When Capital Asset pricing Model (CAPM) is considered as valid asset pricing theory, Security Market Line (SML) is supposed to give ex-ante returns for the single period investment horizon. Since the required returns should be same as the cost of equity (discount rates) in efficient markets, SML...

In this paper, I examine whether stock return dispersion (RD) provides useful information about future stock returns. RD consistently forecasts a decline in the excess market return at multiple horizons, and compares favorably with alternative predictors used in the literature. The out-of-sample...

We analyze the joint out-of-sample predictive ability of a comprehensive set of 299 firm characteristics for cross-sectional stock returns. We develop a cross-sectional out-of-sample R2 statistic that provides an informative measure of the accuracy of cross-sectional return forecasts in terms of...

The same firm characteristics that help explain cross-sectional variation in expected stock returns, such as size, book-to-market and the earnings yield, also help explain cross-sectional variation in returns to trading in option-implied stock return volatility. This empirical phenomenon is...

In this paper we investigate the predictive power of cross-sectional volatility, skewness and kurtosis for future stock returns. Adding to the work of Maio (2016), who finds cross-sectional volatility to forecast a decline in the equity premium with high predictive power in-sample as well as...

This study investigates the impact of uncertainty on the mean-variance relationship. We find that the stock market’s expected excess return is positively related to the market’s conditional variances and implied variance during low uncertainty periods but unrelated or negatively related to...

This paper compares various machine learning models to predict the cross-section of emerging market stock returns. We document that allowing for non-linearities and interactions leads to economically and statistically superior out-of-sample returns compared to traditional linear models. Although...

Machine learning (ML) is a novel method that has applications in asset pricing and that fits well within the problem of measurement in economics. Unlike econometrics, ML models are not designed for parameter estimation and inference, but similar to econometrics, they address, and may be better...

I propose to forecast the market returns through its constituents. In contrast to the voluminous literature that concentrates on the predictive power of aggregate cross-sectional or macroeconomic predictors, I analyze the return predictability of sub-portfolios that compose the market portfolio....

We provide a short and selected review of the vast literature on cross-section predictability. We focus on the state of art methods used to forecast the cross-section of stock returns with major predictors and are primarily interested in the ideas, methods, and their applications. To understand...