Predictive Power of Public Emotions as Extracted from Daily News Articles on the Movements of Stock Market Indices

The emergence of computing power and the abundance of data have made it possible to assist human decisions, especially in the stock markets, in which the ability to predict future values would lower the risk of investing. In this paper, we present a new approach for identifying the predictive power of public emotions extracted from various sections of daily news articles on the movements of stock market indices. The approach utilizes the results of a lexicon emotion analysis conducted on crowd-annotated news to extract various types of public emotions from daily news articles. We also propose a model and an analysis method to score news articles regarding public emotions, and to identify which news sections and emotions cause movements in a stock market index. The results of an experiment conducted with 24,763 news articles show that some types of public emotions are significantly correlated with changes in the trading volume and the closing price of a stock market.