Forecasting of Taiwan's weighted stock Price index based on machine learning

Abstract This study proposes a stack framework of light gradient boosting machine (LGBM) for Taiwan stock market index prediction. Stock market predictions have been regarded as a challenging task, as the market is affected by several factors such as political events, general economic conditions, institutional investors' choices, movement of the global market, psychology of investors. We construct a rich feature set to capture the impacts of global markets, institutional investors' choices, and the psychology of investors. A feature selection algorithm is proposed to choose important feature subset and enhance the training performance. To further improve the prediction accuracy, we employ stacking strategy to combine multiple classifiers together. A 10‐year period of the Taiwan stock exchange capitalization weighted stock index (TAIEX) is used to verify the performance of the proposed model. The experimental results suggest that our prediction model as well as the feature selection method can achieve good prediction performance.