Finansiering

What determine the prices of financial assets? An equilibrium view will give that the price of an asset is determined by supply and demand. Based on a specific problem statement, a topic in asset pricing clarifies which factors affect the pricing of the financial assets. A well-known example is the classical CAPM, which implies that the expected excess return on a given asset is determined by the asset’s systematic risk relative to the market portfolio. Since the CAPM cannot explain all empirically observed phenomena this give rise to study other equilibrium models.

When individuals and households invest in financial assets it is primarily to move consumption possibilities either between different points in time (e.g. for pension savings) or from some economic scenarios to others (e.g. by investing in assets, which give a high return when the households labor income is low). The demand for financial assets is thereby closely connected to the consumption pattern of households, providing a link between prices of financial assets and the consumption of households – this is the so-called consumption based CAPM. The student is asked to derive the consumption based CAPM (CCAPM), ideally in a multiperiod setting, discuss its ability to describe stock returns based on aggregated consumption, as well as a comparison between the consumption based CAPM and the classical CAPM.

Literature (suggestions):
Cochrane, J. (2005). Asset Pricing. Princeton University Press. Dele af kapitel 1 og 21.
Munk, C. (2005). Asset pricing modeller. Kapitel 2 i bogen Udviklingslinier i finansiering redigeret af M. Christensen, Jurist- og Økonomforbundets Forlag.
Munk, C. (2013). Financial Asset Pricing Theory. Oxford University Press.

Prerequisites: "Finansiering, investering og virksomhedsstrategi".

In this topic, the focus involves an empirical view on the well-known CAPM or other asset pricing models; students wanting to focus on theory are recommended to consider the topic on C-CAPM. The CAPM is one of the most important tools for pricing an individual security or a portfolio. The purpose of this project is to help you to understand how to use this model to describe the relationship between the risk and return. Alternatively, you can for example consider the 3-factor model of Fama and French which is probably the most influential empirical asset pricing model in the last 30 years. The purpose is then to understand how to use this model to empirically describe asset returns. In all cases you are expected to run OLS (or more advanced) regressions and test the significance of the model parameters, i.e., the intercept (alpha) and the slope/”loadings” on the market, size, and value premiums. The data can be any return series that you are interested in. You can use any statistic packages for OLS regressions (e.g., Excel, R/S, Eviews, Stata, SPSS, Matlab, etc.). Finally, you need to give some economic explanations for the parameters in the model. Alternatively, you may want to use the 4-factor Carhart model, especially explaining mutual funds performance. You can also test whether CAPM and other asset pricing models can explain the expected returns of innovative financial instruments, e.g. bitcoins, ripple or other cryptocurrencies, and give some economic explanations for the results.

Literature (suggestions):
M. Brennan, ”Capital market equilibrium with divergent borrowing and lending rates”, Journal of Financial and Quantitative Analysis 6, 1971
Sharpe, W., 1964, Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk, Journal of Finance 19, 425-442
Fama, E. and K. French (1996)“Multifactor Explanations of Asset Pricing Anomalies”, Journal of Finance, 51, 55-84.
Fama, E. and K. French (2015)“A five-factor asset pricing model”, Journal of Financial Economics, 116, 1-22.
Carhart, Mark M. "On persistence in mutual fund performance." The Journal of finance 52.1 (1997): 57-82.
Campbell, J. Y., Lo, A. W. and MacKinlay, A. C., 1997, The Econometrics of Financial Markets, Princeton University Press, Chapter 5.
Sharpe, W., 1964, Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk, Journal of Finance 19, 425-442

Prerequisites: "Finansiering, investering og virksomhedsstrategi" and "Økonometri"

This topic offers an empirical perspective on factor models; students wanting to focus on theory are recommended to consider the topic on C-CAPM. For example, you can analize the 3-, or 5-factor model of Fama and French to understand how this model empirically describes asset returns. You are expected to run OLS (or more advanced) regressions and test the significance of the model parameters, i.e., the intercept (alpha) and the slope/”loadings” on the market, size, value, and possibly other premiums. The data can be any return series that you are interested in. You can use any statistic packages for OLS regressions (e.g., Excel, R/S, Eviews, Stata, SPSS, Matlab, etc.). Finally, you give some economic explanations for the parameters in the model. A second alternative is to consider the 4-factor Carhart model especially to explain mutual funds performance. In this case, mutual fund returns are the variable that you want to understand. A third, more technical alternative is to use programming (in Python, SAS, R, and similar languages) to construct the underlying factors used in the research mentioned above from the WRDS (CRSP and Compustat) dataset that the university recently made available. In this project, the main points are the execution, merging datasets, using the correct definitions, dealing with a huge amount of data, and finally comparing the resulting factors with a few benchmarks, and explaining the process in detail.

