A Study Of Importance Of Textual Features For Predictive Models Of Financial Indicators

In this study, we experimentally assess the potential of informal and unregulated communication to contribute to predictive models of financial markets indicators. The data sources that were analyzed are unregulated parts of yearly reports of the companies of the DOW30 index, text of tweets that mention these companies, data from financial statements, and stock market data about stock prices and volume. We conducted correlation analysis of descriptive and target features and an analysis of impacts of descriptive features to predictive power of models for regression and classification. The results indicate that overall the studied features only weakly describe the complex and noisy target phenomena and that also the linguistic features can contribute to phenomena models, particularly the features that represent expressions of sentiment, both in tweets and annual reports.