We examine the post-IPO excess stock returns of hospitality firms from 1996 to 2012 and find underperformance relative to the market on average. However, there are large differences in returns and some firms significantly outperform. We demonstrate that a substantial portion of this variation...

We investigate the pricing of risk-neutral skewness in the stock options market by creating skewness assets comprised of two option positions (one long and one short) and a position in the underlying stock. The assets are created such that exposure to changes in the price of the underlying stock...

This paper examines the cross-sectional properties of stock return forecasts based on Fama-MacBeth regressions using all firms contained in the STOXX Europe 600 index during the September 1999-December 2018 period. Our estimation approach is strictly out-of-sample, mimicking an investor who...

Finance literature highlights various reasons for stock performance subsequent to earnings announcements. However, other moving parts in these scenarios must also be simultaneously specified. While both revenue and earnings surprises are important for determining stock performance,...

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...

This paper uses accounting-based reverse engineering of market expectations to identify potentially mispriced stocks. Building upon the “errors-in-expectations” hypothesis, we develop a theoretically funded yet practical tool for stock screening in this paper. We use the Ohlson (1995) model...

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...

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...

We survey the literature on stock return forecasting, highlighting the challenges faced by forecasters as well as strategies for improving return forecasts. We focus on U.S. equity premium forecastability and illustrate key issues via an empirical application based on updated data. Some studies...

This paper examines the relationship between two Presidential elections and stock returns in Egypt. The available literature showed mixed results on the relationship between Presidential Elections and stock returns. The author examined daily data and used an OLS regression. Each Event Window...