Theses

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 (e.g., the momentum or beta anomaly).
• Test the robustness of your results with respect to combinations of selected control variables. Are your 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.

Data:

• AQR Data Library
• Kenneth French's database
• Refinitiv Datastream

Theoretical part of the task:

• Describe the term "investor sentiment" and explain ways to measure it. In particular, address methods for text-based measurement of investor sentiment, such as LLMs or dictionary-based approaches.
• Provide a review of relevant literature examining the relationship between text-based sentiment measures and stock returns.

Empirical part of the task:

• Derive a sentiment measure using a selected LLM.
• Conduct a descriptive analysis of the sentiment measure and compare it with other measures of investor sentiment.
• Analyze the relationship between your inferred sentiment measure and stock returns using regression models. Additionally, compare the explanatory power of the sentiment measure for stock returns with other measures of investor sentiment.

Basic literature:

• Baker, M. und Wurgler, J. (2006), Investor Sentiment and the Cross-Section of Stock Returns. The Journal of Finance, 61, 1645-1680.
• Devlin, J., Chang, M., Lee, K. und Toutanova, K. (2019): BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 1, 4171-4186.
• Huang, A.H., Wang, H. und Yang, Y. (2023), FinBERT: A Large Language Model for Extracting Information from Financial Text. Contemporay Accounting Research, 40, 806-841.
• 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.

Daten:

• Refinitiv Workspace
• FRED
• Loughran-McDonald Master Dictionary
• Hugging Face
• New York Times News Article

Theoretical part of the task:

• 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.

Basic literature:

• 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:

• Refinitiv Workspace
• Refinitiv Datastream
• LISS Panel

Theoretical part of the task:

• 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, based on your performance analysis, whether factor models (e.g., CAPM, Fama-French Three-Factor Model) can explain these returns.

Basic literature:

• 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.

• Explain the investor’s decision problem and the derivation of optimal portfolio weights.
• What is home bias? What are possible explanations, and are they consistent with the assumption of rational decision-makers?

• Construct equity portfolios for different countries for which the household portfolio shares (in a selected country) are known.
• Estimate the (co)variances of these portfolios.
• For plausibly parameterized expected-utility preferences, compute and compare the risk premia that lead to or rationalize the observed household versus market portfolio weights.

Basic literature:

• French, K. R. und Poterba, J. M. (1991). Investor diversification and international equity markets. Working Paper, National Bureau of Economic Research.
• Gaar, E., Scherer, D., und Schiereck, D. (2022). The home bias and the local bias: A survey. Management Review Quarterly, 72(1), 21–57.
• Longin, F. und Solnik, B. (1995). Is the correlation in international equity returns constant: 1960-1990? Journal of International Money and Finance, 14(1), 3–26.

Data:

• Bundesbank
• Refinitiv Workspace

Theoretical part of the task:

• 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.
• Short-term reversal as a proxy for overreaction: Discuss under which conditions short-term reversal could plausibly represent overreactions and when alternative explanatory mechanisms dominate. Derive hypotheses regarding which conditions influence the effect (e.g., volatility regimes, recession vs. expansion phases, sentiment).

Empirical part of the task:

• 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).
• Regime-dependent analysis for identifying short-term reversal effects in specific market environments: Can you identify different regimes?

Basic literature:

• Kelly, B., Moskowitz, T., and Pruitt, S. (2021): Understanding Momentum and Reversal. Journal of Financial Economics, 140(3), 726-743.
• 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.

Data:

• Kenneth French's database
• CRSP US Stock Database
• Refinitiv Workspace

Theoretical part of the task:

• Explain the term "air pollution" and explain how it is commonly measured in the literature.
• Explain the channels through which air pollution can have an influence on stock markets or investor behavior.

Empirical part of the task:

• Analyze the influence of air pollution on stock market-related variables such as returns, trading volume, or investor sentiment.
• Examine the correlation based on different aspects, e.g., for different markets (e.g., USA, China, Germany, etc.), for different time periods, depending on the shift from floor trading to electronic trading, or at a local level.

Basic literature:

• Ding, X., Guo, M. und Yang, T. (2021):  Air Pollution, Local Bias and Stock Returns. Finance Research Letters, 39, 101576 .
• Levi, T. und Yagil, J. (2011): Air Pollution and Stock Returns in the US. Journal of Economic Psychology, 32, 374-383.
• Li, Q. und Peng, C. H. (2016): The Stock Market Effect of Air Pollution: Evidence from China. Applied Economics, 48(36), 3442-3461.
• Lepori, G. M. (2016): Air Pollution and Stock Returns: Evidence from a Natural Experiment. Journal of Empirical Finance, 35, 25-42.
• Teng, M. und He, X. (2020): Air Quality Levels, Environmental Awareness and Investor Trading Behavior: Evidence from Stock Market in China. Journal of Cleaner Production, 244. 

