S. (1993): Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency.  The Journal of Finance,  48(1), 65-91.
• Lee, W.Y., Jiang, C.X., and Indro, D.C. (2002): Stock market volatility, excess returns, and the role of investor sentiment. Journal of Banking & Finance,  26(12), 2277–2299.
• Lee, C.M.C., Shleifer, A., and Thaler, R.H. (1991): Investor Sentiment and the Closed-End Fund Puzzle. The Journal of Finance, 46(1), 75–109.
• Lemmon, M. and Portniaguina, E. (2006): Consumer Confidence and Asset Prices: Some Empirical Evidence.  The Review of Financial Studies , 19(4), 1499–1529.  
• Stambaugh, R.F., Yu, J., and Yuan, Y. (2012): The short of it: Investor sentiment and anomalies.  Journal of Financial Economics , 104(2), 288-302.
• Kenneth French Data Library
• Refinitiv Datastream
• Describe the term "investor sentiment" and explain ways to measure it. In particular, address methods for text-based measurement of investor sentiment.
• Provide a review of relevant literature examining the relationship between text-based sentiment measures and stock returns.
• Calculate a text-based sentiment measure and explain its step-by-step derivation from raw text to final measure.
• Perform a descriptive analysis of the sentiment measure.
• Analysieren den Zusammenhang zwischen Ihrem hergeleiteten Stimmungsmaß und Aktienrenditen anhand von Regressionsmodellen.
• Analyze the relationship between your inferred sentiment measure and stock returns using regression models.
• McDonald, B. and Loughran, T. (2011): When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10-Ks. The Journal of Finance, 66(1), 35-65.
• Smales, L. A. (2017): The importance of fear: investor sentiment and stock market returns.  Applied Economics , 49(34), 3395-3421.
• Stambaugh, R.F., Yu, J., and Yuan, Y. (2012): The short of it: Investor sentiment and anomalies. Journal of Financial Economics, Special Issue on Investor Sentiment,  104(2), 288-302.
• Tetlock, P.C. (2007): Giving Content to Investor Sentiment: The Role of Media in the Stock Market. The Journal of Finance, 62(3), 1139-1168.
• Refinitiv Workspace
• Loughran-McDonald Master Dictionary
• New York Times News Article
• Separate the empirical evidence of investor participation from the assumptions of classical portfolio theory. Motivate and explain determinants of participation.
• Formulate a probit model in accordance with relevant models from the literature. Introduce the probit regression.
• Among other things, you will deal with estimation using the maximum likelihood method.

 Empirical part of the task:

• Check the developed model by means of a panel data set.
• Explicitly refer to the definitions you used to create variables and describe the data set.
• Perform the estimation of the probit model and interpret your results.
• Grinblatt, M., Keloharju, M., and Linnainmaa, J. (2011): IQ and stock market participation. The Journal of Finance, 66 (6), 2121-2164.
• Kaustia, M. and Torstila, S. (2011): Stock market aversion? Political preferences and stock market participation. Journal of Financial Economics, 100(1), 98-112.
• Van Rooij, M., Lusardi, A., and Alessie, R. (2011): Financial literacy and stock market participation. Journal of Financial Economics, 101(2), 449-472.
• Brooks, C. (2019):  Introductory Econometrics for Finance. Fourth edition. Cambridge, United Kingdom; New York, NY: Cambridge University Press.
• Polkovnichenko, V. (2005): Household Portfolio Diversification: A Case for Rank-Dependent Preferences.  The Review of Financial Studies, 18(4), 1467–1502.
• Malmendier,  U. and Nagel, S. (2019): Depression Babies: Do Macroeconomic Experiences Affect Risk Taking?.  The Quarterly Journal of Economics,  126(1), 373–416.

