UNexpected Returns: A Model Free Approach

38 Pages Posted: 3 Apr 2020

Date Written: November 19, 2019


This paper studies the partitioning of stocks into groups with distinctive expected returns based on ex-ante firm characteristics, which can be used as comparable groups to compute the abnormal part of returns, that is, UNexpected returns. In order for stock expected returns to be similar within groups and disperse across groups, I introduce a methodology to select characteristics that best distinguish expected returns, and cutoffs points where returns are most sensitive to the underlying characteristics. I show that:

1) the combination of chosen characteristics changes over time;

2) there are significant differences in fund unexpected returns once the time-variation in comparable groups is incorporated;

3) and the resulting portfolios exhibit desirable properties as basis assets.

Keywords: Portfolio Sorting, Machine Learning, Firm Characteristics, Fund Performance, Basis Assets

JEL Classification: G10

Suggested Citation

Wang, Mengchuan (Kitty), UNexpected Returns: A Model Free Approach (November 19, 2019). Available at SSRN: https://ssrn.com/abstract=3551910 or http://dx.doi.org/10.2139/ssrn.3551910

Mengchuan (Kitty) Wang (Contact Author)

Emory University ( email )

1300 Clifton Rd NE
Atlanta, GA 30322
United States
14044835207 (Phone)

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