Master Thesis Topics FSS 2017

Transcription

1 University of Mannheim Business School Chair of International Finance Mannheim L9, Mannheim Germany Master Thesis Topics FSS 2017 Topic R1: Attention Comovement and the Stock Market Advisor: Fabian Brunner Topic R2: Security Analysts: Leaders and Followers Advisor: Anja Kunzmann Topic R3: Determinants of performance in multi-strategy portfolios Advisor: Pavel Lesnevski (in cooperation with Allianz Global Investors) Topic R4: Behavioral biases among portfolio managers Advisor: Pavel Lesnevski (in cooperation with Allianz Global Investors) Topic R5: A Study of Market Segmentation and Liquidity of a G7 Country Yield Curve Advisor: Zorka Simon Topic R6: Central Bank Liquidity Provision The Case of Corporate Debt and the Bank of England Advisor: Zorka Simon Topic R7: New Highs and Lows, Investor Attention, and Stock Returns Advisor: Michael Ungeheuer Topic R8: The Pricing of Daily Winners and Losers: Overnight versus Intraday Returns Advisor: Michael Ungeheuer 1

2 Topic R1: Attention Comovement and the Stock Market Advisor: Fabian Brunner Prior research has shown that the selective and limited attention of investors has an impact on stock markets. For example, Da et al. (2011) show that stocks that receive a high level of attention, as measured by Google search volume, exhibit positive temporary price pressure and higher trading volume. In a different branch of literature, it has been shown that the prices of certain groups of stocks excessively comove. Assuming rational agents and perfect markets, comovement in stock returns should be entirely explained by comovement in underlying fundamental values. Barberis et al. (2005) document that stock returns of companies that are members of the S&P500 index comove more than can be explained by the common variation in underlying fundamental properties. Recently, Drake et al. (2016) connect the ideas put forward in these two different branches of literature. They construct a measure of attention comovement, examine how the variation of attention at the firm level is connected to the variation of attention at the industry and market level and investigate whether attention comovement is related to return comovement. The goal of this thesis is to build on the work of Drake et al. (2016) and examine how attention comovement is related to firm characteristics and whether attention comovement can partially explain return comovement. The student should base the analysis on a considerably larger sample and also use data on Wikipedia page views as a measure of investor attention. The empirical work requires the use of large databases, i.e. CRSP, Compustat and IBES. The databases are readily accessible for affiliates of the University of Mannheim. For the extension of the study, we will provide data on Wikipedia page views. The candidate should feel comfortable in the use of a statistical software program and econometrics (such as STATA). Barberis, Nicholas, Shleifer, Andrei and Wurgler, Jeffrey, 2005: Comovement, Journal of Financial Economics 75, pp Da, Zhi, Engelberg, Joseph and Gao, Pengtie, 2011: In Search of Attention, Journal of Finance 66, pp Drake, Michael, Jennings, Jared, Roulstone, Darren and Thornock, Jacob, 2016: The Comovement of Investor Attention, Management Science forthcoming. 2

3 Topic R2: Security Analysts: Leaders and Followers Advisor: Anja Kunzmann Security analysts are important intermediaries in financial markets. The two key outputs they produce, earnings forecasts and stock recommendations, have been in the focus of a growing literature in finance. While most studies consider either of these outputs separately, Loh and Mian (2006) attempt to link the two. They examine whether the relative superiority of an individual analyst in issuing accurate earnings forecasts is associated with a higher profitability of that analyst s stock recommendations. Their findings suggest that more accurate earnings forecasts indeed appear to facilitate superior stock recommendations. Considering the vast amount of resources that analysts employ for their earnings analyses, these findings are comforting for analysts: The effort for generating superior earnings forecasts allows them to gain a competitive advantage in making profitable stock recommendations. Over time, this advantage may permit lead analysts to become recognized for their superiority, and "followers" to free-ride on the information produced by the leaders". Welch (2000) has shown that security analysts indeed follow the actions of their colleagues by mimicking the most recent revisions of stock recommendations. In this study, the student should build on the work by Loh and Mian (2006) and extend their analyses by including the most recent data. In addition to that, the student should consider other measures besides forecast accuracy to determine the leader status of an analyst (e.g., timeliness (Cooper et al. (2001) or consistency (Hilary and Hsu (2013)), and test the quality of these measures as predictors of the profitability of stock recommendations. Another extension could be to check whether the likelihood of an analyst following the revisions of another analyst depends on the latter's history of accuracy. Access to financial markets data from CRSP, Compustat and I/B/E/S will be provided. The empirical work for this topic requires the use of statistical software (e.g. Stata), manipulation of data and the application of econometric methods. Some experience in this area would be helpful. Cooper, R. A., Day, T. E., & Lewis, C. M. (2001). Following The Leader: A Study of Individual Analysts Earnings Forecasts. Journal of Financial Economics, 61(3), Loh, R. K., & Mian, G. M. (2006). Do Accurate Earnings Forecasts Facilitate Superior Investment Recommendations?. Journal of Financial Economics, 80(2), Hilary, G., & Hsu, C. (2013). Analyst Forecast Consistency. The Journal of Finance, 68(1), 2. Jegadeesh, N., & Kim, W. (2010). Do Analysts Herd? An Analysis of Recommendations and Market Reactions. Review of Financial Studies, 23(2), Welch, I. (2000). Herding Among Security Analysts. Journal of Financial economics, 58(3),