 Data:

• Explain the difference between normative and descriptive decision theories.
• Introduce and explain selected static and dynamic portfolio insurance strategies.
• Explain Cumulative Prospect Theory (CPT) and its role for the evaluation of portfolio insurance strategies.
• Conduct a simulation study comparing different selected portfolio insurance strategies in regard to their CPT value and the corresponding expected utility (EUT). Do the decisions of CPT investors differ from an EUT investor?
• Interpret your results in regards to the sensitivity of your results to the different CPT parameters. Is any parameter more important than others?
• Tversky, A. and Kahneman, D. (1992): Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and uncertainty , 5(4), 297-323.
• Dichtl, H. and Drobetz, W. (2011): Portfolio insurance and prospect theory investors: Popularity and optimal design of capital protected financial products. Journal of Banking and Finance , 35(7), 1683-1697.
• Dierkes, M., Erner, C., and Zeisberger, S. (2010): Investment horizon and the attractiveness of investment strategies: a behavioral approach. Journal of Banking and Finance, 34, 1032-1046.
• Explain the Cumulative Prospect Theory (CPT) as a descriptive decision theory and outline differences from normative decision theories.
• Explain how individual stocks can be evaluated as "prospects" under the CPT.
• Present the model-theoretical prediction for stock returns of companies depending on their CPT value.

Quantitative part of the task:

• Calculate the CPT values of all companies in a relevant sample of a stock market (e.g., US market).
• Analyze the performance of companies depending on their CPT values using portfolio construction and Fama-MacBeth regressions.
• Evaluate with your performance analysis whether factor models (e.g., CAPM, Fama-French Three-Factor Model) can explain these returns.
• Tversky, A. and Kahneman, D., (1992 ), Advances in prospect theory: Cumulative representation of uncertainty, Journal of Risk and Uncertainty , 5(4), 297-323. Cambridge, United Kingdom.
• Barberis, N., Abhiroop, M. and Baolian, W., (2016 ), Prospect theory and stock returns: An empirical test, The review of financial studies , 29(11), 3068-3107. Cambridge, United Kingdom.
• Bali, T.G., Engle, R. F. and Murray, S., (2016 ), Empirical asset pricing: The cross section of stock returns, John Wiley & Sons, Cambridge, United Kingdom.

Bachelor theses in Asset Management

• Define sustainability criteria (e.g. ESG) and explain the Morningstar-Sustainability-Ranking .
• Give an overview of the relevant literature of performance measurements and explain common descriptive and risk-adjusted performance measurements.
• Calculate and compare performance measurements for different categories of sustainability funds and a market benchmark.
• Identify and interpret differences between the categories.
• Bauer, R., Koedijk, K., and Rotten, R. (2005): International evidence on ethical mutual fund performance and investment style. Journal of Banking & Finance, 29(7), 1751-1767.
• Brooks, C. (2019): Introductory Econometrics for Finance. Fourth edition. Cambridge, United Kingdom ; New York, NY: Cambridge University Press.
• Schroeder, M. (2006): Is there a Difference? The Performance Characteristics of SRI Equity Indices. Journal of Business Finance & Accounting, 34(1-2), 331-348.
• Database of Richard Stehle
• Morningstar

Bachelor theses in Risk Management

• Introduce in general terms the role of volatility in financial markets.
• Explain the concept of Realized Volatility and provide an overview of traditional econometric forecasting models, such as Corsi's (2008) heterogenous autoregressive (HAR) model.
• Explain selected machine learning methods and their estimation procedures in the context of Realized Volatility predictions.
• Evaluate the predictive performance of selected machine learning methods based on a chosen data set, such as daily Realized Volatility of the S&P 500.
• Compare your results with those of selected traditional econometric models and discuss your findings.

Basic literature (selection):

• Corsi, F. (2008): A Simple Approximate Long-Memory Model of Realized Volatility.  Journal of Financial Econometrics,  7(2), 174–196.
• Bucci, A. (2020): Realized Volatility Forecasting with Neural Networks.  Journal of Financial Econometrics,  18(3), 502–531.
• Christensen, K., Siggaard, M., and Veliyev, B. (2022): A Machine Learning Approach to Volatility Forecasting. Journal of Financial Econometrics.
• James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013): An introduction to statistical learning: with applications in R. 2nd Edition, Springer.

