Financial Markets and Investment Strategies
Risks and rewards play a pivotal role in theoretical and empirical research on financial markets. One of the fundamental concepts of asset pricing is that investors are rewarded for bearing risk, through higher expected returns, but that only particular, aggregate and non-diversifiable, sources of risk are priced by the market. However, the notion that security prices are always accurate (the “efficient markets hypothesis”) has been undermined by mounting evidence on incidences of mispricing, “behavioural finance”, and limits to arbitrage. This development has opened the door to idiosyncratic risk and variables related to mispricing being considered as potential determinants of expected returns. Establishing the determinants of asset prices and expected returns of securities in different asset classes remains a key challenge, and a very active area of research. Research on financial markets further considers trading on financial markets, the role of different types of (institutional) investors, the viability of different investment strategies, and also the broader role of financial markets in society.
In this project, which is an umbrella for several potential projects, a number of alternative research problems are collected that provide additional insight into the role of risk, information, and investor behaviour in financial markets. All projects are of an empirical nature and are exploring the wide range of databases that is available at Erasmus University, including data on international stock prices, institutional ownership, mutual fund and hedge fund performance. Key questions are identified and investigated using appropriate and, where necessary, innovative econometric techniques. The first year of the project will be used to get acquainted with potential supervisors and to identify concrete research topics. During the entire project, three or four papers will be written, potentially with different supervisors, which jointly constitute the PhD thesis.
Investments, asset pricing, mutual funds, hedge funds, financial risks, performance
Precisely formulated research problems and questions are to be developed during the first year of the project, but in general can be expected to center around one of the following (interrelated) themes. The discussion of the themes contain various references to recent papers by ERIM finance faculty members, which gives an indication of the type of research that ERIM finance researchers specialize in.
The question why some securities have a higher price (or a lower expected return) than others is still the quintessential question in the literature on financial markets. Over the past 60 years, the literature has developed several theoretical models (such as CAPM, APT, and ICAPM) and a host of empirical models (such as the 4-factor model of Carhart, 1997, the 4-factor model of Hou, Xue, and Zhang, 2015, and the 5-factor model of Fama and French, 2016) to answer this question. Most studies on these models are focused on stock markets. The underlying rationale (risk or mispricing) for the different factors (or anomalies) is still heavily debated. Furthermore, recent research questions the reliability of many of the papers that report anomalies in the cross-section of stock returns (e.g., Harvey, Liu and Zhu, 2016). In short, more research is needed to establish what the best way is for investors and corporations to estimate expected stock returns, or the cost of equity capital.
Investment strategies and performance
Value, size, and momentum are more or less accepted trading strategies that yield returns above what is predicted by a standard asset pricing model (CAPM). However, it is not yet established whether these patterns can be attributed to underlying risk factors. Moreover, the profitability of such strategies varies substantially over time. For example, momentum profits have been very poor since the end of the previous century. Time-varying exposures to other risk factors may drive part of the momentum returns and may also explain time-variation in performance. Apart from understanding the nature and sources of this and similar investment strategies, Interesting questions are whether improved trading strategies can be developed that are more robust over time and survive transaction costs. Also, the question arises to what extent these strategies proxy for other types of portfolio risk such as skewness or outside risk factors such as inflation.
Financial market efficiency
Financial markets serve many roles in the economy, including capital allocation, consumption smoothing, risk sharing, and aggregating information about investor beliefs. The efficacy of all of these roles depends on market prices accurately reflecting what assets are truly worth – in other words, on financial markets being efficient. Market efficiency has been the subject of a heated debate among academics and industry professionals in the past decades and remains central to the study of financial markets. Recent lines of research emphasize the importance of behavioural finance (e.g., Hirshleifer, 2001), the limits of arbitrage (e.g., Shleifer and Vishny, 1997; de Jong, Rosenthal, and van Dijk, 2009), and time- and cross-sectional variation in market efficiency (e.g., Rösch, Subrahmanyam, and van Dijk, 2017). Potential research topics include the (time-varying) market efficiency of other asset classes and of non-U.S. markets and the strength of various behavioural biases and limits to arbitrage in different markets. A closely related topic is the predictability of stock returns. There seems to be a consensus in the current literate that expected returns vary over time. But what the pattern is by which expected returns vary over time and to what extent this pattern is consistent with market efficiency is less clear (e.g., Cochrane, 2007; de Roon and Szymanowska, 2012).
