This paper provides new evidence on the pricing of market skewness risk by incorporating investor sentiment in the relation between sensitivity to innovations in implied market skewness and expected stock returns. Using both univariate and multivariate specifications, we conduct an extensive...

We examine whether real-time return forecasts are valuable to an investor looking to allocate their portfolio across a wide selection of countries. We expand the Sum-of-Parts (SoP) method for forecasting stock returns to an international setup by adding FX returns as an additional component. We...

This paper examines to what extent the momentum spread ratio (MSR) can predict momentum profits. The momentum spread ratio as a potential proxy of investor underreaction can significantly predict the momentum, industry momentum, and residual momentum, especially after 1994, suggesting that...

This study investigates the impact of investor sentiment on excess equity return forecasting. A high (low) investor sentiment may weaken the connection between fundamental economic (behavioral-based non-fundamental) predictors and market returns. We find that although fundamental variables can...

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...

Australia has become one of most prolific issuers of seasoned equity offerings (SEOs) globally. Due to its convenience, firms issue SEOs as their primary capital raising mechanism particularly during economic disruptions i.e., the early 2000s dot-com bubble, 2008 Global Financial Crisis and...

The literature argues that news coverage predicts future stock returns because it drives attention flows through a behavioral channel. We show that the behavioral channel only accounts for half of the predictive power. The other half is explained by a novel rational channel that posits that...

Purpose: In this paper we try to explain US stock market variations and cash flow fundamentals by employing three different book-valued based ratios, First, we explore the explanatory capacity of the simple book-market ratio on time-varying expected returns, and procced on altering its...

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 examine the potential of ChatGPT, and other large language models, in predicting stock market returns using sentiment analysis of news headlines. We use ChatGPT to indicate whether a given headline is good, bad, or irrelevant news for firms' stock prices. We then compute a numerical score and...