Data Resources:

• Oxford Realized Library
• Provide an overview of the relevant literature on the forecasting of credit defaults of companies and individuals.Pay special attention to so-called P2P loans.
• Identify relevant characteristics of private debtors that potentially affect the risk of credit default.
• Explain the logit regression and address the marginal effects and the ROC procedure.
• Set up a logit model to estimate the probability of default of personal loans.
• Analyse the Lending Club data set and present the characteristics of the loans granted there.
• Do you estimate the logit model set up on the basis of the data, can defaults be forecast?
• Emekter, R., Tu, Y., Jirasakuldech, B., and Lu, M. (2015): Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending.  Applied Economics, 47(1), 54-70.
• Hull, J. (2018): Risk management and financial institutions. Hoboken, New Jersey: Wiley & Sons.
• Brooks, C. (2014): Introductory econometrics for finance. Cambridge: Cambridge University Press. 
• Lending Club Privatkredite, via kaggle.com

Bachelor theses in Asset Pricing

• Describe the momentum anomaly and explain how to construct the momentum strategy.
• Note both advantages and disadvantages of the momentum strategy. In particular, focus on momentum crashes.
• Outline the risk management strategies of Barroso and Santa-Clara (2015) and Dierkes and Krupski (2022).
• Estimate the momentum strategy for the U.S. market over the period from 1926 to 2022.
• Implement the risk management strategies of Barosso and Santa-Clara (2015) and Dierkes and Krupski (2022).
• Outline both advantages and disadvantages of each strategy.
• Barroso, P. and Santa-Clara, P. (2015): Momentum has its moments. Journal of Financial Economics, 116(1), 111–120.
• Cooper, M.J., Gutierrez, R.C., and Hameed, A. (2004): Market States and Momentum. The Journal of Finance, 59(3), 1345–1365.
• Dierkes, M. and Krupski, J. (2022): Isolating momentum crashes. Journal of Empirical Finance, 66, 1-22.
• Daniel, K. and Moskowitz, T.J. (2016): Momentum crashes. Journal of Financial Economics, 122(1), 221–247.
• Jegadeesh, N. and Titman, S. (1993): Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. The Journal of Finance, 48(1), 65–91.
• Kenneth French's database
• Derive the Capital Asset Pricing Model (CAPM) and explain why the use of additional factors can be a useful extension.
• Outline the three-factor model of Fama and French (1993).
• Explain the value and the size effect on which the three-factor model is built.
• Calulate the risk factors yourself using monthly price data.
• Analyze to which extend multi-factor models can increase the explanability of return data.
• Explicitly conduct a performance test against the CAPM.
• What influence do the factors of value and size have on returns? Do they match your expectations? 
• Fama, E. F. and French, K. R. (1993): Common risk factors in the returns on stocks and bonds.  Journal of Financial Economics, 33(1), 3–56.
• Fama, E. F. and French, K. R. (1992): The cross-section of expected stock returns.  Journal of Finance, 47(2), 427–465.
• Fama, E. F. and French, K. R. (2015): A five-factor asset pricing model.  Journal of Financial Economics, 116(1), 1–22.
• Empirical research shows a strong negative relationship between returns and idiosyncratic volatility.
• Derive why in neoclassical finance theory idiosyncratic volatility should not affect returns.
• Introduce the so-called idiosyncratic volatility puzzle and provide an overview of relevant related literature. Explain possible solutions to the puzzle.
• Calculate idiosyncratic volatilities for a cross-section of stocks.
• Evaluate pricing effects of idiosyncratic volatility using portfolio formation and investigate whether they are significant.
• Ang, A., Hodrick, R. J., Xing, Y., and Zhang, X. (2006): The cross‐section of volatility and expected returns.  Journal of Finance, 61(1), 259-299.
• Ang, A., Hodrick, R. J., Xing, Y., and Zhang, X. (2009): High idiosyncratic volatility and low returns: International and further US evidence.  Journal of Financial Economics, 91(1), 1-23.
• Bali, T. G. and Cakici, N. (2008): Idiosyncratic volatility and the cross section of expected returns.  Journal of Financial and Quantitative Analysis, 43(01), 29-58.
• Short-Term Reversal is one of the most distinctive anomalies in asset pricing. Explain the (short-term) reversal effect and show why this effect counteracts the weak form of the efficient market hypothesis.
• Introduce to the relevant literatur.
• Provide an overview of the different explanatory approaches.
• Conduct an empirical analysis of the short term reversal effect using linear regression and portfolio formation.
• Investigate whether the short term reversal effect can be explained by capital market models (e.g. CAPM, Fama-French three factor model).
• Jegadeesh, N. (1990): Evidence of predictable behavior of security returns.  Journal of Finance, 45(3), 881-898.
• Jegadeesh, N. and Titman, S. (1995): Short-horizon return reversals and the bid-ask spread. Journal of Financial Intermediation, 4(2), 116-132.
• Campbell, J. Y., Grossman, S. J., and Wang, J. (1993): Trading volume and serial correlation in stock returns.  Quarterly Journal of Economics, 108, 905–939.
• Kelly, B., Moskowitz, T., and Pruitt, S. (2021): Understanding Momentum and Reversal.  Journal of Financial Economics, 140(3), 726-743.
• CRSP US Stock Databases
• Introduce the topic of economic uncertainty and distinguish this concept from other concepts relevant to finance such as risk and investor sentiment.
• Introduce the literature on uncertainty measurement and explain the different methodological approaches. In this context, explain in detail the derivation of two selected measures.
• Explain why economic uncertainty can have a theoretical impact on real and financial economics.  In this context, present empirical literature that examines the relationship between uncertainty and financial markets.
• Perform a descriptive analysis of the selected uncertainty measures.
• Analyze the relationship between the selected uncertainty measures and stock returns using regression models.
• Bloom, N. (2014): Fluctuations in Uncertainty. Journal of Economic Perspectives, 28(2), 153-176.
• Brogaard, J., and Detzel, A. (2015): The Asset-Pricing Implications of Government Economic Policy Uncertainty. Management Science, 61(1), 3-18.
• Jurado, K., Ludvigson, S. C., and Serena, N. (2015): Measuring Uncertainty. American Economic Review,  105(3), 1177-1216.
• Knight, F.H. (1921): Risk, Uncertainty and Profit. Houghton Mifflin Company, Boston , 682-690.
• Datenbank von Sydney Ludvigson
• EPU Datenbank

