Essence of the Cross Section

Abstract

I develop a method to identify the strongest determinants of expected returns. Instead of sorting stocks on characteristics, I sort stocks into portfolios based on their realized returns---the variable of interest---at each month in the past and find the average of each characteristic among assets in each portfolio. Then I create out-of-sample portfolios such that they are as similar as possible to the returns-sorted portfolios regarding 178 characteristics. This approach separates low-mean stocks from the high-mean ones so that a long-short portfolio gains an out-of-sample monthly alpha of 1.74% (t = 13.78). Characteristics that differ between low- and high-mean stocks drive the dispersion in expected returns. I find price-based characteristics are the strongest predictors.

Keywords: asset pricing, portfolios, cross-section of expected returns, stock characteristics, machine learning, high-dimensional return predictability

JEL Classification: G10, G11, G14, C14, C11, C21, C22, C23, C58