Presentation information

International Session

International Session » ES-2 Machine learning

[2S4-IS-2b] Machine learning

Wed. Jun 15, 2022 1:20 PM - 2:40 PM Room S (Online S)

Chair: Ken Ishibashi (University of Hyogo)

1:40 PM - 2:00 PM

[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.)


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.

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