Financial time series forecasting with multi-modality graph neural network

可解释性 计算机科学 杠杆(统计) 金融市场 图形 投资策略 人工神经网络 模态(人机交互) 财务 人工智能 机器学习 经济 市场流动性 理论计算机科学
作者
Dawei Cheng,Fangzhou Yang,Sheng Xiang,Jin Liu
出处
期刊:Pattern Recognition [Elsevier BV]
卷期号:121: 108218-108218 被引量:214
标识
DOI:10.1016/j.patcog.2021.108218
摘要

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.
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