Stock prediction: an event-driven approach based on bursty keywords

Abstract

There are many real applications existing where the decision making process depends on a model that is built by collecting information from different data sources. Let us take the stock market as an example. The decision making process depends on a model which that is influenced by factors such as stock prices, exchange volumes, market indices (e.g. Dow Jones Index), news articles, and government announcements (e.g., the increase of stamp duty). Yet Nevertheless, modeling the stock market is a challenging task because (1) the process related to market states (rise state/drop state) is a stochastic process, which is hard to capture using the deterministic approach, and (2) the market state is invisible but will be influenced by the visible market information, like stock prices and news articles. In this paper, we propose an approach to model the stock market process by using a Non-homogeneous Hidden Markov Model (NHMM). It takes both stock prices and news articles into consideration when it is being computed. A unique feature of our approach is event driven. We identify associated events for a specific stock using a set of bursty features (keywords), which has a significant impact on the stock price changes when building the NHMM. We apply the model to predict the trend of future stock prices and the encouraging results indicate our proposed approach is practically sound and highly effective.