Generating Domain-Specific Dictionaries using Bayesian Learning

This paper aims to operationalize subjective information processing in financial news disclosures. In order to measure news tone, previous research commonly utilizes manually-selected positive and negative word lists, such as the Harvard-IV psychological dictionary. However, such dictionaries may not be suitable for the domain of financial news because positive and negative entries could have different connotations in a financial context. To overcome the problem of words that are selected ex ante, we incorporate several Bayesian variable selection methods to select the relevant positive and negative words from financial news disclosures. These domain-specific dictionaries outperform existing dictionaries in terms of both their explanatory power and predictive performance, resulting in an improvement of up to 93.25 % in the correlation between news sentiment and stock market returns. According to our findings, the interpretation of words strongly depends on the context and managers need to be cautious when framing negative content using positive words.