Theses

We appreciate your interest in writing a thesis at the Institute of Banking and Finance. The following information provides an insight into the subject areas for Bachelor and Master theses. As part of your thesis, you not only critically examine the relevant literature, but also carry out your own quantitative analysis. This requires the use of numerical-statistical analysis software (R, Matlab, Stata). At the beginning of the semester, the institute offers an introduction to scientific work, which accompanies you when you get started in R and is therefore mandatory for all Bachelor and Master candidates. We wish you every success with your thesis.

Bachelor theses

Registration process

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 Jan Krupski.


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 topics will be held in February 2022 via Cisco WebEx. More information will be available in January. To join the session (via browser or app), please click here. Further information is available via this link (please note that this presentation will be updated soon).

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. 

Exposé

 

As soon as you have received your topic, the 14-day processing time for an proposal begins. Your synopsis covers the following aspects on a maximum of three pages in continuous text:

  • Problem definition and  objective
  • Methods, procedures, theoretical or conceptual approaches
  • Necessary data and sources for data acquisition
  • Expected knowledge gains for research and / or practice
  • Basic literature (from international, refereed journals)

Please send the proposal to your supervisor. If the proposal is convincing, the work will be registered immediately.

Bachelor theses in the field of Behavioral Finance

  • Stock price bubbles: Empirical tests and economic models

    Theoretical part of the task:

    • Describe and explain economic explanations for the emergence of price bubbles in financial markets, in particular the determination of the fundamental value.
    • Classify the terms stationarity and cointegration in these economic explanations and introduce possible test procedures, in particular unit root tests.
    • Explain the sup-ADF test according to Phillips et al (2011).
    • Address the possible weaknesses and known extensions.

     

    Empirical part of the task:

    • Analyse selected indices for stock price bubbles, using the sup-ADF test and at least one extension.
    • Compare your results with the literature and go into detail about identified price bubbles.

     

    Basic literature:

    • Phillips, P.C.B., Wu, Y. and Yu, J. (2011): Explosive Behavior in the 1990s NASDAQ: When did Exuberance Escalate Asset Values? International Economic Review, No 52 (1): 201-226.
    • Brooks, C. (2019): Introductory Econometrics for Finance. Fourth edition. Cambridge, United Kingdom; New York, NY. Cambridge University Press.

     Dates:

    • Thomson Reuters Eikon
    • Thomson Reuters Datastream
  • The influence of investor sentiment on stock returns

    Theoretical part of the task:

    • Explain "Noise Trader Theory" according to De Long et al (1990).
    • What are the characteristics of companies that are particularly strongly influenced by investor sentiment?
    • Explain the term investor sentiment. What measures have been used to investigate the influence of investor sentiment on stock returns? What hypotheses can be made for these measures of investor sentiment?

    Empirical part of the task:

    • To examine the influence of investor sentiment on simultaneous and future stock returns of small and large companies.
    • Test the robustness of your results with combinations of selected control variables. Is the empirical evidence valid for the entire period under investigation?

     

    Basic literature:

    • Baker, M. und Wurgler, J. (2006): Investor Sentiment and the Cross-Section of Stock Returns. The Journal of Finance 61(4), 1645–1680.
    • Baker, M. und 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. und Waldmann, R.J. (1990): Noise Trader Risk in Financial Markets. Journal of Political Economy 98(4), 703–738.
    • Fisher, K.L. und Statman, M. (2000): Investor Sentiment and Stock Returns. Financial Analysts Journal 56(2), 16–23.
    • Lee, W.Y., Jiang, C.X. und 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. und Thaler, R.H. (1991): Investor Sentiment and the Closed-End Fund Puzzle. The Journal of Finance 46(1), 75–109.
    • Lemmon, M. und Portniaguina, E. (2006): Consumer Confidence and Asset Prices: Some Empirical Evidence. The Review of Financial Studies, 19(4), 1499–1529.  

     

    Dates:

  • Determinants of stock market participation

    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 (selection):

      • Grinblatt, M., Keloharju, M., und Linnainmaa, J. (2011): IQ and stock market participation. The Journal of Finance, Nr. 66 (6), 2121-2164.
      • Kaustia, M., und 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. und 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, Volume 18, Issue 4, Pages 1467–1502, DOI
      • Malmendier,  U. und Nagel, S. (2019): Depression Babies: Do Macroeconomic Experiences Affect Risk Taking?*, The Quarterly Journal of Economics, Volume 126, Issue 1, Pages 373–416, DOI  

       Dates:

      • Thomson Reuters Eikon
      • Thomson Reuters Datastream
      • LISS Panel

    Bachelor theses in the field of derivatives and risk management

    • Valuation of options using the binomial model

      Theoretical part of the task:

      • Introduce the term option and distinguish in particular between European and American stock options.
      • Describe the binomial model according to Cox, Ross and Rubinstein (1979) and at least one extension, for example the trinomial model.
      • Explain the option price model according to Black & Scholes (1973) and explain the relationship between the models.