Literature (suggestions):
Fama, E. and K. French (1996) “Multifactor Explanations of Asset Pricing Anomalies”, Journal of Finance, 51, 55-84.
Fama, Eugene F., and Kenneth R. French. "A five-factor asset pricing model." Journal of financial economics 116.1 (2015): 1-22.
Carhart, Mark M. "On persistence in mutual fund performance." The Journal of finance 52.1 (1997): 57-82.
Campbell, J. Y., Lo, A. W. and MacKinlay, A. C., 1997, The Econometrics of Financial Markets, Princeton University Press, Chapter 5.
Sharpe, W., 1964, Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk, Journal of Finance 19, 425-442

Prerequisites: "Finansiering, investering og virksomhedsstrategi" and "Økonometri"

Corporate finance deals with the understanding of a firm’s financial issues. For example, how to evaluate a real investment (e.g., a drug of a biotech product or taking over another company), and when to initiate the investment. One can also consider how much debt a firm should have, what is the market value of the debt, how to signal the quality of a product or new investment to the financial market, and how to pay the executives in a firm. Some of these aspects have been touched upon in the standard undergraduate finance course (Finansiering, investering og virksomhedsstrategi or Finance), for example, the conflict between shareholders and creditors, when a firm makes an investment decision, but there are plenty of other aspects which a project can focus on.

How does production flexibility impact the value and timing of a firm’s investments? What is the impact on the value of a firm’s debt, if the firm acts strategically to transfer value from the creditors to the equity holders? Real options analysis is an eminent method to address questions like these. Specifically, real options analysis is a fundamental method to handle optimal decision making under uncertainty for so-called irreversible decisions. A Bachelor’s project should first consider how to handle real options analysis. Subsequently, the student can use this method to analyze various problems, e.g., valuation effects of production flexibility, how a firm’s debt and equity values are affected by the equity holders’ incentive to exploit opportunities, and do these incentive effects also impact the firm’s investment policy? Can debt covenants mitigate such agency problems?

Literature (suggestions):
Dixit, A.K. and R.S. Pindyck (1994). Investment under Uncertainty, Princeton University Press. Chapters 1, 2, 5, 6. Schwartz,
E. S. and C. Zozaya-Gorostiza (2003, January). Investment Under Uncertanty in Information Technology: Asquisition and Development Projects. Management Science 49 (1), 57–70.
Carlson, M., A. Fisher, and R. Giammarino (2004). Corporate Investment and Asset Price Dynamics: Implications for the Cross-section of Returns. Journal of Finance LIX, no. 6, 2577-2603.
Myers, S. (1977). Determinants of Corporate Borrowing, Journal of Financial Economics 9, 147-176.
Gertner R. and D. Scharfstein (1991). A Theory of Workouts and the Effects of Reorganization Law, Journal of Finance 46, 1189-1222.
Kahl, M. (2002). Economic Distress, Financial Distress, and Dynamic Liquidation. The Journal of Finance LVII (1), 135-168.
Anderson, R. and S. Sundaresan. (1996).Design and Valuation of Debt Contracts, Review of Financial Studies 9(1), 37-68.
Mella-Barral, P. and W. Perraudin (1997). Strategic Debt Service, Journal of Finance 52, 531-556.
Leland, H. (1998). Agency Costs, Risk Management, and Capital Structure, Journal of Finance 53, 1213-1243.
Fan H. and S. M. Sundaresan (2000). Debt Valuation, Renegotiation, and Optimal Dividend Policy, Review of Financial Studies, 13, 4, 1057-1099.
Special relevance: Students working with theory; math.econ students.
Prerequisites: "Finansiering, investering og virksomhedsstrategi". Depending on the focus, this topic can be technically demanding.

