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Author: Qing Yan
Publisher:
ISBN:
Category :
Languages : en
Pages : 240
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Book Description
This dissertation investigates the performance of hedge funds and actively managed U.S. equity mutual funds. The first chapter examines the relation between hedge funds and the low beta anomaly. Different conditions in the mutual fund and hedge fund industries should lead to different approaches with respect to the low beta anomaly. I find that, unlike most mutual funds, the average hedge fund tends to benefit considerably from the anomaly. About 2.3% per year of apparent alpha for the average hedge fund can be attributed to the low beta anomaly rather than manager skill. Low skill managers are the most reliant on the anomaly to generate returns, with the most reliant underperforming the least reliant by 5.9% per year. The second chapter uses machine learning to dynamically identify and optimally combine the predictors of hedge fund performance. The portfolio formed based on the machine learning models has an out-of-sample alpha of 7.8% per year. The importance of each predictor varies over time, but among the 22 predictors I consider, the consistently important predictors are average return, maximum return, alpha, systematic risk, and beta activity. Machine learning provides valuable, unique information about future hedge fund performance that is not captured by individual predictors. The third chapter studies whether the quality of fund risk management can predict fund performance. I find that the risk management skills of mutual fund managers-as quantified by their funds' maximum drawdowns-are persistent and predictive of subsequent risk-adjusted performance. Funds with relatively strong past performance and relatively low past maximum drawdowns have, on average, an out-of-sample alpha of 2.68% per year. That alpha is magnified when markets are turbulent-a time during which risk management skills should be most valuable. Investors are averse to drawdown risk. After controlling for typical measures of past performance, fund flows are still a decreasing function of maximum drawdowns, particularly among investors with greater risk aversion and during times of generally heightened risk aversion.
Author: Qing Yan
Publisher:
ISBN:
Category :
Languages : en
Pages : 240
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Book Description
This dissertation investigates the performance of hedge funds and actively managed U.S. equity mutual funds. The first chapter examines the relation between hedge funds and the low beta anomaly. Different conditions in the mutual fund and hedge fund industries should lead to different approaches with respect to the low beta anomaly. I find that, unlike most mutual funds, the average hedge fund tends to benefit considerably from the anomaly. About 2.3% per year of apparent alpha for the average hedge fund can be attributed to the low beta anomaly rather than manager skill. Low skill managers are the most reliant on the anomaly to generate returns, with the most reliant underperforming the least reliant by 5.9% per year. The second chapter uses machine learning to dynamically identify and optimally combine the predictors of hedge fund performance. The portfolio formed based on the machine learning models has an out-of-sample alpha of 7.8% per year. The importance of each predictor varies over time, but among the 22 predictors I consider, the consistently important predictors are average return, maximum return, alpha, systematic risk, and beta activity. Machine learning provides valuable, unique information about future hedge fund performance that is not captured by individual predictors. The third chapter studies whether the quality of fund risk management can predict fund performance. I find that the risk management skills of mutual fund managers-as quantified by their funds' maximum drawdowns-are persistent and predictive of subsequent risk-adjusted performance. Funds with relatively strong past performance and relatively low past maximum drawdowns have, on average, an out-of-sample alpha of 2.68% per year. That alpha is magnified when markets are turbulent-a time during which risk management skills should be most valuable. Investors are averse to drawdown risk. After controlling for typical measures of past performance, fund flows are still a decreasing function of maximum drawdowns, particularly among investors with greater risk aversion and during times of generally heightened risk aversion.
Author: Claudia Peitzmeier
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Steve A. Nenninger
Publisher:
ISBN:
Category :
Languages : en
Pages : 88
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Book Description
This dissertation examines mutual fund performance from the points of view of three distinct, but interrelated parties: individual investors, financial advisors, and the boards of directors of mutual fund companies. In the first essay, the flow-performance sensitivity of no-load funds and the three main classes of load fund shares are compared, assuming investment advisors are more likely to guide the decision-making process of load fund investors. In the second essay, the timing of the decision to replace fund managers is examined. In the third essay, performance of actively managed mutual funds are separately examined during good and bad states of the market to test whether mutual funds perform differently under different market conditions.
Author: Alexander Pütz
Publisher:
ISBN: 9783832531898
Category : Index mutual funds
Languages : en
Pages : 0
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Book Description
Institutional investors such as mutual funds and hedge funds play an important role in today's financial markets. This thesis consists of three essays which empirically study the behavior of active fund managers. In particular, the first essay investigates whether managers behave rationally or if some of them unconsciously make wrong investment decisions due to behavioral biases. The second essay examines whether some managers intentionally act to solely advance their own interests by strategically valuing the security positions in their portfolio. The third essay analyzes what the managers' education reveals about their investment behavior.