Bachelor theses in Corporate Finance

• Standard methods for calculating the cost of capital use realized returns as an approximation for expected future returns. Implicit cost of capital offer an alternative in which the estimator for the cost of capital is derived implicitly and ex ante from a valuation model.
• Give an introduction into the valuation of companies.
• Derive the cost of capital model according to Ohlson and Juettner-Nauroth (2005).
• The cost of capital model above requires forecasts of earnings. Explain how earnings can be estimated via regression using the model of Hou et al. (2012). Additionally, address advantages and disadvantages for using estimates from analysts as alternative.
• Conduct an empirical analysis of implicit capital costs at firm and market level for the German (European) stock market.
• Compare the implied cost of capital estimates when using analyst forecasts and when using earnings forecasts by the model of Hou et al. (2012), respectively. 
• Hou, K., Van Dijk, M. A., and Zhang, Y. (2012): The implied cost of capital: A new approach.  Journal of Accounting and Economics, 53(3), 504–526.
• Ohlson, J. A. and Juettner-Nauroth, B. E. (2005): Expected eps and eps growth as determinants of value.  Review of accounting studies, 10(2), 349–365.
• CDAX/STOXX Europe 600 (from Refinitiv Workspace)
• I/B/E/S Estimates

Application for master theses is possible throughout the year, i.e. there are no fixed deadlines. However, you should contact us at least 4 weeks before the desired registration date to find a topic and prepare a proposal.

Please contact Brian von Knoblauch by e-mail and include the following information:

• Choose two preferences from the topics listed below.
• Outline your motivation.
• When is your master thesis supposed to start?
• An up-to-date overview of your grades.

Subsequently, you will receive an e-mail from your supervisor (depending on the topic) to arrange an appointment. In this meeting, we will define the research question of your thesis and outline what should be included in your proposal.

As soon as you have received your topic, you will have roughly 3 weeks to prepare a proposal (please take into account time to revise the proposal!). On 2-3 pages, the proposal should cover the following elements:

After the proposal has been accepted by your supervisor, your master thesis will be registered immediately.

Brief description of the area

Investor sentiment is an important element of Behavioral Finance. Hence, there are numerous studies to analyze the impact of investor sentiment on stock markets. In addition to sentiment measures, recent studies particularly focus on the effects of sentiment on individual and aggregated stock returns. However, both are not conclusively clarified areas of research.

