Financial time series forecasting with multi-modality graph neural network

Financial time series analysis plays a central role in hedging market risks and optimizing investment decisions. This is a challenging task as the problems are always accompanied by multi-modality streams and lead-lag effects. For example, the price movements of stock are reflections of complicated...

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Vydané v:Pattern recognition Ročník 121; s. 108218
Hlavní autori: Cheng, Dawei, Yang, Fangzhou, Xiang, Sheng, Liu, Jin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.01.2022
Predmet:
Deep learning
Graph attention
Graph neural network
Quantitative investment
Deep learning
Quantitative investment
Graph attention
Graph neural network
ISSN:0031-3203, 1873-5142
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Shrnutí:Financial time series analysis plays a central role in hedging market risks and optimizing investment decisions. This is a challenging task as the problems are always accompanied by multi-modality streams and lead-lag effects. For example, the price movements of stock are reflections of complicated market states in different diffusion speeds, including historical price series, media news, associated events, etc. Furthermore, the financial industry requires forecasting models to be interpretable and compliant. Therefore, in this paper, we propose a multi-modality graph neural network (MAGNN) to learn from these multimodal inputs for financial time series prediction. The heterogeneous graph network is constructed by the sources as nodes and relations in our financial knowledge graph as edges. To ensure the model interpretability, we leverage a two-phase attention mechanism for joint optimization, allowing end-users to investigate the importance of inner-modality and inter-modality sources. Extensive experiments on real-world datasets demonstrate the superior performance of MAGNN in financial market prediction. Our method provides investors with a profitable as well as interpretable option and enables them to make informed investment decisions.
ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2021.108218