This paper investigates the impact of annual report readability on the corporate bond market. My findings indicate that in the US corporate bond market, firms with less readable annual reports tend to have higher credit spreads, higher credit spread volatilities, higher transaction costs, higher...

We adopt a machine learning-based algorithm to identify direct and indirect company mentions in newspaper articles. We identify the importance of properly matching text segments to their respective companies when estimating human-perceived newspaper sentiment. Previous research has developed...

We introduce a novel method for training computer algorithms to measure news sentiment. Our approach leverages human-coded sentiment scores from over 200,000 newspaper articles to teach the computer to select words, word combinations, and their linear weights. In an out-of-sample test, examining...

Recent research suggests that machine learning models dominate traditional linear models in predicting cross-sectional stock returns. We confirm this finding when predicting one-month forward-looking returns based on a set of common stock characteristics, including predictors such as short-term...

Emerging markets (EM) corporate bonds are perceived to offer attractive diversification potential and risk-adjusted returns, but to be illiquid. This study expands the empirical evidence by examining the liquidity of EM debt by solving a triangular structured system. We find EM bond liquidity to...