Data:

• EPA AQI data
• Refinitiv Workspace
• OpenAQ

Theoretical part of the task:

• Explain why there is a potential link between emissions and the financial performance of companies.
• Present literature that examines this correlation and analyze the influence of methodological differences on the results of various studies.

Empirical part of the task:

• Analyze the impact of emissions on stock returns using various methods (e.g., portfolio construction, Fama-MacBeth regressions, or panel regressions).
• Investigate the influence of methodological variations on your results. Address, for example, the choice of emission proxies, the selection of data lags, or the difference between estimated emissions and those disclosed by the company.

Basic literature:

• Aswani, J., Raghunandan, A. & Rajgopal, S. (2024): Are Carbon Emissions Associated with Stock Returns? Review of Finance, 28(1), 75-106. 
• Bauer, M. D., Huber, D., Rudebusch, G. D. & Wilms, O. (2022): Where is the Carbon Premium? Global Performance of  Green and Brown Stocks. Journal of Climate Finance, 1. 
• Bolton, P. & Kacperczyk, M. (2021): Do investors care about carbon risk? Journal of Financial Economics, 142(2), 517-549.
• Bolton, P., Halem, Z., & Kacperczyk, M. (2022): The financial cost of carbon. Journal of Applied Corporate Finance, 34(2), 17-29.
• Bolton, P. & Kacperczyk, M. (2023): Global Pricing of Carbon-Transition Risk. Journal of Finance, 78, 3677-3754.
• Zhang, S. (2025): Carbon Returns across the Globe. Journal of Finance, 80, 615-645.

Theoretical part of the task:

• Define biodiversity risks and distinguish between physical risks and transition risks.
• Specifically illustrate the differentiation from climate risk and provide a rationale for sectoral heterogeneity.
• Aggregated risk measure: Explain the NYT Biodiversity News Index and the use of AR(1) innovations as risk shocks.
• Motivate the construction of a hedge portfolio regarding firm-specific dependency on biodiversity news.

Empirical part of the task:

• Investigate the economic effect of greater firm-/industry-specific exposure to biodiversity-loss risk using appropriate statistical methods. To this end, apply portfolio sorts and/or Fama and MacBeth (1973) regressions.
• Estimate correlations and simple time-series regressions of the resulting portfolio excess returns on the AR(1) innovations of the Biodiversity News Index.
• Optional: Climate-risk comparison—repeat the analysis using a climate-news index (e.g., the NYT Climate News index) and contrast the resulting findings.

Basic literature:

• Giglio, S., Kuchler, T., Stroebel, J., Zeng, X. (2025): Biodiversity Risk. Review of Finance.
• Dimson, E., Kacperczyk, M., & Starks, L. (2026). Biodiversity and natural resource finance. Review of Finance, 30(1), 1-9.
• Engle, R. F., Giglio, S., Kelly, B., Lee, H., Stroebel, J. (2020): Hedging Climate Change News. Review of Financial Studies.
• IPBES (2019). Summary for policymakers of the global assessment report on biodiversity and ecosystem services.

Data:

• biodiversityrisk.org
• CRSP/Compustat
• Kenneth French Data Library
• Optional: Refinitiv Workspace (ESG and complementary indicators)

Theoretical part of the task:

• Define sustainability criteria (e.g., ESG) and explain the derivation of the ESG score according to Refinitiv Workspace.
• Provide an overview of current literature on performance measurement and explain common descriptive and risk-adjusted performance measures.
• Introduce the literature on the performance measurement of sustainable companies. In doing so, address theoretical arguments as well as empirical results regarding the over- or underperformance of sustainable companies.

Empirical part of the task:

• Calculate and compare the performance measures for various sustainability categories as well as a market benchmark. Identify and interpret differences between the various categories.
• Alternatively: Independently create the Green-Minus-Brown (GMB) risk factor according to Pastor et al. (2021) from firm-level ESG data and evaluate the GMB effect on returns.

Basic literature:

• Halbritter, G. und Dorfleitner, G. (2015): The wages of social responsibility — where are they? A critical review of ESG investing. Review of Financial Economics, 26, 25-35.
• Pastor, L., Stambaugh, R. F. und Taylor, L. A. (2021): Sustainable investing in equilibrium. Journal of Financial Economics, 142, 550–571.
• Pástor, Ľ., Stambaugh, R. F., & Taylor, L. A. (2022). Dissecting green returns. Journal of Financial Economics, 146(2), 403-424.