      Empirical part of the task:

      • Carry out an assessment of selected European and American options.
      • Compare the valuation of the models in relation to the number of time steps.
      • What does a hedging strategy look like?

      Basic literature (selection):

      • Black, F. and Scholes, M. (1973): The Pricing of Options and Corporate Liabilities. The Journal of Political Economy, 81(3), 637-654.
      • Cox, J.C., Ross, S.A. and Rubinstein, M. (1979): Option pricing: A simplified approach. Journal of Financial Economics, 7(3), 229-263.
      • Hull, J. (2012): Options, futures, and other derivatives (8th ed). Boston: Prentice Hall.
    • Implied volatility as an indicator of realized volatility and stock returns

      Theoretical part of the task:

      • Explain the calculation of risk-neutral moments from "out-of-the-money" put and call options according to Bakshi et al (2003).
      • Explain the concept of "realised volatility".
      • Explain the procedure for calculating the risk-neutral volatility measure "VIX" according to the CBOE.

      Empirical part of the task: 

      • Check the predictive power of the risk-neutral volatility on the "Realized Volatility" of the S&P 500 with different residual maturities.
      • Compare the predictive power of the VIX and the Realized Volatility in relation to stock returns of the S&P 500.

      Basic literature (selection):

      • 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.
      • Bali, T.G., Hu, J. and Murray, S. (2019): Option Implied Volatility, Skewness, and Kurtosis and the Cross-Section of Expected Stock Returns (SSRN Scholarly Paper No. ID 2322945). Social Science Research Network, Rochester, NY.
      • Bali, T.G. and Murray, S. (2013): Does Risk-Neutral Skewness Predict the Cross-Section of Equity Option Portfolio Returns? Journal of Financial and Quantitative Analysis 48(4), 1145–1171.

      Dates:

    • Value at Risk

      Theoretical part of the task:

      • Introduce value-at-risk (VaR) as a measure of risk.
      • Explain selected models for estimating the volatility of return time series. Also discuss the importance of assuming normally distributed returns.
      • Explain possible backtesting methods for value-at-risk.

      Empirical part of the task:

        • Implement selected models for VaR estimation using a data set of your choice.
        • Backtest these methods and compare the models based on your results.

        Basic literature (selection):

        • Bollerslev, T. (1986): Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3), 307-327.
        • Christoffersen, P. F. (1998): Evaluating interval forecasts. International Economic Review 39(4), 841-862.
        • Franke, J., Härdle, W., Hafner, C. (2003): Einführung in die Statistik der Finanzmärkte, 2. Auflage. Springer-Verlag, Berlin Heidelberg.

        Dates:

        • Thomson Reuters Eikon
        • Thomson Reuters Datastream
      • Realized Volatility Forecasting with Neural Networks

        Theoretical part of the task:

        • 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 neural networks and their estimation procedure in the context of Realized Volatility predictions.

         

        Empirical part of the task:

        • Evaluate the predictive performance of selected neural network architectures based on a chosen data set, such as daily Realized Volatility of the S&P500.
        • 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.
        • Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani (2013), An introduction to statistical learning: with applications in R. 2nd Edition, Springer.

         

        Data Resources:

        • Thomson Reuters Datastream
        • Oxford Realized Library

      Bachelor theses in the field of credit risk management

      • Forecast of personal loan defaults

        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.
            • Do you estimate the logit model set up on the basis of the data, can defaults be forecast?

              Basic literature (selection):

              • 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. 

              Dates:

            Bachelor theses in the field of Asset Pricing

            • The influence of investor sentiment on the beta anomaly

              Theoretical part of the task:

              • Describe the beta anomaly and explain possible reasons for the lack of empirical evidence of CAPM.
              • Describe and explain the "betting against beta" strategy according to Frazzini & Pedersen (2014).
              • Describe the term investor sentiment and briefly discuss ways to measure it. Explain a selected mood measure in more detail.
              • Explain the expected impact of investor sentiment on the beta anomaly. Pay particular attention to the slope of the security market line.

