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

TL;DR

This paper introduces FinGAT, a novel deep learning model that leverages graph attention networks to recommend top-K profitable stocks by capturing latent stock and sector interactions without pre-defined relationships.

Contribution

The paper proposes a new hierarchical and graph-based deep learning approach for stock recommendation that models latent relationships among stocks and sectors without prior knowledge.

Findings

FinGAT outperforms state-of-the-art methods on multiple stock datasets.

The model effectively captures latent stock-sector interactions.

Jointly optimizing stock recommendation and movement prediction improves results.

Abstract

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.