Abstract
To detect abnormal price jumps of financial markets, some indicators based on volatility have been used such as the bipower variation and the BPV ratio. However, these indicators only focus on a single individual stock and do not consider the relationships among all individual stocks composing a complex financial system. For this reason, we applied an autoencoder to learn the relationships among all stocks, and we considered a stock price that the autoencoder cannot restore as an abnormal price. Moreover, we identified that the price movement immediately following an abnormal price is clearly biased, and we confirmed the validity of our trading strategy based on this anomaly by performing some statistical significance tests.