              Empirical part of the task:

              • Check the influence of a selected mood measure on the beta anomaly and the "betting against beta" factor.
              • Examine the effect of investor sentiment on the slope of the Security Market Line.

              Basic literature:

              • Antoniou, C., Doukas, J.A. and Subrahmanyam, A. (2016): Investor Sentiment, Beta, and the Cost of Equity Capital. Management Science 62(2): 347–367.
              • 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.
              • Frazzini, A. and Pedersen, L.H. (2014): Betting against beta. Journal of Financial Economics 111(1), 1–25.
              • 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.
              • Yu, J. and Yuan, Y. (2011): Investor sentiment and the mean–variance relation. Journal of Financial Economics 100(2), 367–381.

              Dates:

               

            • Momentum Crashes

              Theoretical part of the task:

              • Describe the momentum anomaly and explain the portfolio construction.
              • Describe the advantages and disadvantages of the momentum strategy. Make particular reference to momentum crashes.
              • Describe strategies to reduce momentum crashes according to Barosso & Santa-Clara (2015) and Daniel & Moskowitz (2016).

              Empirical part of the task:

              • Calculate for the US market the return of the momentum strategy in the period 1926-2019.
              • Replicate the strategies of Barosso & Santa-Clara (2015) and Daniel & Moskowitz (2016).
              • Examine and explain the advantages and disadvantages of the strategies.

              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.
              • 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.

              Dates:


            Masterarbeiten

            Registration procedure and organizational process

            Master theses can be written continuously at the Institute for Banking and Finance. There are no fixed registration deadlines. Nevertheless, a preparation time is necessary to find and discuss a topic, which is why you should contact us at least 4 weeks before the desired registration date.

            Please contact Jan Krupski by email with the following information:

            • Two topic preferences from the topics listed below or your own topic suggestion
            • Brief explanation of your motivation
            • Mention of the target registration date
            • Attachment of a current transcript of records

            You will then receive an e-mail from your supervisor (depending on the subject area and workload), who will arrange an appointment with you. In this appointment, we will define the question together with you, based on which you will work out a proposal.

            Exposé

            As soon as you have received your topic, the 14-day processing time for proposal begins. Your synopsis covers the following aspects on a maximum of three pages in continuous text:

            • Problem definition and goal setting
            • Methods, procedures, theoretical or conceptual approaches
            • Necessary data and sources for data acquisition
            • Expected knowledge gains for research and / or practice
            • Basic literature (from international, refereed journals)

            Then present your proposal to your supervisor. The work will then be registered immediately.

            Subject areas

            • Investor sentiment

              Brief description of the subject area

              Investor sentiment is an important component of Behavioral Finance. There are numerous studies that analyse the impact of investor sentiment on security prices. In addition to measuring investor sentiment, the effects of investor sentiment on individual and aggregated security prices are particularly interesting and not conclusively clarified fields of research.

              Examples of topics

              1. Measuring investor sentiment: alternatives to the Baket & Wurgler Sentiment Index
              2. Investor sentiment and stock returns
              3. Investor sentiment in ICAPM: risk or mispricing?
              4. Investor sentiment and the trade-off between risk and return
              5. Effects of investor sentiment on capital market anomalies

              Basic literature

              • De Long, B.J., Shleifer, A., Summers, L.H., 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.
            • Behavioural economic decision theories

              Brief description of the subject area

              Investorenpräferenzen sind ein Ansatz der Behavioral Finance, die beobachteten Abweichungen des Verhaltens individueller Investoren von den Prognosen der neoklassischen Theorie zu begründen. Als bedeutendste Entscheidungstheorien gelten die (Cumulative) Prospect Theory und die Salience Theory, welche im Rahmen der Masterarbeit näher beleuchtet werden.

              Examples of topics

              • Portfolio value hedging strategies under Cumulative Prospect Theory and Salience Theory
              • The salience effect on the stock market
              • Expected returns under Cumulative Prospect Theory
              • Skew preference 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.
            • Capital Market Anomalies

              Brief description of the subject area

              The literature provides numerous empirical studies that demonstrate contradictions to the predictions of neoclassical theory. In addition to proving the existence and robustness of the anomalies across markets and market phases, the various explanatory approaches in particular are exciting questions that can be investigated in the context of the Master's thesis.

