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Minimum distance estimation of the cross section of expected stock returns

URL to cite or link to: http://hdl.handle.net/1802/33055

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Thesis (Ph. D.)--University of Rochester. William E. Simon Graduate School of Business Administration, 2017.
Lewellen (2015), building on the prior work of Haugen and Baker (1996) and Hanna and Ready (2005), showed that Fama-MacBeth regression slopes with many anomaly variables can be used to forecast returns out-of-sample. This paper proposes a different way to "combine" the anomaly variables, using the minimum distance estimator that is more efficient than the Fama-MacBeth. The method essentially weights period-by-period slopes by their estimated precisions. By substantially reducing the amount of noise in the estimates, this method allows a trading strategy that produces larger long-short portfolio spreads and alphas than those produced by the Fama-MacBeth method, when stocks are sorted by the resulting fitted values. In direct comparisons, it is also shown that such a strategy generates significant abnormal returns not spanned by the returns from the Fama- MacBeth strategy, and that the explanatory power of these return estimates is larger than that of the Fama-MacBeth estimates. The results are robust to different variable selections, time periods and rolling window lengths. The strategy also performs better while having lower transaction costs. In addition, I present an application that uses my method to generate the level, slope and curve factor model in the spirit of Clarke (2016), and show that such a three-factor model performs substantially better than his version of the factor model and also favorably to other leading factor models.
Contributor(s):
Hao Zou - Author

Robert Novy-Marx - Thesis Advisor

Primary Item Type:
Thesis
Identifiers:
Local Call No. AS38.626
LCSH Stocks--Rate of return--Mathematical models.
Language:
English
Subject Keywords:
Minimum distance; Empirical asset pricing; Factor model
Sponsor - Description:
William E. Simon Graduate School of Business Administration, University of Rochester - Doctoral fellowship
First presented to the public:
8/31/2019
Originally created:
2017
Date will be made available to public:
2019-08-31   
Original Publication Date:
2017
Previously Published By:
University of Rochester
Place Of Publication:
Rochester, N.Y.
Citation:
Extents:
Illustrations - illustrations (some color)
Number of Pages - xii, 105 pages
License Grantor / Date Granted:
Marcy Strong / 2017-10-02 15:26:05.796 ( View License )
Date Deposited
2017-10-02 15:26:05.796
Submitter:
Marcy Strong

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