4 Topic R3: Determinants of performance in multi-strategy portfolios Advisor: Pavel Lesnevski (in cooperation with Allianz Global Investors) Brinson, Hood, and Beebower (1986) and Brinson, Hood, and Beebower (1991) show that around 90% of return variation over time in pension funds returns are explained by asset allocation policy and only less than 10 % by market timing and security selection. Ibbotson and Kaplan (2000) confirm this finding for a sample of mutual funds. Thus, existing studies show that asset allocation policy is a prevailing determinant of the performance of pension and mutual funds. The goal of this master s thesis is to conduct a similar analysis for multi-strategy portfolios that have more space for strategy timing and strategy implementation. In the first step, a student should do a literature review. Next, a student should select appropriate benchmarks and policy portfolio weights for alternative strategies, and conduct performance decomposition along the lines of those in Brinson, Hood, and Beebower (1986) and Ibbotson and Kaplan (2000). Finally, the student is expected to develop and test further ideas. This topic will be supervised in cooperation with Allianz Global Investors (AGI). Data should be obtained from AGI and thus commuting to Frankfurt will be necessary. The travel expenses will be reimbursed by AGI. After the data is acquired, the analysis part can be conducted in any location. The empirical work for this topic requires the use of statistical software (e.g. Stata), manipulation of data and the application of econometric methods. Brinson, Gary P., L. Randolph Hood, and Gilbert L. Beebower, 1986, Determinants of Portfolio Performance, Financial Analysts Journal 51, Brinson, Gary P., L. Randolph Hood, and Gilbert L. Beebower, 1991, Determinants of Portfolio Performance II: An Update, Financial Analysts Journal 47. Ibbotson, Roger G., and Paul D. Kaplan, 2000, Does Asset Allocation Policy Explain 40, 90, or 100 Percent of Performance? Financial Analysts Journal 56, Xiong, James X, Roger G Ibbotson, Thomas M Idzorek, and Peng Chen, 2010, The Equal Importance of Asset Allocation and Active Management, Financial Analysis Journal 66,

5 Topic R4: Behavioral biases among portfolio managers Advisor: Pavel Lesnevski (in cooperation with Allianz Global Investors) Participants of capital markets tend to exhibit irrational behavior. Hirshleifer (2001) and Barberis and Thaler (2003) make a review of widespread behavioral biases. Although retail investors are believed to be more prone to such biases [Barber and Odean (2008)], portfolio managers also deviate from rational behavior [Kacperczyk and Seru (2007), Puetz and Ruenzi (2011), Cici (2012)]. The first part of this master s thesis is devoted to a literature review of common behavioral biases among portfolio managers. In the empirical part, a student should choose detectable biases and test whether these biases lead to an inferior performance and lower subsequent fund flows for a sample of European portfolio managers. This topic will be supervised in cooperation with Allianz Global Investors (AGI). Data should be obtained from AGI and thus commuting to Frankfurt will be necessary. The travel expenses will be reimbursed by AGI. After the data is acquired, the analysis part can be conducted in any location. The empirical work for this topic requires the use of statistical software (e.g. Stata), manipulation of data and the application of econometric methods. Hirshleifer, David, 2001, Investor Psychology and Asset Pricing, The Journal of Finance 56, Barberis, Nicholas, and Richard Thaler, 2003, A survey of behavioral finance, Handbook of the Economic of Finance, Puetz, Alexander, and Stefan Ruenzi, 2011, Overconfidence among professional investors: Evidence from mutual fund managers, Journal of Business Finance and Accounting 38, Kacperczyk, Marcin, and Amit Seru, 2007, Fund Manager Use of Public Information: New Evidence on Managerial Skills, The Journal of Finance 62, Barber, Brad M., and Terrance Odean, 2008, All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors, Review of Financial Studies 21, Cici, Gjergji, 2012, The Prevalence of the Disposition Effect in Mutual Funds Trades, Journal of Financial and Quantitative Analysis 47,

