A deep learning algorithm for stock selection based on multi-factor anomaly detection

Abstract

In recent years, quantitative investment has been a hot spot in the development of the financial market. Quantitative stock selection is the most crucial part of quantitative investment. It is of great significance to study how to select high-quality stocks from thousands of stocks, bring them into the stock pool and allocate assets. Various machine learning and deep learning algorithms have been used in this research. This paper proposes a new stock selection strategy for multi-factor anomaly detection based on variational auto-encoder. First, we select factors from three aspects: fundamental, technical, and capitalization. Then, unsupervised anomaly detection is performed on the multivariate time series data based on variational auto-encoder to obtain the anomaly scores of the factors, and get the abnormal result by comparing it with the threshold. Finally, the abnormal results are used to select stocks combined with the trend of the selected stocks. We apply the model to four groups of stocks belonging to SCI300, SSE 50, SZSI and CSI500 respectively, and evaluate the performance compared with the Buy&Hold strategy, Traditional multi-factor model stock selection strategy, AdaBoost machine learning stock selection strategy. The experimental results show that the model can identify “good” stocks from the sample, and the performance of the selected portfolio is better than the benchmarks test.