Possible topics (among others) are

• Measuring investor sentiment: alternatives to the Baket and Wurgler (2006) sentiment Index
• Investor sentiment and stock returns
• Investor sentiment and the risk-return trade-off
• Effects of investor sentiment on capital market anomalies

Basic literature

• De Long, B.J., Shleifer, A., Summers, L.H., and Waldman, R.J. (1990): Noise Trader Risk in Financial Markets. Journal of Political Economy,  98(4), 703–738.
• Baker, M. and Wurgler, J. (2006): Investor sentiment and the cross-section of stock returns. The Journal of Finance, 61(1), 1645–1680.
• Kozak, S., Nagel, S., and Shrihari, S. (2018): Interpreting Factor Models. The Journal of Finance, 73(3), 1183–1223.
• Yu, J. and Yuan, Y. (2011): Investor sentiment and the mean–variance relation. Journal of Financial Economics, 100(2), 367–381.
• Stambaugh, R.F., Yu, J., and Yuan, Y. (2012): The short of it: Investor sentiment and anomalies. Journal of Financial Economics, Special Issue on Investor Sentiment, 104(2), 288–302.

Preferences are a behavioral approach to explain the observed deviations of individual investors' behavior from the predictions of neoclassical theory. As of now, the most important theories for decision making under risk are the (Cumulative) Prospect Theory and the Salience theory.

• Portfolio insurance strategies under Cumulative Prospect Theory and Salience Theory
• The salience effect on the stock market
• Expected returns under Cumulative Prospect Theory
• Skewness preferences and security prices
• Bordalo, P., Gennaioli, N., and Shleifer, A. (2012): Salience theory of choice under risk. The Quarterly Journal of Economics, 127(3), 1243-1285.
• Tversky, A. and Kahneman, D. (1992): Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and uncertainty, 5(4), 297-323.
• Dichtl, H. and Dobritz, W. (2011): Portfolio insurance and prospect theory investors: Popularity and optimal design of capital protected financial products. Journal of Banking & Finance, 35(7), 1683-1697.
• Cosemans, M. and Frehen, R. (2017): Salience Theory and Stock Prices: Empirical Evidence. Working Paper.
• Barberis, N. and Huang, M. (2008): Stocks as Lotteries: The Implications of Probability Weighting for Security Prices. American Economic Review, 95(5), 2066-2100.
• Barberis, N., Mukherjee, A., and Wang, B. (2016): Prospect Theory and Stock Returns: An Empirical Test. Review of Financial Studies, 29(11), 3068-3107.

Kurzbeschreibung des Themenbereichs

Sustainability is progressively gaining prominence in investment considerations. Beyond purely financial factors, the inquiry emerges as to the impact of the environmental, social, and governance (ESG) dimensions on both corporations and investors, and how a company's ESG performance influences its returns.

Themenbeispiele

• Construction and analysis of an ESG pricing factor
• Estimation of the ex-ante Greenium by Implied Cost of Capital
• Measurement of "Climate Change" and Analysis of the Risk Premium of Climate Change Betas or Climate Change Risks
• Analysis of the Impact of Weather and Pollution on Stock Returns

Basisliteratur

• Pástor, Ľ., Stambaugh, R., and Taylor, L.A. (2021): Sustainable investing in equilibrium.  Journal of Financial Economics,  142(2), 550-571.
• Pástor, Ľ., Stambaugh, R. F., and Taylor, L. A. (2022): Dissecting green returns.  Journal of Financial Economics, 146(2), 403-424.
• Ardia, D., Bluteau, K., Boudt, K., and Inghelbrecht, K. (2023): Climate change concerns and the performance of green vs. brown stocks. Management Science . 
• Sautner, Z., Van Lent, L., Vilkov, G. and Zhang, R. (2023): Firm-Level Climate Change Exposure. The Journal of Finance, 78(3), 1449-1498.
• Sautner, Z., Van Lent, L., Vilkov, G. and Zhang, R. (2023): Pricing Climate Change Exposure. Management Science.
• Loughran, T. and Schultz, P. (2004): Weather, Stock Returns, and the Impact of Localized Trading Behavior. Journal of Financial and Quantitative Analysis,   39(2), 343-364.
• Ding, X., Guo, M., and Yang, T. (2021): Air pollution, local bias, and stock returns. Finance Research Letters, 39, 1-6.
• Hirshleifer, D. and Shumway, T. (2003): Good Day Sunshine: Stock Returns and the Weather. The Journal of Finance, 58(3), 1009-1032.

