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

I. Fang Su, Ping Lei Lin, Yu Chi Chung, Chiang Lee

研究成果: Article同行評審

1 引文 斯高帕斯(Scopus)


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.

期刊Expert Systems
出版狀態Published - 2023 11月

All Science Journal Classification (ASJC) codes

  • 控制與系統工程
  • 理論電腦科學
  • 計算機理論與數學
  • 人工智慧


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