How can a firm’s financial decisions (e.g., using debt instead of equity) reveal the management’s information to the financial market? Can an entrepreneur signal the quality of his project to the financial market when choosing the funding of the project? Alternatively, students interested in game theory can try to connect some of the papers mentioned below to signaling models.

Literature (suggestions):
Leland, H.E. and D.H. Pyle. (1977). Informational asymmetries, Financial Structure, and Financial Intermediation, Journal of Finance 32, 371-387.
Myers S.C. and N.S. Majluf. (1984).Corporate Financing and Investment Decisions when Firms have Information that Investors do not have, Journal of Financial Economics 13, 187-221.
Christensen, P.O. and G.A. Feltham (2003). Economics of Accounting, Volume 1, Kluwer. kap. 13.
Koracjzyk, R., D. Lucas and R. McDonald (1992). Equity issues with time-varying asymmetric information, Journal of Financial and Quantitative Analysis 1992, vol. 27, no. 3, 397—417.
DeAgenlo, H., DeAngelo, L. and Skinner, D. (2000). Special dividends and the evolution of dividend signalling. Journal of Financial Economics, vol. 57, 309-354
Special relevance: Students working with theory; math. econ students.
Prerequisites: "Finansiering, investering og virksomhedsstrategi" or technically inclined stu¬dents who have had “Finansiering”.

Why are some firms buyers, while other firms are sellers or targets for a takeover? Some firms consider acquisitions as an opportunity to grow, while other firms acquire a target firm to avoid being taken over in the future. Apparently, there is a relationship between general movements in the market and takeover activity. What causes this?

Literature (suggestions):
Gorton, G., Kahl, M. and Rosen, R.J. (2009). Eat or Be Eaten: A Theory of Mergers and Firm Size, The Journal of Finance, Vol. 64, No. 3, pp. 1291-1344.
Harford, J. (2005). What drives merger waves? Journal of Financial Economics, Vol. 77, pp. 529-560.
Lambrecht, B.M. (2004). The timing and terms of mergers motivated by economies of scale. Journal of Financial Economics, Vol. 72, pp. 41-62.
Rhodes-Kropf, M. and Viswanathan, S. (2004). Market Valuation and Merger Waves. The Journal of Finance, Vol. 59, No. 6, pp. 2685-2718.

The result of this project will consist on an “equity research” type of document. A company can be values applying one or more valuation methods. One may obtain an intrinsic valuation, relating the value of the company to its intrinsic characteristics, its capacity to generate cash flows and the risk in the cash flow; relative valuation, estimating the value of the company by looking at the pricing of “comparable” companies relative to a common variable like earnings, cash flows, book value or sales; and/or contingent claim valuation, using option pricing models to measure the value of the company that share option characteristics. One should be comfortable retrieving information from financial statements, making sensible analysis assumptions, understanding the underlying business/environment that affects the value of the asset chosen. One should also be willing to deepen ones understanding about valuation based on, for example, the suggestions below.

Literature (suggestions):
Damodaran, Aswath. Investment valuation: Tools and techniques for determining the value of any asset. John Wiley & Sons, 2012.
Damodaran, Aswath. The dark side of valuation: Valuing young, distressed, and complex businesses. Ft Press, 2009.
Damodaran, Aswath. Damodaran on valuation. John Wiley & Sons, 2008. Pinto, Jerald E., Elaine Henry, Thomas R. Robinson, and John D. Stowe. Equity asset valuation. Vol. 27.
John Wiley & Sons, 2010. Online webcasts: http://people.stern.nyu.edu/adamodar/New_Home_Page/webcastvalonline.htm

In 2017 Nets granted their executives option compensation with a value of about 20 mio. DKK. When Nets did this again in 2019 with a value of 7 bln. DKK it became highly debated. Why do many companies give CEOs and other top executives stock-based compensation in the form of call options on the firm’s stock value? How do such options impact the (financial or investment) decisions executives make in their company? One can discuss and analyze questions like this; for example, using a binomial or Black-Scholes model setup.

