Temporal and Heterogeneous Graph Neural Network for Financial Time Series Prediction

Sheng Xiang · Dawei Cheng · Ying Zhang · Yuqi Liang

134 citations0/0 files storedACM International Conference on Information and Knowledge Management (CIKM)

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Stock Market Forecasting Methods Time Series Analysis and Forecasting Advanced Graph Neural Networks

Abstract

The price movement prediction of stock market has been a classical yet challenging problem, with the attention of both economists and computer scientists. In recent years, graph neural network has significantly improved the prediction performance by employing deep learning on company relations. However, existing relation graphs are usually constructed by handcraft human labeling or nature language processing, which are suffering from heavy resource requirement and low accuracy. Besides, they cannot effectively response to the dynamic changes in relation graphs. Therefore, in this paper, we propose a temporal and heterogeneous graph neural network-based (THGNN) approach to learn the dynamic relations among price movements in financial time series. In particular, we first generate the company relation graph for each trading day according to their historic price. Then we leverage a transformer encoder to encode the price movement information into temporal representations. Afterward, we propose a heterogeneous graph attention network to jointly optimize the embeddings of the financial time series data by transformer encoder and infer the probability of target movements. Finally, we conduct extensive experiments on the stock market in the United States and China. The results demonstrate the effectiveness and superior performance of our proposed methods compared with state-of-the-art baselines. Moreover, we also deploy the proposed THGNN in a real-world quantitative algorithm trading system, the accumulated portfolio return obtained by our method significantly outperforms other baselines.

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References

2015Evaluating multiple classifiers for stock price direction prediction 2019Temporal Relational Ranking for Stock Prediction 2019Stock closing price prediction based on sentiment analysis and LSTM 2021Financial time series forecasting with multi-modality graph neural network 2015Stock Market Volatility and Learning 2014Using Structured Events to Predict Stock Price Movement: An Empirical Investigation 2019Enhancing Stock Movement Prediction with Adversarial Training 2018Incorporating Corporation Relationship via Graph Convolutional Neural Networks for Stock Price Prediction

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