Financial market liquidity
Financial markets allow companies to make long-term economic investments while allowing investors to remain liquid – that is, enabling them to convert their financial assets into cash when their consumption or income patterns require them to. The liquidity of financial markets – that is, the ease and cost of trading securities – is therefore valued by investors. This should imply that a security’s liquidity is relevant for its pricing, which has been confirmed for different asset classes (e.g., Amihud and Mendelson, 1986; Acharya and Pedersen, 2005, Bongaerts, de Jong, and Driessen, 2011, Bongaerts, de Jong, and Driessen, 2017). We also know that the liquidity of individual stocks exhibits significant co-movement within various stock markets (e.g., Chordia, Roll, and Subrahmanyam, 2000; Karolyi, Lee, and van Dijk, 2012). We know less about what role liquidity plays in the spreading of financial crises, and on whether liquidity has a direct impact on the real and financial decisions of corporations. As an example, Hanselaar, Stulz, and van Dijk (2018) show that companies raise more equity when stock markets become more liquid.
Institutional ownership and asset pricing
While asset pricing factors like the size effect and book-to-market effects are empirically well-documented, their existence is not yet well-understood. The behaviour of institutional investors, as revealed by (changes in) their stock ownership, participation in different markets (such as other international stock markets, as well as commodity and bond markets) and may help to shed some light on these findings (see, e.g., Chen, Hong, and Stein, 2002; Sias, Starks, and Titman, 2006). A possible focus is on the role of short sales and short sale constraints (e.g., Nagel, 2005). An alternative emphasis is on the question to what extent institutional holdings contain information that can be exploited in trading strategies (e.g., Wermers, Yao, and Zhao, 2012; Jiang, Verbeek, and Wang, 2014). Other approaches may look at preferences of institutional investors (for instance aversion to inflation or to portfolio skewness) and the exposure of institutional investors to other markets (such as real estate).
Mutual fund performance and investor flows
A very active literature tries to identify to what extent mutual fund managers are skilled and what separates skilled managers from unskilled ones (e.g. Huij and Verbeek, 2009, Berk and Van Binsbergen, 2015). Fund investors react to fund performance in their decision to allocate capital. In addition to the question what information signals fund investors pay attention to (e.g. Armstrong, Genc, and Verbeek, 2018), the responsiveness of fund flows to past performance, in combination with the fact that most funds operate within larger fund families, creates incentives and conflicts of interest (e.g. Del Guercio, Genc, and Tran, 2018). Further, the growth of individual funds as well as the entire fund industry, may negatively affect the potential of fund managers to find attractive investment opportunities (Pastor, Stambaugh, and Taylor, 2015, Dyakov, Jiang, and Verbeek, 2018). The interaction between fund families, fund managers and fund investors provides an interesting area for future research, involving issues like conflicts of interest, decreasing returns to scale, behavioural biases, and cross-subsidization.
Hedge funds and financial market stability
Despite their increasing importance, little is known about the impact of hedge fund expansion on financial markets. On the one hand, hedge funds often claim themselves to be arbitrageurs who provide liquidity and improve the efficiency of financial markets (see, e.g., SEC, 2003, and Garbaravicius and Dierick, 2005 for discussions on possible economic benefits of hedge funds). On the other hand, many observers argue that the expansion of hedge funds poses significant risks to the stability of the financial system and threats to the economy, see, e.g., Chan, Getmansky, Haas, and Lo (2006) and Khandani and Lo (2007). As exemplified in the recent global financial crisis, the global financial system has become more fragile and interconnected. Is the expansion of the hedge fund industry contributing to this enhanced financial fragility? The answer to this question is crucial, because it will determine whether tighter oversight and regulation of hedge funds should ensue to protect the stability of the financial and economic system. We know less about the role that liquidity plays in the spreading of financial crises and about the effects of liquidity on corporations.