6 Topic R5: A Study of Market Segmentation and Liquidity of a G7 Country Yield Curve Advisor: Zorka Simon European pension funds and insurers managed more than 3.5 trillion worth of assets in For these institutions, it is crucially important to attain precise estimates of long term discount rates for their asset management and valuation of liabilities for regulatory purposes. Despite its practical importance and potential welfare consequences, modelling and examining the long end of the nominal term structure has attracted little attention in the academic literature. Driessen, Nijman and Simon (2016) aim to fill this gap by studying the differential pricing of short and long maturity bonds, especially focusing on segmentation in yields and liquidity. To address this question, they explore the channels through which segmentation and differences in liquidity along the yield curve affect the pricing of short and long maturity German nominal bonds between 2005 and In this study the candidate would replicate the analysis of Driessen et al. (2016) based on the sovereign bond market of a G7 country, e.g. Italy or the US; and compare the findings to those of the reference paper. The empirical approach consists of the following steps: first, by analyzing the pricing errors of nominal bonds, a market liquidity measure is constructed. Then, using this measure, one can show how the nature of liquidity and the drivers of yields differ across short and long maturity bonds. Replicating the findings of Driessen et al. (2016) could contribute to the policy discussion on the asset and liability valuation of pension funds and insurers. This empirical topic requires the use of Matlab s Financial Instruments Toolbox and customizing existing code for fitting interest rate curve functions. The student will be supported by the advisor in this task. Additionally, we further recommend that the candidate should feel comfortable using Stata for the regression analyses. Access to financial market data from Bloomberg or Datastream will be provided. Driessen, J., Nijman, T. E. and Simon, Z. (2016): Much ado about nothing: A study of differential pricing and liquidity of short and long term bonds, Working paper available upon request Hu, G. X., Pan, J. and Wang, J. (2013), Noise as Information for Illiquidity, The Journal of Finance, Vol. 68, pp Krishnamurthy, A., Nagel, S. and Vissing-Jorgensen, A. (2015): ECB Policies involving Government Bond Purchases: Impact and Channels, Working paper 6

7 Topic R6: Central Bank Liquidity Provision The Case of Corporate Debt and the Bank of England Advisor: Zorka Simon Despite becoming a common practice in the major economies around the world, the approach of quantitative easing and other unconventional monetary policy measures have been riven by controversy. In most economies, following the financial crisis and its subsequent economic downturn, central banks had to apply nonstandard policy measures when the target for the short-term interest rate, the main instrument of monetary policy, has hit its zero lower bound. Therefore, nonstandard policy measures, such as large asset purchase programs (LSAPs) were introduced to stimulate the economy. In these programs, the central bank uses newly created money to purchase securities in the market, often targeting selected sectors of the economy, to improve liquidity and stimulate economic activity within that segment. However, the liquidity provision and other monetary policies could have unintended consequences that might affect the public and private sectors for years to come. For instance, market interventions could encourage bond market participants to increase their inventory risk taking, or firms whose debt is being purchased as part of LASPs could tilt their capital structure towards debt due to the decreased liquidity premium of the corporate bond segment. This thesis examines the methods by which the Monetary Policy Committee of the Bank of England arrives at its decision, and takes a particular interest in exploring the effect of large scale corporate bond purchases on the liquidity and pricing of corporate debt included in these programs between 2007 and Scrutinizing these monetary policies, can we find a quantifiable effect on the liquidity premium in the secondary markets of corporate debt? Could the increased demand for corporate debt affect the funding structure or investment decisions of firms? The empirical work for this topic requires the use of statistical software (e.g. Stata), manipulation of data and the application of econometric methods. Some experience in this area would be helpful. We are going to provide data on corporate bond purchases of the Bank of England for the period of 2007 and 2013, these have to be complemented with information from Datastream, Worldscope and potential other sources. Joyce, M. (2012): Quantitative easing and other unconventional monetary policies: Bank of England conference summary, Bank of England Quarterly Bulletin, London: Bank of England. Joyce, M., Tong, M. and Woods, R. (2011): The United Kingdom's quantitative easing policy: Design, operation and impact, Bank of England Quarterly Bulletin. London: Bank of England. Joyce, Michael, Miles, David, Scott, Andrew, and Dimitri Vayanos (2012): Quantitative easing and unconventional monetary policy an introduction, The Economic Journal, 122, F271-F288. Krishnamurthy, A. and A. Vissing-Jorgensen (2011): The Effects of Quantitative Easing on Interest Rates: Channels and Implications for Policy, Brookings Papers on Economic Activity, Fall

