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FinGAT: Financial Graph Attention Networks for Recommending Top-K Profitable Stocks
arXiv - CS - Computational Engineering, Finance, and Science Pub Date : 2021-06-18 , DOI: arxiv-2106.10159 Yi-Ling Hsu, Yu-Che Tsai, Cheng-Te Li
arXiv - CS - Computational Engineering, Finance, and Science Pub Date : 2021-06-18 , DOI: arxiv-2106.10159 Yi-Ling Hsu, Yu-Che Tsai, Cheng-Te Li
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.
更新日期：2021-06-25
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