The literature provides numerous empirical studies that contradict the predictions of neoclassical theory. In addition to proving the existence and robustness of anomalies across markets and market regimes, examining different approaches to explain the anomalies are of particular interest and can be investigated in the context of your master thesis.

• Out-of-sample tests of selected anomalies (e.g. momentum, idiosyncratic volatility, betting-against-beta, max effect)
• Anomalies and multi-factor models
• Interaction of anomalies (e.g. skewness and momentum)
• Risk management strategies and anomalies
• Ang, A., Hodrick, R.J., Xing, Y., and Zhang, X. (2006): The cross‐section of volatility and expected returns. Journal of Finance, 61(1), 259-299.
• Jegadeesh, N. and Titman, S. (1993): Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. The Journal of Finance,  48(1), 65–91.
• Frazzini, A. and Pedersen, L.H. (2014): Betting against beta. Journal of Financial Economics, 111(1), 1–25.
• Bali, T.G., Cakici, N., and Whitelaw, R.F. (2011): Maxing out: Stocks as lotteries and the cross-section of expected returns. Journal of Financial Economics, 99(2), 427-446.
• Hou, K., Mo, H., Xue C., and Zhang, L. (2019): Which Factors?. Review of Finance, 23(1), 1-35.
• Barroso, P., Detzel, A.L., and Maio, P.F (2020): Managing the Risk of the Low-Risk anomaly. Working Paper.
• Kelly, B. T., Pruitt, S., and Su, Y. (2019). Characteristics are covariances: A unified model of risk and return.  Journal of Financial Economics , 134(3): 501–524.

Although machine learning algorithms are becoming increasingly important, they have rarely been used in empirical capital market research. Thus, the comparison of new and established methods provides numerous research questions.

• Empirical asset pricing and machine learning
• Multi factor models and artificial neural networks
• Hastie, T., Tibshirani, R., and Friedman, J. (2017): The Elements of Statistical Learning 2nd Edition. Springer Verlag.
• Gu, S., Kelly, B., and Xiu, D. (2020): Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223-2273.
• Gu, S., Kelly, B., and Xiu, D. (2021): Autoencoder asset pricing models.  Journal of Econometrics, 222(1): 429–450.
• Gareth, J., Witten, D., Hastie, T., and Tibshirani, R. (2017): An Introductoin to Statistical Learning: With Applicatoins in R. Springer Verlag, New York.
• Hou, K. and Lee, J. (2018): Nonlinear CAPM Beta. Working Paper.
• Dimson, E. (1979): Risk measurement when shares are subject to infrequent trading. Journal of Financial Economics, 7(2), 167-226.

Market prices of derivatives and, in particular, options provide rich information about market participants' expectations about the future. The elicitation of these expectations is possible via well-known option pricing models, such as Black & Scholes (1973), or numerous model-free approaches.

• Estimation of risk-neutral moments from option prices
• Option-implied risk preferences
• Market indicators of volatility and skewness: VIX and SKEW
• Risk premia for variance and skewness
• Option pricing and estimation of the volatility surface using neural networks
• Bakshi, G., Kapadia, N., and Madan, D. (2003): Stock Return Characteristics, Skew Laws, and the Differential Pricing of Individual Equity Options. Review of Financial Studies, 16(1), 101–143.
• Breeden, D.T. and Litzenberger, R.H. (1978): Prices of State-contingent Claims Implicit in Option Prices. Journal of Business, 51(4), 621-651.
• Jackwert, J. (2000): Recovering Risk Aversion from Option Prices and Realized Returns. The Review of Financial Studies, 13(2), 433-451.
• Liu, Z. and Faff, R. (2017): Hitting SKEW for SIX. Economic Modelling, (64), 449-464.
• Bollerslev, T., Tauchen, G., and Zhou, H. (2009): Expected Stock Returns and Variance Risk Premia. The Review of Financial Studies, 22(11), 4463-4492.
• Carr, P. and Wu, L. (2009): Variance risk premiums. Review of Financial Studies, 22(3), 1311-1341.