Litteratur (forslag):
Kulatilaka, N. og A. J. Marcus (1994). Valuing Employee Stock Options, Financial Analysts Journal, Nov-Dec, 46-56.
Ferri, F., & Maber, D. A. (2013). Say on pay votes and CEO compensation: Evidence from the UK. Review of Finance, 17(2), 527-563.
Flor, C. R. og C. Munk (2003). Værdiansættelse af optioner i aflønningskontrakter, finans/invest 4, 22-32.
Flor, C. R. og H. Frimor (2005). Incitamenter og aktiebaseret aflønning.
Kapitel 11 i bogen Udviklingslinier i finansiering redigeret af M. Christensen, Jurist- og Økonomforbundets Forlag.
Cai, J., & Walkling, R. A. (2011). Shareholders’ say on pay: Does it create value? Journal of Financial and Quantitative Analysis, 46(2), 299-339
Core, J.E., Guay, W. R., og D. F. Larcker (2003). Executive compensation and incentives: A survey, FRBNY Economic Policy Review, 27-50.
Correa, R., & Lel, U. (2016). Say on pay laws, executive compensation, pay slice, and firm valuation around the world. Journal of Financial Economics, 122(3), 500-520.
Lambert, R (1986). Executive Effort and the Selection of Risky Projects, RAND Journal of Economics, 17: 77-88.
Hall, B. J. og K. J. Murphy (2002). Stock Options for Undiversified Executives, Journal of Accounting and Economics 33, 3-42.
Hull, J. and A. White (2004, January/February). How to Value Employee Stock Options. Financial Analysts Journal 60 (1), 114 -119.
Link: https://penge.borsen.dk/artikel/3/2213030/nets-chefer_far_tildelt_aktieoptioner_til_flere_mio_kr.html .
Link: https://finans.dk/erhverv/ECE11414519/politisk-flertal-varsler-indgreb-mod-skyhoeje-direktoerloenninger/?ctxref=forside .

The aim of this topic is to gain an understanding of the financial sector. Several different analyses can be conducted: Theoretically, it is interesting to gain knowledge of what constitutes a bank and how does a bank finance itself. Empirically, several interesting topics can be analyzed, for example, one can compare how financial organizations differ before, during and after the recent financial crisis. The starting point of either thesis is to independently review the related literature. Moreover, the student is asked to choose a certain “focus area” and discuss this area in detail. Examples of such focus area might be: What is the role of excessive leverage in the financial sector and how it could be avoided in the future? What is the role of capital regulation, which regulation exists, why, and how has it changed? What is the role of stress-testing subsequently to the financial crisis, what is stress testing, how it is done, what are possible costs and benefits?

Literature (suggestions):
Diamond, D. W., & Dybvig, P. H. (1983). Bank runs, deposit insurance, and liquidity. Journal of political economy, 91(3), 401-419.
Diamond, D. W. (1984). Financial intermediation and delegated monitoring. The review of economic studies, 51(3), 393-414
Beltratti, Andrea and René Stulz (2012). "The credit crisis around the globe: Why did some banks perform better? Journal of Financial Economics 105, 1-17.
Gropp, Reint and Heider, Florian (2010). The determinants of bank capital structure. Review of Finance 14, 587-622.
Admati, Anat R., Peter M. DeMarzo, Martin Hellwig, and Paul Pfleiderer (2013). Fallacies, Irrelevant Facts, and Myths in the Discussion of Capital Regulation: Why Bank Equity is Not Socially Expensive. Working Paper.
Berger, Allen N., and Christa H. S. Bouwman (2013). How does capital affect bank performance during financial crises? Journal of Financial Economics 109, 146-176.

Derivatives have become increasingly important in finance. Futures and options are actively traded on many exchanges throughout the world, and many different types of forward contracts, swaps, options, and other derivatives are entered in by financial institutions, fund managers, and corporate treasurers in the over-the-counter market. In particular, derivatives are widely used to control a company’s financial risk. For example, an international company can hedge its earnings in a foreign country against an unfavorable change in the exchange rate using derivatives. A Bachelor project about derivatives should have a focus on the theoretical valuation of such products under different conditions.

