Using Time-Series Anomaly Detection Approach for Identifying Arbitrage Opportunities
DOI:
https://doi.org/10.22452/mjs.vol45sp2.2Keywords:
anomaly detection, machine learning, pairs trading, Philippine stocks, statistical arbitrageAbstract
Since market anomalies are tedious and difficult to manually identify, the use of anomaly detection techniques to find statistical arbitrage opportunities is becoming increasingly popular. As such, this study compares the performance of different pairs trading strategies on the top 100 Philippine stocks listed on the Philippine Stock Exchange to assess the feasibility of using machine learning-based anomaly detection models. Specifically, time series anomaly detection through generative adversarial networks and auto-encoder with regression were employed to detect statistical arbitrage opportunities. The comparison across the strategies was limited to the top five cointegrated pairs (of order one) with the smallest sum of squared residuals. Results show that, even after accounting for transaction costs, AER outperformed the benchmarks (distance and cointegration approaches) by a wide margin, while TadGAN achieved a slightly better performance than the benchmarks. These findings suggest that anomaly detection models have the potential to yield higher profits than conventional approaches.
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