8 Topic R7: New Highs and Lows, Investor Attention, and Stock Returns Advisor: Michael Ungeheuer Barber and Odean (2008) suggest that investor attention to certain stocks causes net buying of these stocks by retail investors, and consequently a temporary positive price impact of investor attention. They find evidence in support of increased retail investor buy-sell-imbalances for high-attention stocks. As proxies for high attention, they use extreme returns, trading volume, and news coverage. Da et al. (2011) measure investor attention more directly, using Google search volume for tickers (e.g. AAPL for Apple). They find evidence in support of the price impact and reversal pattern suggested by Barber and Odean (2008). Hence, it seems that investor attention has an effect on trading and stock returns. One potential attention-causing event for stocks is reaching the 52-week high or low. These stocks are easy to identify, since webpages provide information on stocks leaving the 52-week low-high range ( Additionally, the Wall Street Journal prints a list on each day of the US stocks that crossed the 52-week high or low during the last trading session. The New York Times provides graphical information on S&P 100 stocks' 52-week low-high price range, together with the current price. The goal of this master thesis is to analyze investor attention shocks, as well as price impact and reversal patterns caused by a stock's price leaving the 52-week low-high range. As a measure of investor attention, we will provide daily page view counts from firms' Wikipedia pages. Do these high/low-stocks receive an investor attention boost? Are they consequently overpriced? Potential extensions that could be analyzed are the following: How is this effect related to the return premium for stocks with high returns during the last year (see George and Hwang, 2004), high monthly trading volume (see Gervais et al, 2001), and the idiosyncratic volatility puzzle (for an overview, see Hou and Loh, 2016)? The empirical work for this topic requires the use of statistical software (e.g. Stata), manipulation of data and the application of econometric methods. Some experience in this area would be helpful. We are going to provide Wikipedia page view data. Barber, B.; Odean, T. (2008): All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors, The Review of Financial Studies 21 (2), pp Da, Z.; Engelberg, J.E.; Gao, P. (2011): In Search Of Attention, The Journal of Finance 66 (5), pp George, T.J.; Hwang, C.-Y. (2004): The 52-Week High and Momentum Investing, The Journal of Finance 59 (5), pp Gervais, S.; Kaniel, R.; Mingelgrin, D.H. (2001): The High-Volume Return Premium, The Journal of Finance 56 (3), pp Hou, K.; Loh, R.K. (2016): Have we solved the idiosyncratic volatility puzzle?, Journal of Financial Economics, forthcoming. 8

9 Topic R8: The Pricing of Daily Winners and Losers: Overnight versus Intraday Returns Advisor: Michael Ungeheuer Stocks ranked as daily winners or losers experience large spikes in retail investor attention, while nonranked stocks with extreme returns do not experience any change in attention (Ungeheuer, 2016). This pattern seems to be driven by daily winner and loser rankings, which are prominently published online: well as in newspapers (e.g. the Wall Street Journal or the New York Times) and on TV shows (e.g., MSNBC's Closing Bell). Consistent with the idea that retail investor attention should cause net buying of stocks by retail investors and subsequent overpricing (Barber and Odean, 2008; Da et al., 2011), Kumar et al. (2016) find that daily winners and losers experience significant increases in buy-sell imbalances and underperform otherwise similar stocks strongly during the months after being ranked. If retail investors cause a price impact and reversal pattern for daily winners and losers, one could expect a strong variation of the returns to daily winners and losers overnight versus intraday. This is because retail investors tend to place their orders after markets close, influencing overnight returns, whereas institutional investors tend to trade between open and close, influencing intraday returns (Lou et al., 2016; Bogousslavsky, 2016). The goal of this master thesis is to analyze the overnight (close-to-open) and intraday (open-to-close) returns of daily winners and losers around the days when they are ranked, as well as during the subsequent months. Is there a price impact due to attention-induced retail buying particularly in overnight returns on and directly after the ranking day? Do past daily winners and losers underperform more strongly intraday, when institutional investors trade, during the months after the ranking? The empirical work for this topic requires the use of statistical software (e.g. Stata), manipulation of data and the application of econometric methods. Some experience in this area would be helpful. Ungeheuer, M. (2016): Stock Returns and the Cross-Section of Investor Attention, Working Paper. Barber, B.; Odean, T. (2008): All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors, The Review of Financial Studies 21 (2), pp Da, Z.; Engelberg, J.E.; Gao, P. (2011): In Search Of Attention, The Journal of Finance 66 (5), pp Kumar, A.; Ruenzi, S.; Ungeheuer, M. (2016): Daily Winners and Losers, Working Paper. Lou, D.; Polk, C.; Skouras, S. (2016): A Tug of War: Overnight Versus Intraday Expected Returns, Working Paper. Bogousslavsky, V. (2016): The Cross-Section of Intraday and Overnight Returns, Working Paper. 9