Futures Quantitative Investment with Heterogeneous Continual Graph Neural Network
This study aims to address the challenges of futures price prediction in high-frequency trading (HFT) by proposing a continuous learning factor predictor based on graph neural networks. The model integrates multi-factor pricing theories with real-time market dynamics, effectively bypassing the limit...
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Zusammenfassung: | This study aims to address the challenges of futures price prediction in
high-frequency trading (HFT) by proposing a continuous learning factor
predictor based on graph neural networks. The model integrates multi-factor
pricing theories with real-time market dynamics, effectively bypassing the
limitations of existing methods that lack financial theory guidance and ignore
various trend signals and their interactions. We propose three heterogeneous
tasks, including price moving average regression, price gap regression and
change-point detection to trace the short-, intermediate-, and long-term trend
factors present in the data. In addition, this study also considers the
cross-sectional correlation characteristics of future contracts, where prices
of different futures often show strong dynamic correlations. Each variable
(future contract) depends not only on its historical values (temporal) but also
on the observation of other variables (cross-sectional). To capture these
dynamic relationships more accurately, we resort to the spatio-temporal graph
neural network (STGNN) to enhance the predictive power of the model. The model
employs a continuous learning strategy to simultaneously consider these tasks
(factors). Additionally, due to the heterogeneity of the tasks, we propose to
calculate parameter importance with mutual information between original
observations and the extracted features to mitigate the catastrophic forgetting
(CF) problem. Empirical tests on 49 commodity futures in China's futures market
demonstrate that the proposed model outperforms other state-of-the-art models
in terms of prediction accuracy. Not only does this research promote the
integration of financial theory and deep learning, but it also provides a
scientific basis for actual trading decisions. |
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DOI: | 10.48550/arxiv.2303.16532 |