Author: Yu Xia
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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"This thesis consists of two essays on evaluating mutual fund performance and its predictability. In the first essay, I study the ex ante predictability of 12 well-known predictors for fund performance from investors' perspective. The 12 predictors cover three major categories: fund characteristics, fund performance, and holding-based activeness measures, which are constructed using real-time information. For performance evaluation, I exploit two types of fund picking strategies with either rule-based approach or machine learning methods and find that utilizing machine learning can deliver superior real-time economic gains for investors with fund short-term performance being the primary driver underlying predictability. Specifically, using variable selection methods such as LASSO and elastic net at individual predictor level can generate annual 1.3%-1.7% real-time alphas after adjusting for standard risk factors. The essay further examines whether real-world investors react to those well-known predictors when evaluating mutual fund performance. Using a novel approach to decomposing fund returns, I find that conditional on investors' usage of CAPM, investors react to the components of CAPM alpha implied by predictors in different ways, and investor reaction to predictive information embedded in predictors is stronger within aggressive growth funds. These results provide empirical support for Gârleanu and Pedersen (2018) and suggest ex ante predictability exists not due to lack of investor reaction but as the compensation for employing costly algorithms to identify skilled managers.The second essay examines how decision-making hierarchy in team-managed U.S. equity mutual funds affects their performance and risk-taking behavior. Employing a unique hand-collected dataset, we find that vertically-managed funds with lead managers earn 75 bps per year lower Fama-French five-factor alpha than their horizontally-managed counterparts. Moreover, vertically-managed funds hold less concentrated portfolios and are exposed to lower residual risk, thus showing signs of inferior security selection ability. Using mutual fund industry as a laboratory, the second essay provides evidence supporting a horizontal decision-making structure in organizations functioning in an uncertain expectation environment. These results echo similar mechanisms as in recent cross-country studies on the benefits of democratic form of government for country's economic growth"--
Author: George O. Aragon
Publisher: Now Publishers Inc
ISBN: 1601980825
Category : Financial risk management
Languages : en
Pages : 123
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Book Description
This paper provides a review of the methods for measuring portfolio performance and the evidence on the performance of professionally managed investment portfolios. Traditional performance measures, strongly influenced by the Capital Asset Pricing Model of Sharpe (1964), were developed prior to 1990. We discuss some of the properties and important problems associated with these measures. We then review the more recent Conditional Performance Evaluation techniques, designed to allow for expected returns and risks that may vary over time, and thus addressing one major shortcoming of the traditional measures. We also discuss weight-based performance measures and the stochastic discount factor approach. We review the evidence that these newer measures have produced on selectivity and market timing ability for professional managed investment funds. The evidence includes equity style mutual funds, pension funds, asset allocation style funds, fixed income funds and hedge funds.
Author: Fan Hu
Publisher:
ISBN:
Category :
Languages : en
Pages : 238
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Author: Ulf Herrmann
Publisher:
ISBN:
Category :
Languages : en
Pages : 128
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Author: Bernhard Breloer
Publisher:
ISBN:
Category :
Languages : en
Pages : 115
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Author: Andrew John Caffrey
Publisher:
ISBN:
Category : Financial risk
Languages : en
Pages : 178
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Book Description
This dissertation consists of three essays on the relations among investors, mutual funds, and fund families. Chapter one presents a model of new fund openings as a function of the past performance of a family's existing funds. At the fund level, we model the relations among fund performance, investment flows, and the risk-taking behavior of the fund manager. Our model predicts that families dominated either by outperforming funds or by underperforming funds are more likely to open a new fund than are families composed of average performers. We predict that an asymmetric performance-fund flow relation combined with expected intra-family flows from existing underperformers to a new fund provide an incentive for families with severely under-performing funds to open a new fund in hopes of managing a `star'. Chapter two presents an empirical analysis of new fund openings. We study fund performance, investment flows, and risk level and examine the relation between the distribution of performance across funds within a family and new fund openings. We find that new fund openings are positively correlated with measures of both extreme underperformance and extreme outperformance of existing funds as well as measures of the number of `dog' funds within a family. The evidence supports our predictions in Chapter 1. Chapter three addresses the relation between advisory firm organization and mutual fund performance and expenses. Specifically, we hypothesize three relations. First, the ownership structure of a fund family--mutualized, privately held, or publicly owned--may impact fund manager behavior and be reflected in expenses and/or performance. Second, fund families may experience some net pecuniary benefit or harm as a result of subsidiary affiliation. Finally, we examine expense and performance differences across directly advised versus subadvised funds. We find evidence that publicly owned fund families provide investors with lower style-adjusted returns and alpha at higher cost than do privately owned or mutualized families. Similarly, we find that bank and insurance affiliates underperform their peers in both returns net of expenses and alpha net of expenses, and that diversified financial services affiliates outperform in these measures.