Financial markets for different asset classes (and their interactions)
There is a huge literature that studies asset pricing for stock markets. The literature on the determinants of asset prices on markets for other classes - such as bonds and commodities - is considerably less-developed (see, e.g., Bongaerts, de Jong, and Driessen, 2011, and Szymanowska, de Roon, Nijman, and van den Goorbergh, 2014, for recent exceptions). Furthermore, we know even less about the relations between stock, bond and commodity markets in terms of asset pricing. These links are important though, as different types of agents are active on different markets, and links between markets (via investors) allow for better risk sharing. Questions that arise are: how (if at all) stock, bond and commodity markets are interconnected, what kind of information/shocks propagate between these markets, does the risk sharing across these markets lower the costs of hedging for corporations? Also, how do these types of relations, and their effect on asset pricing, extend to other markets such as the currency markets, markets for real estate, private equity, or even human capital?
The role of financial markets in the economy / in society
Financial markets are supposed to fulfil several key functions in the economy / for society as a whole. For example, allocating capital to its most productive use is arguably the most important social function of a financial system (e.g., Schumpeter, 1912). Financial markets are also supposed to provide liquidity and enable risk sharing. However, we know remarkably little about whether financial markets indeed fulfil these roles. Some prior work examines whether certain financial systems are better than others in allocating capital (e.g., Wurgler, 2000). However, the scope of these studies is limited and several of the social functions of financial markets have not been investigated at all. There is thus considerable scope for improving our understanding of how well these functions are fulfilled, of what we can learn from the rich variation in financial market structures and regulations across countries and over time about which system works best, and whether newly established financial markets contribute to the economy and to society in developing countries.
In general: Literature study. Data collection and cleaning, using and combining existing financial databases. Employing and/or developing econometric methodology. Empirical analyses and interpretation.
Candidates should have a strong background in econometrics, quantitative economics and/or computational economics. Experience with econometrics software and programming languages (e.g., Matlab, R, Ox) is highly recommended. Further, candidates should be fluent in English. Candidates should have a strong interest in finance, but not necessarily an MSc or MPhil degree in finance since the PhD program involves a thorough curriculum of finance courses.
The project will result in three to four articles, that will be targeted for publication in the top finance journals (ERIM P, P* list). In addition, a PhD thesis will be completed that combines these articles.
In general, this project can be expected to have important implications for both the academic literature and the asset management industry.
Armstrong, W.J., E. Genc and M. Verbeek, 2018, Going for Gold: An Analysis of Morningstar Analyst Ratings, Management Science, forthcoming.
Berk, J.B. and J.H. van Binsbergen, 2015, Measuring Skill in the Mutual Fund Industry, Journal of Financial Economics 118, 1-20.
Bongaerts, D., de Jong, F., and J. Driessen, 2011, Derivative pricing with liquidity risk: Theory and evidence from the Credit Default Swap market, Journal of Finance 66, 203-240.
Bongaerts, D., de Jong, F., and J. Driessen, 2017, An asset pricing approach to liquidity effects in corporate bond markets, Review of Financial Studies 30, 1229-1269.
Carhart, M., 1997, On persistence in mutual fund performance. Journal of Finance 50, 679-698.
Chan, N., M. Getmansky, S. Haas, and A. Lo, 2006, Systemic risk and hedge funds, in M. Carey and R. Stulz, eds., The Risks of Financial Institutions, 235-330. Chicago, IL: University of Chicago Press.
Chen, J., H. Hong, and J. Stein, 2002, Breadth of ownership and stock returns, Journal of Financial Economics 66, 171-205
Chordia, T., R. Roll, and A. Subramanyam, 2000, Commonality in liquidity, Journal of Financial Economics 56, 3-28.
