Essays on empirical asset pricing
Name
1051454989-MIT.pdf
Description
Full printable version
Size
12.12 MB
Format
Adobe PDF
Checksum (MD5)
6d9bc7665041d4c19af7d4b0f2f8b12a
Author(s)
Chen, Yixin, Ph. D. Massachusetts Institute of Technology
Advisor(s)
Leonid Kogan and Jonathan A. Parker.
Date Issued
2018
Publisher
Massachusetts Institute of Technology
Abstract
This dissertation consists of three chapters. Chapter 1 shows that, for active mutual funds, historical in-sample alpha is a poor predictor of out-of-sample alpha. However, by focusing on a subset of skilled managers who are able to generate positive alpha via profitable bets on firm specific risks (stock-picking), I show that a new first-order stochastic dominance (FSD) condition can be employed as an additional search criterion to identify such skilled stock-pickers. I implement an FSD filter to select funds by bootstrapping the return distribution in a given period associated with a random stock-picking strategy that has a given factor exposure and degree of diversification. Simulations show that the identification of funds as skilled by the FSD filter performs well in finite samples, in the face of heteroscedasticity and benchmark mis-specification. With the new FSD filter, I identify a group of active funds that are able to outperform the Carhart benchmark by 2.04% (t=2.78) per year before fees (0.78% (t=1.07) per year after fees) out of sample. Moreover, in this sample of funds, in-sample alpha is significantly predictive of out-of-sample alpha: the top quintile of stock-picking mutual funds deliver an out-of-sample alpha of 3.55% (t=3.24) per year before fees (2.24% (t=2.05) per year after fees). These outperforming funds tend to be more aggressive stock-pickers (hold more concentrated portfolios), charge higher fees, and attract more fund flows. By exploring mutual fund managers' Herding tendency and Trading Intensity, Chapter 2 develops a systematic approach to identify mutual fund managers with the Warren Buffett style, i.e. managers who are fundamental, long-term, value investors. Using data during 1995-2015, I further show that the group of such managers outperformed the Carhart four-factor benchmark by 3.06% (t = 3.58) per year before fees (1.94% (t = 2.35) per year after fees). Moreover, these managers have both statistically and economically high exposures to AQR's Quality Minus Junk (QMJ) factor. Last but not least, I show that their before-fees performances can be almost perfectly replicated by an investor who implements the strategy of investing in the lagged portfolio holdings of these managers when they become publicly available. Chapter 3 proposes a methodology to recover countries' stochastic discount factors (SDFs) from exchange rates under three assumptions: 1) the Euler equation holds internationally; 2) there is a factor structure among exchange rates; 3) there does not exist a special global risk factor which has identical influence on all countries. By designing an empirical test using exchange rates and equity returns of 28 countries from 1988 to August of 2014, I show that the moment conditions are rejected in the data. The failure of the exchange-rate-recovered SDFs to price countries' assets reflects the violation of my assumptions, and highlights the importance of the special global risk factor to price assets in different countries.
Description
Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references.
Subjects
Sloan School of Management.
MIT Department
Sloan School of Management
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