Sentiment extraction from financial public disclosure documents

We address the problem of extracting sentiment in financial public disclosure documents, and explore their effects on daily price movements. We take a collection of public disclosure forms submitted by four companies in the Turkish stock market. Using simple classification algorithms, we point to a significant correlation between the content of disclosure texts and the next day’s price direction. We discuss the relationship between learned term weights and sentiment by comparing to a translation of a well-known financial sentiment lexicon.