Data:

• Kenneth French's database
• Refinitiv Workspace

Theoretical part of the task:

• 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.

Empirical part of the task:

• Analyse the Lending Club data set and present the characteristics of the loans granted there.
• Estimate the established logit model set up on the basis of the data, can defaults be forecast?

Basic literature:

• 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. 

Data:

• Lending Club personal loans, via kaggle.com

Theoretical part of the task:

• 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).

Empirical part of the task:

• 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.

Basic literature:

• 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.

Data:

• Kenneth French's database

Theoretical part of the task:

• 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.

Empirical part of the task:

• 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? 

Basic literature:

• 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.

Data:

• Kenneth French's database
• CRSP
• Refinitiv Workspace

Theoretical part of the task:

• 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.

Empirical part of the task:

• Independent calculation and empirical analysis of idiosyncratic volatility.
• Evaluate pricing effects of idiosyncratic volatility using portfolio formation and investigate whether they are significant.

Basic literature:

• 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.

Data:

• CRSP
• Refinitiv Workspace

Theoretical part of the task:

• Introduce the concept of factor models in asset pricing and explain their significance in explaining stock returns.
• Discuss the challenges of traditional factor models, particularly regarding the increasing number of factors and potential overfitting issues.
• Provide an overview of the literature on dimensionality reduction techniques and introduce different methods. Explain in detail how autoencoders function as deep neural networks for extracting latent factors. Discuss how the factor extracted via an autoencoder could theoretically provide a compact yet informative representation of the original Fama-French factors. Furthermore, reflect on potential drawbacks associated with this approach.
• Compare traditional factor models such as the CAPM and the Fama-French six-factor model with the factor extracted through the autoencoder. Discuss potential advantages of this "Neural Net Deep Factor" in explaining returns and anomaly portfolios.

Empirical part of the task:

• Conduct a descriptive analysis of the six Fama-French factors.
• Analyze their correlations and redundancies to assess whether compression is warranted.
• Train an autoencoder on the six factors and extract the latent factor from the network’s bottleneck. Visualize and interpret the characteristics of this newly derived factor.
• Compare the explanatory power of the “Neural Net Deep Factor” to traditional factor models. Test whether this factor provides better explanatory power for anomaly portfolios than the CAPM or the six-factor model. Use regression models and various model performance metrics for evaluation.

Basic literature:

• Fama, E. F., & 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., & French, K. R. (2018). Choosing factors. Journal of Financial Economics, 128(2), 234–252.
• Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning. Cambridge: MIT press.
• Gu, S., Kelly, B. T., & Xiu, D. (2020). Empirical asset pricing via machine learning. Review of Financial Studies, 33(5), 2223–2273.
• Kelly, B. T., Pruitt, S., & Su, Y. (2019). Characteristics are covariances: A unified model of risk and return. Journal of Financial Economics, 134(3), 501–524.

Data:

• Kenneth French's database

Theoretical part of the task:

• 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.

Empirical part of the task:

• Perform a descriptive analysis of the selected uncertainty measures.
• Analyze the relationship between the selected uncertainty measures and stock returns using regression models.

Basic literature:

• 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.

Data:

• Refinitiv Workspace
• FRED
• Sydney Ludvigson's database
• EPU database

• Explain what Campbell et al. (2008) refer to as the “distress anomaly” and summarize other research on the pricing of bankruptcy/default risk in equities.
• Summarize theoretical arguments for why default risk should be priced in equities.

• Estimate default risk for stocks in a panel dataset.
• Test whether you can confirm the distress anomaly using portfolio sorts and time-series regressions.
• Compare your results with the literature and discuss to what extent they align with risk-based or preference-based explanations.

• Campbell, J. Y., Hilscher, J., and Szilagyi, J. (2008). In search of distress risk. Journal of Finance, 63(6), 2899–2939.
• Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109–131.
• Shumway, T. (2001). Forecasting bankruptcy more accurately: A simple hazard model. Journal of Business, 74(1), 101–124.

• CRSP/Compustat
• Refinitiv Workspace
• Kenneth R. French Data Library

Theoretical part of the task:

• Cost of capital represents a central aspect of corporate valuation (e.g., in divestitures or mergers).
•  Standard procedures for determining the cost of capital use realized returns as an approximation for expected future returns. Implied cost of capital offers an alternative where the estimate for the cost of capital is derived implicitly and ex ante from a valuation model.
• Introduce the fundamentals of valuation. Demonstrate why the adequate choice of the cost of capital rate is of high relevance.
• Derive the cost of capital model according to Ohlson and Juettner-Nauroth (2005).
• The cost of capital model requires earnings forecasts. Explain how earnings can be estimated using the model by Hou et al. (2012). Additionally, address the advantages and disadvantages in comparison to analyst estimates.

