Natural Language Processing for Company Financial Communication Style

Nowadays, financial firms can interpret press releases within few seconds using natural language processing algorithms. Therefore, it is important for public companies to structure its communications in a way that accounts for how the market digests its public information and avoid unnecessary volatility. Companies want to know the impression of their communications, such as investors calls and annual reports, among the investment community including analysts, financial press, and institutional investors. While there have been research papers connecting sentiment analysis of company communication materials to stock movement, research on identifying any similarities in communication styles among public companies has not been a major topic. We aimed to quantify the sentiment of those communication materials and determine if there are any discernible communication styles among leading technology companies. In addition, we conducted analyses and comparisons to stock indices to connect company communication style to market reactions from investors. Our results indicate that there is a signal between sentiment scores derived from Loughran McDonald dictionary and market-residualized stock performance of our company set, highlighting the benefits one can obtain from using NLP techniques.