[2A4-GS-2] Machine learning

[2A4-GS-2-05] Anomaly detection in the stock market using Graph Based Entropy and market beta

〇Yoshiyuki Nakata¹, Takaaki Yoshino¹, Toshiaki Sugie¹, Kakeru Ito¹, Kaira Sekiguchi², Yukio Ohsawa² (1. Nissay Asset Management Corporation, 2. Univ. of Tokyo)

Keywords:Graph-based entropy, cluster analysis, anomaly detection

In the stock market, market beta, which indicates the linkage between the stock index and the individual stock price, is one of the index values that attracts attention from investors. The price of stocks with high market beta (high beta) may strongly reflect investors' sentiment toward the market outlook. On the other hand, the recent trend of the individual stock does not necessarily coincide with the overall market trend, as it also depends on its recent performance. Therefore, the bidding for stocks with extreme trends may strongly reflect investors' expectations for the future relative to the market and the individual stocks. In this study, we propose a method of anomaly detection based on the price movements of stocks in the stock market. We divide the constituent stocks into regions based on market beta and trend, which are expected to reflect investor sentiment toward the market outlook. By applying the Graph Based Entropy method to the price movements of each region, we attempted to detect anomalies such as a strong downtrend in a stock index. We performed the tests on three equity indices, TOPIX 500, S&P 500, and STOXX® Europe 600, and succeeded in detecting several strong downtrends.