A significant portion of information shared in earnings calls is conveyed through verbal communication by corporate managers. However, quantifying the extent of new information provided by managers poses challenges due to the unstructured nature of human language and the difficulty in gauging...

Miller (1977) hypothesizes that differences of opinion among investors about stock value result in overvaluation so long as some investors are short-sales constrained. Prior evidence on the role of differences of opinion for stock prices has not yielded convincing evidence. We test the Miller...

Previous studies have shown that high short interest stocks have low subsequent returns. We test whether the persistence of this effect is due to costs limiting arbitrage. The arbitrage cost that we focus on is idiosyncratic risk which, regardless of the arbitrageur's level of diversification,...

Prevailing models of capital markets capture a limited form of social influence and information transmission, in which the beliefs and behavior of an investor affect others only through market price, information transmission and processing is simple (without thoughts and feelings), and there is...

In this article, the authors introduce a new language representation model for sentiment analysis of financial text called Financial Embedding Analysis of Sentiment (FinEAS). The new approach is based on transformer language models that are explicitly developed for sentence-level analysis. By...

Asset pricing models implicitly assume that firm characteristics are context-free. At the same time, companies provide a substantial narrative context that helps investors to put numeric information in perspective. Management may discuss non-quantitative factors that influence performance, such...

Stock markets proved to be statistically predictable on an economically interesting scale over the past decade by fully data driven automatically constructed maps that associate to a set of new factor values a return prediction that is the average of historically observed returns for an area in...

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...

We employ deep learning in forecasting high-frequency returns at multiple horizons for 115 stocks traded on Nasdaq using order book information at the most granular level. While raw order book states can be used as input to the forecasting models, we achieve state-of-the-art predictive accuracy...

This article proposes a non-parametric portfolio selection criterion for the static asset allocation problem in a robust higher-moment framework. Adopting the Shortage Function approach, we generalize the multi-objective optimization technique in a four-dimensional space using L-moments, and...