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.
Highlights
The stock market has grown swiftly in these years, and trading stocks have become one of the most attractive financial instruments for investors
We propose a novel graph neural network-based model, Financial Graph Attention Networks (FinGAT), to achieve the goal and to implement our idea by dealing with the aforementioned challenges in modeling hierarchical relationships among stocks and sectors
We can find that the proposed FinGAT significantly outperforms all of the competing methods among three datasets, especially on the ranking evaluation in terms of MRR and Precision
Summary
The stock market has grown swiftly in these years, and trading stocks have become one of the most attractive financial instruments for investors. Investing in the stock market is highly profitable and easy to get started. Investing stocks usually involves extremely high risk, which makes drawing up a proper investment plan a crucial task. People tend to empirically choose stocks by their financial knowledge or expertise. As financial technology (FinTech) is in widespread use, people come up with statistical inference models to forecast the dynamic movement of stock prices [23]. Techniques of machine learning and deep learning are investigated and applied in industries, which has shown remarkable success in different stock markets, such as S&P 500 [15] and NASDAQ [12]
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More From: IEEE Transactions on Knowledge and Data Engineering
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