Dynamic Industry-Specific Lexicon Generation for Stock Market Forecast

Press releases represent a valuable resource for financial trading and have long been exploited by researchers for the development of automatic stock price predictors. We hereby propose an NLP-based approach to generate industry-specific lexicons from news documents, with the goal of dynamically capturing, on a daily basis, the correlation between words used in these documents and stock price fluctuations. Furthermore, we design a binary classification algorithm that leverages on our lexicons to predict the magnitude of future price changes, for individual companies. Then, we validate our approach through an experimental study conducted on three different industries of the Standard & Poor’s 500 index, by processing press news published by globally renowned sources, and collected within the Dow Jones DNA dataset. Classification results let us quantify the mutual dependence between words and prices, and help us estimate the predictive power of our lexicons.