Financial technology (FinTech) has drawn much attention among investors and companies. While conventional stock analysis in FinTech targets at predicting stock prices, less effort is made for profitable stock recommendation. Besides, in existing approaches on modeling time series of stock prices, the relationships among stocks and sectors (i.e., categories of stocks) are either neglected or pre-defined. Ignoring stock relationships will miss the information shared between stocks while using pre-defined relationships cannot depict the latent interactions or influence of stock prices between stocks. In this work, we aim at recommending the top-K profitable stocks in terms of return ratio using time series of stock prices and sector information. We propose a novel deep learning-based model, Financial Graph Attention Networks (FinGAT), to tackle the task under the setting that no pre-defined relationships between stocks are given. The idea of FinGAT is three-fold. First, we devise a hierarchical learning component to learn short-term and long-term sequential patterns from stock time series. Second, a fully-connected graph between stocks and a fully-connected graph between sectors are constructed, along with graph attention networks, to learn the latent interactions among stocks and sectors. Third, a multi-task objective is devised to jointly recommend the profitable stocks and predict the stock movement. Experiments conducted on Taiwan Stock, S&P 500, and NASDAQ datasets exhibit remarkable recommendation performance of our FinGAT, comparing to state-of-the-art methods.
翻译:金融技术(FinTech)吸引了投资者和公司的大量注意。虽然金融技术公司在预测股票价格方面的目标常规股票分析,但用于预测股票价格的利润性股票建议的努力较少。此外,在股票价格时间序列模型的现有方法中,股票和部门(即股票类别)之间的关系要么被忽视,要么预先界定。忽视股票关系会错失股票之间共享的信息,而使用预先界定的关系无法描述股票价格之间的潜在互动或影响。在这项工作中,我们的目标是利用股票价格和部门信息的时间序列,就回报率建议高K利润股票。我们提出一个新的深层次的学习模式,即金融图表关注网络(FinGAT),以便在没有预先界定股票关系的情况下,解决股票和部门(即股票类别)之间的关系。FinGAT的概念是三重。第一,我们设计一个等级学习部分,从股票时间序列序列中学习短期和长期顺序模式。第二,我们的目标是利用股票价格和部门之间完全相连的图表,与图表关注网络一道,构建一个全新的基于学习深层次的学习模型模型模型的模型模型,以便共同了解股票和S-Finalstal Studal State Studal State State State State State State State ex ex ex ex ex 和Sevation Stristal compal comm 和共同研拟出一个共同预测一个可预见的股票和Sevmstal 和S-comm-commstal Stubal Q-comstal Q-