Essays on Mutual Fund Performance Evaluation and Investors' Capital Allocations

dc.contributor.advisorWermers, Russen_US
dc.contributor.authorCao, Bingkuanen_US
dc.contributor.departmentBusiness and Management: Financeen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.description.abstractThe dissertation contains two chapters that studies the performance of mutual funds and investors' capital allocations. In the first chapter, I study mutual funds' portfolio management and investors' capital allocations in a unified framework under mandatory portfolio disclosure. By modeling fund managers and investors simultaneously, I show that more skill managers produce better performance by trading more actively, which causes investors to care about both fund performance and activeness when evaluating fund managers. This investor's behavior explains the convex flow-performance relation observed in the market. In addition, my model demonstrates that portfolio holdings information is more useful to investors than fund returns because portfolio holdings reveal manager activeness that is not fully captured by fund returns. My model offers three novel empirical predictions for which I find consistent evidence in the data. First, investor flows respond to both fund performance and activeness. Second, investor flows are more sensitive to the performance of illiquid holdings in the portfolio. Finally, in a diff-in-diff analysis, I show that investor flows become more sensitive to fund activeness when portfolios are disclosed more frequently. In the second chapter, I study the performance attribution of bond mutual funds. I build a comprehensive sample of U.S. actively managed bond mutual funds with a large cross section and long time series, and examine the characteristics of funds that are most associated with superior active bond fund performance. I construct several sets of covariates to measure different aspects of managerial ability, including risk management, credit analysis, activeness, beta timing, liquidity provision, and family synergy. Given the large set of covariates, I employ machine learning methods such as Boosted Regression Trees to select the best predictors of bond fund performance. Unlike equity funds, I find that risk management plays an important role in generating superior performance. In addition, funds that are better at credit analysis and charge lower fees outperform their peers.en_US
dc.subject.pquncontrolledinvestor flowsen_US
dc.subject.pquncontrolledmachine learningen_US
dc.subject.pquncontrolledmutual fundsen_US
dc.subject.pquncontrolledportfolio disclosureen_US
dc.titleEssays on Mutual Fund Performance Evaluation and Investors' Capital Allocationsen_US


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