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Three Essays on the Performance Evaluation of Actively Managed Investment Funds.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Three Essays on the Performance Evaluation of Actively Managed Investment Funds./
作者:	Yan, Qing.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	121 p.
附註:	Source: Dissertations Abstracts International, Volume: 82-12, Section: A.
Contained By:	Dissertations Abstracts International82-12A.
標題:	Finance. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28491701
ISBN:	9798516055850

Three Essays on the Performance Evaluation of Actively Managed Investment Funds.
Yan, Qing.

Three Essays on the Performance Evaluation of Actively Managed Investment Funds. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 121 p.

Source: Dissertations Abstracts International, Volume: 82-12, Section: A.

Thesis (Ph.D.)--University of Arkansas, 2021.

This item must not be sold to any third party vendors.

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.

ISBN: 9798516055850Subjects--Topical Terms:

542899
Finance.
Subjects--Index Terms:

Performance evaluation

Three Essays on the Performance Evaluation of Actively Managed Investment Funds.
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520 $a 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.
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