Is there a theoretical link between the Binomical model and the Black-Scholes model? Whereas the Binomial model models the price of the underlying asset at a finite number of times, the Black-Scholes model models the price of the underlying asset at any point in time. That is, the Binomial model is a discrete-time model, whereas the Black-Scholes model is a continuous-time model. Is it possible by increasing the number of time-periods in the Binomial model to get the same results as in the Black-Scholes model? It could be interesting to compare different approximations of the Binomial model to analyze the speed of converges to the Black-Scholes model. One can also analyze the empirical validity of the Binomial model and the Black-Scholes model.

Literature (suggestions):
Cox, J.C., Ross, S.A. og Rubinstein, M. (1979). Option Pricing: A Simplified Approach. Journal of Financial Economics 7, 229-263.
Leisen, D. og Reimer, M (1996). Binomial Models for Option Valuation - Examining and Improving Convergence, Applied Mathematical Finance 3, 319-346.
Hull, J.C. (2009) Options, Futures, and other Derivatives, kapitlerne 11-13, 17.
Merton, R.C. (1974): On the pricing of corporate debt: The risk structure of interest rates. Journal of Finance 29, 449--470
Prerequisites: "Finansiering, investering og virksomhedsstrategi".

The well-known Black-Scholes model builds on several simplifying assumptions. How do you value options under more realistic assumptions? Often it is not possible to find a “nice” closed-form solution, and you have to turn to numerical solution techniques. One example of a numerical solution procedure is Monte-Carlo simulation, where the valuation builds on many simulations of the price of the underlying asset. This method can e.g. be used to value options with stochastic volatility, American options, and more exotic options. One can also compare Monte-Carlo-valuation to other numerical methods for deriving option prices.

Literature (suggestions):
Glasserman, P. (2004). Monte Carlo Methods in Financial Engineering. Springer.
Hull, J. (2012). Options, Futures, and other Derivatives. 8. udgave, Pearson.
Hull, J. og A. White (1987). The Pricing of Options on Assets with Stochastic Volatilities. The Journal of Finance, vol. 42, no. 2, s. 281-300.
Longstaff, F.A. og E.S. Schwartz (2001). Valuing American options by simulation: a simple least-squares approach. The Review of Financial Studies, vol. 14, no. 1, s. 113-147.
Special relevance: Students working with theory; math.econ students
Prerequisites: "Finansiering, investering og virksomhedsstrategi".

When pricing corporate debt it is convenient to consider debt as a derivative of, e.g., the firm’s underlying profit flow. This so-called structural view on debt claims allows you to analyze how firm fundamentals impact debt pricing. Another view relates to a reduced form setting in which firm fundamentals are replaed by assumptions leading to intensity models of a bond’s default. Both modeling framework relate to a discussion of the probability of default as well as the recovery value at default. The topic has relations to the real options topic stated earlier, but the focus is more specifically on corporate bond pricing.

Literature (suggestions):
Dixit, A.K. and R.S. Pindyck (1994). Investment under Uncertainty, Princeton University Press. Chapters 1, 2, 5, 6.
Flor, C. R. (2019). Dynamic Corporate Finance Theory, lecture notes, University of Southern Denmark.
Anderson, R. and S. Sundaresan. (1996). Design and Valuation of Debt Contracts, Review of Financial Studies 9(1), 37-68.
Black, F. and J. Cox (1976). Valuing Corporate Securities: Some Effects of Bond Indenture Provisions. The Journal of Finance XXXI, 351–367.
Lando, D. (2004). Credit risk modeling. Princeton, New Jersey, USA: Princeton University Press.
Merton, R. C. (1974, May). On the pricing of corporate debt: The risk structure of interest rates. The Journal of Finance XXIX, 449–470.
Jarrow, R. A., D. Lando, and S. M. Turnbull (1997, Summer). A Markov Model for the Term Structure of Credit Risk Spreads. The Review of Financial Studies 10(2), 481–523.
Mella-Barral, P. and W. Perraudin (1997). Strategic Debt Service, Journal of Finance 52, 531-556.
Leland, H. (1998). Agency Costs, Risk Management, and Capital Structure, Journal of Finance 53, 1213-1243.
Fan H. and S. M. Sundaresan (2000). Debt Valuation, Renegotiation, and Optimal Dividend Policy, Review of Financial Studies, 13, 4, 1057-1099.
Special relevance: Students working with theory; math.econ students.
Prerequisites: "Finansiering, investering og virksomhedsstrategi" or technically inclined student who have had “Finansiering”. This topic will typically involve some theoretical work.

