The efficient market hypothesis describes an efficient market as one in which investors cannot consistently predict stock returns because prices instantly reflect all the information flowing into the market. However, return predictability has been documented in many markets. This study tests the...

This paper provides global evidence supporting the hypothesis that expected return models are enhanced by the inclusion of variables that describe the evolution of book-to-market-changes in book value, changes in price, and net share issues. This conclusion is supported using data representing...

Ungeheuer and Weber (2021, UW) propose a Comove measure, the fraction of weekly stock returns that are in the same direction as the market, and document that Comove positively predicts cross-sectional stock returns. We show that Comove is strongly negatively correlated with idiosyncratic...

We examine the predictability of expected stock returns across horizons using machine learning. We use neural networks, and gradient boosted regression trees on the U.S. and international equity datasets. We find that predictability of returns using neural networks models decreases with longer...

The risk premium of stocks due to priced variance risk is summarized to two variables -- the stock-specific price of variance risk (the difference between realized and option-implied variance) and the quantity (i.e., how stock prices respond to their variance shocks) of variance risk....

This study discusses the trading behavior of foreign investors with respect to economic uncertainty in the South Korean stock market from a time-varying perspective. We employ a news-based measure of economic uncertainty along with the model of time-varying parameter vector autoregression with...

This paper introduces a new out-of-sample forecasting methodology for monthly market returns using the variance risk premium (VRP) that is both statistically and economically significant. This methodology is motivated by the `beta representation,' which implies that the market risk premium is...

This paper provides a comprehensive analysis of stock return predictability in the Indian stock market by employing both the portfolio and cross-sectional regressions methods using the data from January 1994 and ending in December 2018. We find strong predictive power of size, cash-flow-to-price...

Researchers and practitioners employ a variety of time-series processes to forecast betas, using either short-memory models or implicitly imposing infinite memory. We find that both approaches are inadequate: beta factors show consistent long-memory properties. For the vast majority of stocks,...

We investigate the cross-sectional predictability of stock returns after controlling for systematic risk factors in Taiwan. We additionally control for GDP growth, industrial production growth and inflation rate because they have a significant and negative pricing premium across stock returns...