Using data for forty markets, this paper examines the nature and possible causes of time-variation within the stock return-dividend yield predictive regression. The results in this paper show that there is significant time-variation in the predictive equation for returns and that such variation...

Traditional finance theory posits that the relationship between the risk and return of stocks is positive. Furthermore, investment practice is often based on the central contention of the Capital Asset Pricing Model (CAPM) that high (low) beta stocks earn higher (lower) returns. However, this...

Long histories of returns are needed but often lacking when estimating the equity premium. This paper studies stock return predictability from the perspective of a Bayesian investor who has access to international data. Learning across countries arises whenever this investor believes that...

This study examines the relationship between positive and negative investor sentiments and stock market returns and volatility in Group of 20 countries using various methods, including panel regression with fixed effects, panel quantile regressions, a panel vector autoregression (PVAR) model,...

We examine the relation between stock returns and profit persistence. Profit persistence is an indicator of competitive pressure or managerial ability. This, in turn, can impact firm risk and cash flow and thus stock returns. Using data on US firms, we consider panel regression at both sector...

We provide empirical evidence that the returns on US equity momentum exhibit a time-varying skewness which deepens during dramatic losses (crashes). As a result, the dynamics of the strategy expected returns reflects the time variation in both conditional volatility and skewness. This has first...

This study quantifies the presence of financial distress in the cross section of stock returns. Systemic risk is defined as the occurrence of simultaneous tail events for a large fraction of firms. A tail event is interpreted as evidence of downside risk in the tail distribution of financial...

We extract contextualized representations of news text to predict returns using the state-of-the-art large language models in natural language processing. Unlike the traditional bag-of-words approach, the contextualized representation captures both the syntax and semantics of text, thus...

This paper offers a simple yet effective way of estimating the moments of a stock's return distribution. The methodology is based on quantile regression, which is able to effectively summarize a stock's return moments by using a rich set of information about different parts of the stock's return...