Accurate Multivariate Stock Movement Prediction via Data-Axis Transformer with Multi-Level Contexts

How can we efficiently correlate multiple stocks for accurate stock movement prediction? Stock movement prediction has received growing interest in data mining and machine learning communities due to its substantial impact on financial markets. One way to improve the prediction accuracy is to utilize the correlations between multiple stocks, getting a reliable evidence regardless of the random noises of individual prices. However, it has been challenging to acquire accurate correlations between stocks because of their asymmetric and dynamic nature which is also influenced by the global movement of a market. In this work, we propose DTML (Data-axis Transformer with Multi-Level contexts), a novel approach for stock movement prediction that learns the correlations between stocks in an end-to-end way. DTML makes asymmetric and dynamic correlations by a) learning temporal correlations within each stock, b) generating multi-level contexts based on a global market context, and c) utilizing a transformer encoder for learning inter-stock correlations. DTML achieves the state-of-the-art accuracy on six datasets collected from various stock markets from US, China, Japan, and UK, making up to 13.8%p higher profits than the best competitors and the annualized return of 44.4% on investment simulation.

Automated summary

This study introduces a new approach named DTML (Data-axis Transformer with Multi-Level contexts) for learning correlations between multiple stocks for stock movement prediction. By learning temporal correlations within each stock, generating multi-level contexts based on a global market context, and utilizing a transformer encoder for learning inter-stock correlations, DTML achieves state-of-the-art accuracy and higher profits compared to competitors on datasets collected from US, China, Japan, and UK stock markets.