Portfolio selection is one of the classic areas of research in finance. Results not only depend on investor preferences, but also on the data generating process and the investment horizon. While neoclassical models explore the optimal portfolio choice, it is equally important to apply behavioral analyses in order to understand why many people do not engange in the stock market and how investors make portfolio choices.

• The optimal portfolio choice under ambiguity
• The optimal portfolio choice with a long investment horizon and predictability
• The influence of estimation risk on the optimal portfolio selection
• Portfolio selection under behavioral decision theories
• Participation in the stock market

Basisc literature

• Garlappi, L., Uppal, R., and Wang, T. (2007): Portfolio Selection with Parameter and Model Uncertainty: A Multi-Prior Approach.  The Review of Financial Studies, 20(1), 41-81.

DeMiguel, V., Garlappi, L., and Uppal, R. (2009): Optimal Versus Naive Diversification: How Inefficient is the 1/N Portfolio Strategy?.  The Review of Financial Studies, 22(5), 1915–1953.

• Barberis, N. (2000): Investing for the Long Run when Returns Are Predictable.  The Journal of Finance, 55, 225-264.

Chapman, D.A. and Polkovnichenko, V. (2009): First‐Order Risk Aversion, Heterogeneity, and Asset Market Outcomes.  The Journal of Finance, 64, 1863-1887.

• Grinblatt, M., Keloharju, M., and Linnainmaa, J. (2011): IQ and stock market participation.  The Journal of Finance, 66 (6), 2121-2164.
• Kaustia, M. and Torstila, S. (2011): Stock market aversion? Political preferences and stock market participation.  Journal of Financial Economics, 100(1), 98-112.
• Van Rooij, M., Lusardi, A., and Alessie, R. (2011): Financial literacy and stock market participation.  Journal of Financial Economics, 101(2), 449-472.
• Brooks, C. (2019): Introductory Econometrics for Finance. Fourth edition. Cambridge, United Kingdom ; New York, NY, Cambridge University Press.
• Malmendier, U. and Nagel, S. (2011): Depression Babies: Do Macroeconomic Experiences Affect Risk Taking?.  The Quarterly Journal of Economics, 126(1), 373–416.

Although Modigliani and Miller (1958) document that - when assuming a perfect market - capital structure is irrelevant, there are numerous studies to show that this result does not hold empirically. More recent studies, such as Baker and Wurgler (2002), show that financing decisions (and thus capital structure), in particular, depend on market timing.

• Empirical validation of theories on IPO underpricing
• Long-term performance of IPOs
• Market timing of financing decisions
• Forecast of earnings and implied cost of capital

Ritter, J. R. (1991): The long‐run performance of initial public offerings.  The Journal of Finance,   46 (1), 3-27.

• Loughran, T. and Ritter, J. R. (2002): Why don’t issuers get upset about leaving money on the table in IPOs?. The Review of Financial Studies,  15(2), 413-444.
• Ritter, J. R. and Welch, I. (2002): A review of IPO activity, pricing, and allocations.  The Journal of Finance,  57(4), 1795-1828.
• Green, T. C. and Hwang, B. H. (2012): Initial public offerings as lotteries: Skewness preference and first-day returns.  Management Science , 58(2), 432-444.
• Laeven, L. and Levine, R. (2007): Is there a diversification discount in financial conglomerates?. Journal of Financial Economics,  85(2), 331-367.
• Baker, M. and Wurgler, J. (2002): Market timing and capital structure.  The Journal of Finance,  57(1), 1-32.
• Hou, K., Van Dijk, M. A., and Zhang, Y. (2012): The implied cost of capital: A new approach.  Journal of Accounting and Economics,  53(3), 504–526.

On the following pages you will find more information about the scientific work at the Institute for Banking and Finance. Please note the formal information and the dates for the introduction to scientific work.

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Behavioral Finance – Bc. Dalibor Kováč

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Bc. Dalibor Kováč

Bachelor's thesis, behavioral finance, thesis defence.

• Supervisor: doc. Ing. Martin Svoboda, Ph.D.
• Reader: Ing. Věra Jančurová

Citation record

Iso 690-compliant citation record:, full text of thesis, contents of on-line thesis archive, other ways of accessing the text, masaryk university.

Bachelor programme / field: Finance and Accounting / Finance

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