Big Data and the Cross-section of Stock Returns
Friday, Jan. 5, 2018 2:30 PM - 4:30 PM
- Chair: Svetlana Bryzgalova, Stanford University
Nonparametric Dissection of the Cross Section of Expected Stock Returns
AbstractWe propose a nonparametric method to study which characteristics provide incremental information for the cross section of expected returns. We use the adaptive group LASSO to select characteristics and to estimate how they affect expected returns nonparametrically. Our method can handle a large number of characteristics, allows for a flexible functional form, and is insensitive to outliers. Many of the previously identified return predictors do not provide incremental information for expected returns, and nonlinearities are important. Our proposed method has higher out-of-sample explanatory power compared to linear panel regressions.
A Portfolio Perspective on the Multitude of Firm Characteristics
AbstractWe investigate which characteristics matter jointly for an investor who cares not only about average returns but also about portfolio risk, transaction costs, and out-of-sample performance. We find only a small number of characteristics--six--are significant without transaction costs. With transaction costs, the number of significant characteristics increases to 15 because the trades in the underlying stocks required to rebalance different characteristics often net out. We show investors can identify combinations of characteristics with abnormal out-of-sample returns net of transaction costs that are not fully explained by the Fama and French (2015) and Hou, Xue, and Zhang (2014) factors.
The Cross-section of Risk and Return
AbstractIn the finance literature, a common practice is to create factor-portfolios by sorting on characteristics (such as book-to-market, profitability or investment) associated with average returns. The goal of this exercise is to create a parsimonious set of factor-portfolios that explain the cross-section of average returns, in the sense that the returns of these factor-portfolios span the mean-variance efficient portfolio. We argue that this is unlikely to be the case, as factor-portfolios constructed in this way fail to incorporate information about the covariance structure of returns. By using a high statistical power methodology to forecast future covariances, we are able to construct a set of portfolios which maintains the expected return, but hedges out much of the unpriced risk. We apply our methodology to hedge out unpriced risk in the Fama and French (2015) five-factors. We find that the squared Sharpe ratio of the optimal combination of the resulting hedged factor-portfolios is 2.29, compared with 1.31 for the unhedged portfolios, and is highly statistically significant.
- G1 - General Financial Markets