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    Essays on Information Asymmetry, Active Management, and Performance

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    Genre
    Thesis/Dissertation
    Date
    2016
    Author
    Stetsyuk, Ivan
    Advisor
    Elyasiani, Elyas
    Committee member
    Anderson, Ronald
    Basu, Sudipta, 1965-
    Rytchkov, Oleg
    Mansur, Iqbal
    Department
    Business Administration/Finance
    Subject
    Finance
    Egarch
    Managed Volatility
    Mutual Fund
    Real Estate
    Recession
    Return Volatility
    Permanent link to this record
    http://hdl.handle.net/20.500.12613/2462
    
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    DOI
    http://dx.doi.org/10.34944/dspace/2444
    Abstract
    Agency theory suggests that information asymmetry between mutual fund managers and mutual fund investors can be mitigated if managers are compensated for the private information that influences mutual fund risk and performance. This study investigates the role of active management in influencing returns and return volatility of mutual funds. Chapter 1 investigates whether real estate mutual funds (REMFs) outperform Carhart’s (1997) four-factor and index benchmarks using daily return data from the CRSP survivorship bias-free mutual fund database from September 1998 to December 2013. We employ generalized autoregressive conditionally heteroscedastic (GARCH) volatility models to estimate more precise alphas than those generated in the extant studies. We document that risk-adjusted alphas of actively managed REMFs are statistically and economically significant, reflecting the informational advantage and skills of active managers. We also show that actively managed REMFs outperform the real estate index benchmark (Ziman Real Estate Index) and generate a yearly buy-and-hold abnormal return of 3.64%. Active management, therefore, provides value beyond the diversification benefits that can be generated by investing into the real estate index. While active managers of REMFs generate abnormal returns (gross of expenses), they capture the entire amount themselves, sharing none with investors (net of expenses). Accordingly, the average abnormal return to investors is close to zero due to expenses associated with REMFs, such as management fees, 12b-1 fees, waivers, and reimbursements. Finally, we find that passively managed REMFs do not generate abnormal risk-adjusted alphas in Carhart’s (1997) four-factor model. Chapter 2 examines managed volatility mutual funds (MVMFs) that utilize a range of investment strategies focused on portfolio volatility. These funds have increased in popularity in the wake of the financial crisis (December 2007 to June 2009) which introduced considerable volatility into the markets. We test whether MVMFs provide better performance during periods of recessions and expansions as compared to conventional mutual funds (MFs). We obtain several interesting results. First, MVMFs underperform compared to conventional MFs by more than 2% during the entire sample period. Second, MVMFs outperform conventional MFs in recessions by over 4% annually. Third, MVMFs underperform conventional MFs by more than 2.5% during expansions. Our results suggest that MVMFs can benefit investors during periods of recessions at the cost of performing worse during expansions. Chapter 3 studies MF return volatility patterns by testing a host of hypotheses for MFs with various style objectives. To conduct the tests, we use daily returns data from the CRSP survivorship bias-free mutual fund database from September 1998 to December 2013. We examine volatility patterns across the following nine styles: Passively Managed, Actively Managed, Sector, Capitalization, Growth and Income, Income, Growth, Hedged, and Dedicated Short Bias. We employ the exponential generalized autoregressive conditionally heteroscedastic (EGARCH) volatility model. Several results are obtained. First, we show that the financial crisis of 2007-2009 had a positive or a negative impact on volatility, depending on the investment style. Second, MF volatility behavior exhibits significant cluster effects in all styles, indicating that larger return shocks lead to greater increases in return volatility. Third, shock-persistence patterns differ across various MF styles with shocks to Dedicated Short Bias MFs being the least persistent and Capitalization and Growth and Income being the most persistent. Lastly, there is considerable negative asymmetry in MF return volatility changes in response to good and bad news in the sense that negative shocks to MF returns increase volatility more than positive shocks of the same magnitude for many Actively Managed MF styles. Significant negative asymmetry of this type makes the industry vulnerable to market downturns and should be addressed by regulators, MF managers, and investors.
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