The impact of news sentiment on the stock market fluctuation : the case of selected energy sector

Abstract

This paper examines the impact of the sentiments of OPEC news on stock market prices of public listed oil and gas companies in Bursa Malaysia. We used data of stock market prices from randomly selected oil and gas companies for the period of 2012 to 2017. For the methodology, we first established a supervised machine learning algorithm-based news classifier to classify the OPEC news following its sentiments. We developed a financial news sentiment classifier by combining machine learning algorithms and lexicon-based labelling methods. We then applied the event study method to investigate how stock market prices react to OPEC news’ sentiment. The results showed a negative correlation between OPEC news sentiment and stock market prices of oil and gas companies during the event window based on each OPEC news release date. The results further showed that the stock market prices do not react to OPEC news sentiment on event day. These findings should provide some guides to stock investors on the movement of the selected stock market prices of energy sector companies during the event window period.