[2106.10159] FinGAT: Financial Graph Attention Networks for

Cheng-Te Li View a PDF of the paper titled FinGAT: Financial Graph Attention Networks for Recommending Top-K Profitable Stocks， and Science (cs.CE); Information Retrieval (cs.IR); Social and Information Networks (cs.SI) Cite as: arXiv:2106.10159 [cs.LG] (or arXiv:2106.10159v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2106.10159 Focus to learn more arXiv-issued DOI via DataCite 。

to tackle the task under the setting that no pre-defined relationships between stocks are given. The idea of FinGAT is three-fold. First， in existing approaches on modeling time series of stock prices， a multi-task objective is devised to jointly recommend the profitable stocks and predict the stock movement. Experiments conducted on Taiwan Stock， to learn the latent interactions among stocks and sectors. Third， 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， and NASDAQ datasets exhibit remarkable recommendation performance of our FinGAT， comparing to state-of-the-art methods. Comments: Accepted to IEEE TKDE 2021. The first two authors equally contribute to this work. Code is available at this https URL Subjects: Machine Learning (cs.LG) ; Computational Engineering， Yu-Che Tsai， less effort is made for profitable stock recommendation. Besides， along with graph attention networks。

Financial Graph Attention Networks (FinGAT)， the relationships among stocks and sectors (i.e.， 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，。

SP 500， by Yi-Ling Hsu and 2 other authors View PDF Abstract: Financial technology (FinTech) has drawn much attention among investors and companies. While conventional stock analysis in FinTech targets at predicting stock prices， a fully-connected graph between stocks and a fully-connected graph between sectors are constructed， [Submitted on 18 Jun 2021] Title: FinGAT: Financial Graph Attention Networks for Recommending Top-K Profitable Stocks Authors: Yi-Ling Hsu， Finance， we devise a hierarchical learning component to learn short-term and long-term sequential patterns from stock time series. Second。