              Examples of topics

              1. Out-of-sample tests of selected anomalies (e.g. momentum, idiosyncratic volatility, betting-against-beta, max effect)
              2. Anomalies and current 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., und Zhang, X. (2006): The cross‐section of volatility and expected returns. Journal of Finance, 61(1), 259-299.
              • Jegadeesh, N. und 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. und Pedersen, L.H. (2014): Betting against beta. Journal of Financial Economics 111(1), 1–25.
              • Bali, T.G., Cakici, N. und 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. und Zhang, L. (2019): Which Factors?. Review of Finance 23(1), 1-35.
              • Barroso, P., Detzel, A.L. und Maio, P.F (2020): Managing the Risk of the Low-Risk anomaly. Working Paper.
            • Machine Learning Methods in Asset Pricing

              Brief description of the subject area

              While machine learning algorithms are becoming more and more the focus of public attention, their applications in empirical capital market research have so far been rare. The comparison of new approaches with established methods provides numerous possible questions.

              Examples of topics

              1. Empirical Asset Pricing with Machine Learning

               

              Basic literature

              • Hastie, T., Tibshirani, R. and Friedman, J. (2017): The Elements of Statistical Learning 2nd Edition. Springer Verlag.
              • Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223-2273.
              • 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.
            • Options

              Brief description of the subject area

              The market prices of derivatives and in particular options provide a wealth of information about market participants' expectations about the future. The extraction of these expectations can be based on well-known option price models such as Black & Scholes (1973) or model-free methods.

              Examples of topics

              1. Estimation of risk-neutral moments from option prices
              2. Option-implicit risk preferences
              3. Market indicators of volatility and skewness: VIX and SKEW
              4. Risk premiums for variance and skewness

              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., und Wu, L. (2009): Variance risk premiums. Review of Financial Studies, 22(3), 1311-1341.
            • Portfolio selection

              Brief description of the subject area

              Portfolio selection is one of the classic areas of investigation in the financial industry. Results depend on investor preferences, data generating process and, if applicable, investment horizon. While neoclassically motivated models explore the optimal portfolio choice, behavioural economic analyses also need to understand the frequent non-participation in the stock market and actual portfolio movements of investors.

              Examples of topics

              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 behavioural economic decision theories
              5. Participation in the stock market

              Basisc literature

              • Garlappi, L., Uppal, R., 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., 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., Linnainmaa, J. (2011): IQ and stock market participation. The Journal of Finance, 66 (6), 2121-2164.
              • Kaustia, M., 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., Alessie, R. (2011): Financial literacy and stock market participation. Journal of Financial Economics, 101(2), 449-472.
              • Brooks, Chris. Introductory Econometrics for Finance. Fourth edition. Cambridge, United Kingdom ; New York, NY, Cambridge University Press, 2019.
              • Polkovnichenko, V. (2005): Household Portfolio Diversification: A Case for Rank-Dependent Preferences, The Review of Financial Studies, 18(4), 1467–1502.
              • Malmendier, U., Nagel, S. (2011): Depression Babies: Do Macroeconomic Experiences Affect Risk Taking?, The Quarterly Journal of Economics, 126(1), 373–416.
            • Corporate Finance

              Brief description of the subject area

              Entscheidungen Corporate Finance ... Results depend on investor preferences, data-generating process and, if applicable, investment horizon. While neoclassically motivated models explore the optimal portfolio choice, behavioural economic analyses also need to understand the frequent non-participation in the stock market and actual portfolio movements of investors.

              Examples of topics

              1. Empirical validation of theories on IPO underpricing
              2. Long-term performance of IPOs
              3. Market timing of financing decisions
              4. The puzzle of teeing off conglomerates

              Basic literature

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

              • Loughran, T., 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., Welch, I. (2002). A review of IPO activity, pricing, and allocations. The Journal of Finance, 57(4), 1795-1828.
              • Green, T. C., Hwang, B. H. (2012). Initial public offerings as lotteries: Skewness preference and first-day returns. Management Science, 58(2), 432-444.
              • Laeven, L.,  Levine, R. (2007). Is there a diversification discount in financial conglomerates? Journal of Financial Economics, 85(2), 331-367.
              • Baker, M., Wurgler, J. (2002). Market timing and capital structure. The Journal of Finance57(1), 1-32.

            General information on final theses

            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.

            Contact for general questions about theses

            M.Sc. Jan Krupski
            Research Staff
            Office hours
            by appointment
            Address
            Königsworther Platz 1
            30167 Hannover
            Building
            Room
            153
            M.Sc. Jan Krupski
            Research Staff
            Office hours
            by appointment
            Address
            Königsworther Platz 1
            30167 Hannover
            Building
            Room
            153