Persistence of ex-ante volatility and the cross-section of stock returns

We suggest a new measure of total ex-ante volatility () in stock returns, which includes traditional non-market (or idiosyncratic) risk and the unexpected component of market return. We find that the portfolio-level measure exhibits strong predictive power for the cross-section of average returns during the post-1963 period. We demonstrate that (1) the persistence of gives rise to economically significant spread in returns between value and growth stocks, and (2) the cross-sectional dispersion in stock returns is positively related to the estimated value of . The benefit of the measure is that it is countercyclical and contains relevant information about the time-variation in value premium

The value premium, aggregate risk innovations, and average stock returns

We test whether innovations in aggregate risk, interpolated from a vector autoregressive system that contains the Chen, Roll and Ross (1986) five factors as in Petkova (2006), are common factors in cross-sectional stock returns. We provide direct evidence that innovation in industrial production growth, a classical business-cycle variable that summarizes the state of the economy, is associated with the cross-sectional return predictability of individual stocks. We conclude that the role of innovation in aggregate risk is not random, and furthermore that it provides guidance concerning an important source of nonfinancial market-based risk in asset returns

Risk Characterization of Firms with ESG Attributes Using a Supervised Machine Learning Method

We examine the risk–return tradeoff of a portfolio of firms that have tangible environmental, social, and governance (ESG) attributes. We introduce a new type of penalized regression using the Mahalanobis distance-based method and show its usefulness using our sample of ESG firms. Our results show that ESG companies are exposed to financial state variables that capture the changes in investment opportunities. However, we find that there is no economically significant difference between the risk-adjusted returns of various ESG-rating-based portfolios and that the risk associated with a poor ESG rating portfolio is not significantly different than that of a good ESG rating portfolio. Although investors require return compensation for holding ESG stocks, the fact that the risk of a poor ESG rating portfolio is comparable to that of a good ESG rating portfolio suggests risk dimensions that go beyond ESG attributes. We further show that the new covariance-adjusted penalized regression improves the out-of-sample cross-sectional predictions of the ESG portfolio’s expected returns. Overall, our approach is pragmatic and based on the ease of an empirical appeal