Empirical part of the task:

• 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. 

Basic literature:

• 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.

Data:

• CDAX/STOXX Europe 600 (from Refinitiv Workspace)
• I/B/E/S Estimates

Theoretical part of the task:

• Introduce the fundamentals of the CAPM. Explain the role of beta as a measure of systematic risk and as a central driver of the cost of equity. Show why the adequate choice and estimation of beta is of high relevance for WACC and corporate valuation. 
• Derive the OLS estimator for beta from the market model regression using excess returns and discuss its statistical properties (bias, variance, confidence intervals). Address factors influencing beta estimation uncertainty, e.g., sample length, frequency (daily/weekly/monthly), choice of market index, heteroscedasticity, and autocorrelation. Optional: address intervaling and non-synchronicity effects in high-frequency data.
• Discuss time-varying betas (e.g., rolling windows) and the consequences for the stability of the cost of capital rate.
• Discuss practical decisions in the valuation process: choice of market risk premium (ex-ante/ex-post), robustness analyses, governance aspects (e.g., documentation of beta choice, consistency across projects).

Empirical part of the task:

• Empirical quantification of beta estimation uncertainty for European or American stocks (e.g., STOXX Europe 600 or S&P 500). Estimate firm-specific OLS betas using different window lengths and frequencies.
• Document the distribution of the standard errors/confidence intervals of the betas and analyze whether and how the dispersion between different beta estimators correlates with future stock returns.
• Highlight the practical implications for valuation professionals: Which ranges for WACC and corporate values should be communicated, and which beta method is recommended depending on the data situation?

Basic literature:

• Hollstein, F., Prokopczuk, M., & Simen, C. W. (2020). Beta uncertainty. Journal of Banking & Finance, 116, 105834.
• Armstrong, C. S., Banerjee, S., & Corona, C. (2013). Factor-loading uncertainty and expected returns. The Review of Financial Studies, 26(1), 158-207.
• Biggerstaff, L., Goldie, B. und Kassa, H. (2025), ‘Beta estimation precision and corporate investment efficiency’,
  Journal of Corporate Finance 91, 102728.
• Corhay, A. (1992). The intervalling effect bias in beta: A note. Journal of Banking & Finance, 16(1), 61-73.

Data:

• LSEG Workspace or
• CRSP/Compustat merged

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

1. Measuring investor sentiment: alternatives to the Baket and Wurgler (2006) sentiment Index
2. Investor sentiment and stock returns
3. Investor sentiment and the risk-return trade-off
4. 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.

Brief description of the area

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.

Possible topics (among others) are

• 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

Basic literature

• 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

1. Construction and analysis of an ESG pricing factor
2. Estimation of the ex-ante Greenium by Implied Cost of Capital
3. Measurement of "Climate Change" and Analysis of the Risk Premium of Climate Change Betas or Climate Change Risks
4. 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.

Brief description of the area

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.

Possible topics (among others) are

1. Out-of-sample tests of selected anomalies (e.g. momentum, idiosyncratic volatility, betting-against-beta, max effect)
2. Anomalies and multi-factor models
3. Interaction of anomalies (e.g. skewness and momentum)
4. Risk management strategies and anomalies

Basic literature

• 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.

Brief description of the area

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.

Possible topics (among others) are

1. Empirical asset pricing and machine learning
2. Multi factor models and artificial neural networks

Basic literature

• 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.

Brief description of the area

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.

Possible topics (among others) are

1. Estimation of risk-neutral moments from option prices
2. Option-implied risk preferences
3. Market indicators of volatility and skewness: VIX and SKEW
4. Risk premia for variance and skewness
5. Option pricing and estimation of the volatility surface using neural networks

Basic literature

• 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.

Brief description of the area

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.

Possible topics (among others) are

1. The optimal portfolio choice under ambiguity
2. The optimal portfolio choice with a long investment horizon and predictability
3. The influence of estimation risk on the optimal portfolio selection
4. Portfolio selection under behavioral decision theories
5. 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.
• 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. (2011): Depression Babies: Do Macroeconomic Experiences Affect Risk Taking?. The Quarterly Journal of Economics, 126(1), 373–416.

Brief description of the area

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.

Possible topics (among others) are

1. Empirical validation of theories on IPO underpricing
2. Long-term performance of IPOs
3. Market timing of financing decisions
4. Forecast of earnings and implied cost of capital

Basic literature

• 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.