Probably one of the most important decisions many people face is the choice of a portfolio of financial assets for retirement savings. We have studied the mean-variance analysis by Markowitz in the undergraduate finance course “Finansiering, Investering og Virksomhedsstrategi”. The analysis is however based on some critical assumptions, which the interested student can take a closer look at. For example, the mean-variance analysis is a one-period model, which does not seem realistic. Investors live in a dynamic world, and hence take dynamic decisions. This is the topic in 1.4b. Another critic is that the results in the mean-variance analysis are based on the assumption that the investor knows the true model describing his investment opportunities as well as the true set of parameter estimates. In reality no one knows the either the true model or the true set of parameter estimates. What one knows is an approximation of the model and some parameters, which are estimated with estimation error. How can we deal with this type of uncertainty when modelling the investor’s optimal investment strategy? This is the topic in 1.4c.

What is the theoretical foundation of Markowitz’s portfolio choice model? The purpose of this project is to derive the efficient frontier together with a description of how investors with different types of utility functions choose their optimal portfolio of financial assets at the efficient frontier.

Literature (suggestions):
Huang, C. og R.H. Litzenberger. (1988). Foundations for Financial Economics, North-Holland, kapitel 3.
Ingersoll, J.E. (1987). Theory of Financial Decision Making, Rowman & Littlefield. Kapitel 1-4.
Prerequisites: “Finance” for Economics and Business Administration students as well as knowledge about linear algebra. This topic cannot be chosen by students having followed the course “Finansiering, investering og virksomhedsstrategi”.

What is the utility maximizing dynamic investment strategy of an individual investor? How is financial investments optimally allocated to different asset classes, e.g. stocks and bonds? How are financial investments optimally allocated to single securities within each asset class? And how do the answers to the former questions depend on e.g. risk aversion, time horizon, initial wealth, labor income, and asset price dynamics? In particular, it could be interesting to analyze if the recommendations of investment advisors are consistent with the theory of optimal investments. Another issue is that the true model, as well as the true set of parameter estimates describing the investor’s investment opportunities, is known, but what we know is an approximation of the model and some parameters which are estimated with an error. For example, the expected returns, variances, and covariances are the key inputs of every portfolio selection model. These parameters are not known a priori and are usually estimated with an error. The interested student could extend the classical mean-variance analysis to take these facts into account. In connection with this it could also be relevant to make a discussion about the Ellsberg paradox.

Literature (suggestions):
Munk, C. og C. Sørensen (2001). Skal investorer med lang investeringshorisont have større aktieandel? Finans/invest 7, 10-17.
Munk, C. (2012). Dynamic Asset Allocation. Undervisningsnote. Kapitel 6.
Grosen, A. og P.H. Nielsen (2006). Livsindkomsttankegangen vinder frem. Finans/invest 6, 2-3.
Møller, M., C. Sørensen og J.P. Nielsen (2006). Er opsparing simplere end din investeringsrådgiver tror – eller mere indviklet end lærebogen tilsiger? Finans/invest 8, 4-8.
Grosen, A. og P.H. Nielsen (2007). Human kapital – en overset post på den privatøkonomiske balance. Finans/invest 2, 2-3.
Larsen, A.L. (2007). Lønindkomstens betydning for porteføljevalget – Er du en aktie eller en obligation? Finans/invest 2, 4-9.
Garlappi, L., R. Uppal og T. Wang (2007). Portfolio Selection with Parameter and Model Uncertainty: A Multi- Prior Approach, The Review of Financial Studies, vol. 20, no. 1, 41—81.
Ellsberg, D. (1961). Risk, Ambiguity and the Savage Axioms, Quarterly Journal of Economics, vol. 75, 643— 669.
Flor, C.R. og L.S. Larsen (2014). Indledende Porteføljevalgsteori. Undervisningsnote, SDU.
Prerequisites: "Finansiering, investering og virksomhedsstrategi".

