[2S4-IS-2b] Machine learning

[2S4-IS-2b-02] Proposal for Turning Point Detection Method using Financial Text and Transformer

〇Rei Taguchi¹, Hikaru Watanabe¹, Hiroki Sakaji¹, Kiyoshi Izumi¹, Kenji Hiramatsu² (1. The University of Tokyo, 2. IFIS Japan Limited.)

Regular

Keywords:Stock Market, Financial Time Series Analysis, Natural Language Processing, Machine Learning, Anomaly detection

In this study, we demonstrate whether analysts' sentiment toward individual stocks is useful for stock market analysis. This can be achieved by creating a polarity index in analyst reports using natural language processing. In this study, we calculated anomaly scores for the created polarity index using anomaly detection algorithms. The results show that the proposed method is effective in detecting the turning point of the polarity index.