Integrating finance dictionary in lexicon-based approach with machine learning algorithm to analyse the impact of OPEC news sentiment on financial market / Wu Ling - Wu, Ling (2020)
Since last few decades, machine learning algorithm which trains computers to learn from experience, is one of the most rapidly developing techniques which settles in the intersection research field of statistics and computer science. This research aims to build a properly trained machine learning classifier to study the impact of Organization of Petroleum Exporting Countries (OPEC) news sentiment on stock prices of six Malaysian public listed companies (energy sector) in the main board of Bursa Malaysia. The data used in this research are collected during the period 2012-2017. To carry out the research, firstly, lexicon-based approach is used to analyze the sentiment of sentences in the financial news articles. A sentiment dictionary from a finance domain is applied to improve the accuracy in labelling the financial news sentences. The labelled sentences are then used to train the supervised machine learning classifiers. The classifiers classify the OPEC news sentences into three different categories – negative (labeled with sentiment score -1), neutral (labeled with sentiment score 0), and positive (labeled with sentiment score 1). The performance of the supervised machine learning classifier is found to achieve 70% accuracy. The OPEC news article’s sentiment score is calculated using relative proportional difference evaluating method: S = (P-N) / (P+N), whereby, P and N are the number of positive and negative sentences in the article, respectively. The sentiment score of each article ranges from -1 to 1. Using event study method, this sentiment score is used to compare with the historical stock prices of the six selected public listed energy sector companies. Results of the analysis show that OPEC news sentiment shows impact on the stock prices of these six companies. However, the impact did not occur on the news release date. During the event window period (i.e., five days before and after a news released), there is a negative correlation between OPEC news sentiment and the six companies’ average cumulative abnormal return. Cumulative abnormal return is the average of daily abnormal return during the event window, which can be used to show the overall fluctuation of the stock prices. The findings of this research show that applying financial sentiment dictionary to train the supervised machine learning algorithm can enhance the performance of machine learning classifier. Results of statistical analysis in this research also provides a clear picture to the stock investors on the movement of the six Malaysian energy sector companies’ stock prices during the event window period. This can help them to make better decisions in their trading in order to obtain profitable stock returns.