Technical analysis uses past return patterns to predict future returns. This approach is highly controversial provided that any predictability even violates the assumption of weakly efficient capital markets. The topic and different strategies have been widely discussed for stocks and stock markets. Most studies find that technical analysis is not creating value for stock investment strategies. Recent survey evidence shows that technical analysis seems to be particularly popular in currency trading. The goal of this thesis is to summarize the literature on technical analyses and to test several technical trading rules on currencies and exchange rates in an own empirical analysis.

Literature:
Brock, W.; Lakonishok, J.; LeBaron, B. (1992): Simple Technical Trading Rules and the Stochastic Properties of Stock Returns, in: Journal of Finance, Vol. 47, No. 5, pp. 1731-1764.
Lo, A. W.; Mamaysky, H.; Wang, J. (2000): Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation, in: Journal of Finance, Vol. 55, No. 4, pp. 1705-1770.
Menkhoff, L. (1997). Examining the use of technical currency analysis. In: International Journal of Finance & Economics, Vol. 2, No. 4, pp. 307-318. Park,
C. H.; Irwin, S. H. (2007): What Do we know about the Profitability of Technical Analysis? in: Journal of Economic Surveys, Vol. 21, No. 4, pp. 786-826.
Sullivan, R.; Timmermann, A.; White, H. (1999). Data‐snooping, technical trading rule performance, and the bootstrap. The Journal of Finance, Vol. 54, No. 5, pp. 1647-1691.

Literature has shown that the average mutual fund underperforms its benchmark index after costs (Malkiel (2003). However, there is some evidence that a strategy which is chasing past performance might be value creating for investors (e.g. Gruber (1996). Recently, some papers have discussed how to improve the selection of funds by developing additional measures (Cremers and Petajisto (2009)). The thesis is supposed to replicate the findings by Gruber (1996) and Malkiel (2003) either for the US, the European or the Danish market of mutual funds. As an addition, the thesis may develop and test ideas on how to improve the selection of mutual funds to make investments in it value creating to investors pursing this strategy. Literature: Cremers, K. M.; Petajisto, A. (2009): How active is your fund manager? A new measure that predicts performance. The Review of Financial Studies, Vol. 22, No. 9, pp. 3329-3365. Carhart, M. M. (1997): On Persistence in Mutual Fund Performance, in: Journal of Finance, Vol. 52, No. 1, pp. 57-82. Fama, E. F.; French, K. R. (2010): Luck Versus Skill in the Cross Section of Mutual Fund Returns, in: Journal of Finance, Vol. 65, No. 5, pp. 1915-1947. Gruber, M. (1996): Another Puzzle: The Growth in Actively Managed Mutual Funds, in: Journal of Finance, Vol. 51, No. 3, pp. 783–810. Kosowski, R.; Timmermann, A.; Wermers, R.; White, H. A. L. (2006): Can Mutual Fund "Stars" Really Pick Stocks? New Evidence from a Bootstrap Analysis, Journal of Finance, Vol. 61, No. 6, pp. 2551-2595. Malkiel, B. G. (2003): Passive Investment Strategies and Efficient Markets, in: European Financial Management, Vol. 9, No. 1, pp. 1-10.

Literature has shown that the average mutual fund underperforms its benchmark index after costs (Malkiel (2003). However, there is some evidence that a strategy which is chasing past performance might be value creating for investors (e.g. Gruber (1996). Some papers have used mutual fund flows to document that this performance chasing is done by investors (Sirri and Tufano (1998)). The thesis is supposed to replicate the findings by Sirri and Tufano (1998) for the Danish market of mutual funds and potentially discuss whether investors benefit from their strategies of performance chasing. Literature: Sirri, E. and Tufano P. (1998): Costly Search and Mutual Fund Flows, in: The Journal of Finance, Vol. 53, No. 5, pp. 1589–622. Carhart, M. M. (1997): On Persistence in Mutual Fund Performance, in: Journal of Finance, Vol. 52, No. 1, pp. 57-82. Grubert, M. (1996): Another Puzzle: The Growth in Actively Managed Mutual Funds, in: Journal of Finance, Vol. 51, No. 3, pp. 783–810