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Can I read FinGAT: Financial Graph Attention Networks for Recommending Top-K Profitable Stocks on EtoBox?
FinGAT: Financial Graph Attention Networks for Recommending Top-K Profitable Stocks by Yi-Ling Hsu; Yu-Che Tsai; Cheng-Te Li is a Computer Science article available to read on EtoBox.
What is FinGAT: Financial Graph Attention Networks for Recommending Top-K Profitable Stocks about?
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 c
Who reads FinGAT: Financial Graph Attention Networks for Recommending Top-K Profitable Stocks?
It is typically read by researchers, students, and practitioners in Computer Science.
- Author
- Yi-Ling Hsu; Yu-Che Tsai; Cheng-Te Li
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Published
- 2022
- Language
- EN
- Field
- Computer Science (Physical Sciences)