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Probabilistic Lexicon-Based Approach for Stock Market Prediction: A Case Study of The Stock Exchange of Thailand (SET)

Stock market prediction has long been a major research topic that exploits various machine learning techniques and diverse sets of data. Most existing works utilize multiple stock historical statistics as well as up-to-date data of relevant factors which could have impacted the stock price value such as oil price, gold price, etc. Very few works explore the possibility of incorporating financial news when predicting the stock price direction. In this paper, a predictive approach using the probabilistic lexicon generated from Thai financial news and stock market closing prices is investigated. Relevant event terms will be extracted and assigned probabilistic values according to the proposed Probabilistic Lexicon Based Stock Market Prediction (PLSP) algorithm. The obtained results of this study show that the proposed model is superior to other models.