Interpretable Stock Anomaly Detection Based on Spatio-Temporal Relation Networks with Genetic Algorithm

Instability in financial markets represents a considerable risk to investors; examples of instability include a market crash caused by systematic risks and abnormal stock price volatility caused by artificial hype. The early detection of abnormal behavior can help investors adjust their strategy and reduce investment risks. We proposed a spatiotemporal convolutional neural network-based relational network (STCNN-RN) model that can learn the complex correlations between multiple financial time-series data sets, and we used genetic algorithms with a constrained gene to discover the time points for outlier companies by fitting the STCNN-RN model; we used these outlier points to identify abnormal situations. Most research on identifying anomalous patterns has been unable to sufficiently explain the reason for anomalies to investors. We applied an interpretability model to enable investors to understand these anomalous time points in relation to companies and discover the key factors giving rise to the anomalies. The experiment results revealed that the proposed model can be used to model multiple financial time-series data sets and to capture anomalous situations in relevant companies. Because this study explored the discovery of anomaly phenomena in all transaction data and the explanation of these abnormalities, investors can understand a stock market situation holistically.