FinGAT: Financial Graph Attention Networks for Recommending Top-K Profitable Stocks

Yi Ling Hsu, Yu Che Tsai, Cheng Te Li

研究成果: Article同行評審

5 引文 斯高帕斯(Scopus)


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.

頁(從 - 到)469-481
期刊IEEE Transactions on Knowledge and Data Engineering
出版狀態Published - 2023 1月 1

All Science Journal Classification (ASJC) codes

  • 資訊系統
  • 電腦科學應用
  • 計算機理論與數學


深入研究「FinGAT: Financial Graph Attention Networks for Recommending Top-K Profitable Stocks」主題。共同形成了獨特的指紋。