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

Jaemin Yoo · Yejun Soun · Yong-chan Park · U Kang

151 citations0/0 files storedACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)

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Stock Market Forecasting Methods Financial Markets and Investment Strategies Time Series Analysis and Forecasting

Abstract

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.

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References

2017A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction 2017Stock market's price movement prediction with LSTM neural networks 2017Stock Price Prediction via Discovering Multi-Frequency Trading Patterns 2018Stock Movement Prediction from Tweets and Historical Prices 2013Automated news reading: Stock price prediction based on financial news using context-capturing features 2019Enhancing Stock Movement Prediction with Adversarial Training 2018Listening to Chaotic Whispers 2019Stock Price Prediction Using News Sentiment Analysis

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