Cochrane, J.H., 2007, The dog that did not bark: A defense of return predictability, Review of Financial Studies 21, 1533-1575.
de Jong, A., L. Rosenthal, and M. van Dijk, 2009, The risk and return of arbitrage in dual-listed companies, Review of Finance 13, 495-520.
De Roon, F., and M. Szymanowska, 2012, Asset pricing restrictions on predictability: Frictions matter, Management Science 58, 1916-1932.
Del Guercio, D. Genc, E, and H. Tran, 2018, Playing favorites: Conflicts of interest in mutual fund management, Journal of Financial Economics 128, 535-557.
Dyakov, T., H. Jiang, and M. Verbeek, 2018, Trade less and exit overcrowded markets: Lessons from international mutual funds, working paper, http://dx.doi.org/10.2139/ssrn.3078535.
Fama, E.F. and French, K.R., 2016, Dissecting anomalies with a five-factor model, Review of Financial Studies 29, 69-103.
Garbaravicius, T., and F. Dierick, 2005, Hedge funds and their implications for financial stability, European Central Bank Occasional Paper 34.
Hanselaar, R., Stulz, R.M. and M.A. van Dijk, 2018, Do firms issue more equity when markets become more liquid?, Journal of Financial Economics, forthcoming.
Harvey, C.R., Liu, Y. and H. Zhu, 2016, … and the cross-section of expected returns, Review of Financial Studies 29, 5-68.
Jiang, H., M. Verbeek, and Y. Wang, 2014, Information content when mutual funds deviate from benchmarks, Management Science 60, 2038-2053.
Hirshleifer, D., 2001, Investor psychology and asset pricing. Journal of Finance 56, 1533-1597.
Hou, K., Xue, C. and L. Zhang, 2015. Digesting anomalies: An investment approach, Review of Financial Studies 28, 650-705.
Huij, J. and M. Verbeek, 2009, On the use of multifactor models to evaluate mutual fund performance, Financial Management 38, 75-102.
Karolyi, G., K.-H. Lee, and M.A. van Dijk, 2012, Understanding commonality in liquidity around the world, Journal of Financial Economics 105, 82-112.
Khandani, A., and A. Lo, 2007, What happened to the quants in August 2007?, Journal of Investment Management 5, 5-54.
Nagel, S., 2005. Short sales, institutional investors and the cross section of stock returns. Journal of Financial Economics, 78, 277-309.
Pastor, L., R.F. Stambaugh, and L.A. Taylor, 2015, Scale and Skill in Active Management. Journal of Financial Economics 116, 23–45.
Rösch, D., A. Subrahmanyam, and M.A. van Dijk, 2017, The dynamics of market efficiency, Review of Financial Studies 30, 1151-1187.
Securities and Exchange Commission, 2003, Implications of the growth of hedge funds, Staff Report to the Securities Exchange Commission, Washington, D.C.
Sias, R., L. Starks, and S. Titman, 2006, Changes in institutional ownership and stock returns: Assessment and methodology, Journal of Business 79, 2869-2910.
Schumpeter, J.A., 1912, Theorie der Wirtschaftlichen Entwicklung [Theory of Economic Development] (1934 trans. Edition), Harvard University Press, Cambridge, MA.
Shleifer, A., and R. Vishny, 1997, The limits of arbitrage, Journal of Finance 52, 35-55.
Szymanowska, M., F. de Roon, T. Nijman, and R. van den Goorbergh, 2014, An anatomy of commodity futures risk premia, Journal of Finance, 69, 453-482.
Wermers, R., T. Yao, and J. Zhao, 2012, Forecasting stock returns through an efficient aggregation of mutual fund holdings, Review of Financial Studies 25, 3490-3529.
Wurgler, J., 2000, Financial markets and the allocation of capital, Journal of Financial Economics 58, 187-214.