Results of the summarizing process for the arXiv paper: 2106.10159v1

Financial technology (FinTech) has become a prominent area of interest for investors and companies. While traditional stock analysis in FinTech focuses on predicting stock prices, there is a lack of emphasis on recommending profitable stocks. Additionally, existing approaches to modeling time series of stock prices often overlook the relationships between stocks and sectors or rely on predefined relationships that fail to capture the underlying interactions and influences among stock prices. In this study, the authors aim to address these limitations by proposing a novel deep learning-based model called Financial Graph Attention Networks (FinGAT). The objective of FinGAT is to recommend the top-K profitable stocks based on return ratio using both time series data of stock prices and sector information. Importantly, FinGAT operates under the assumption that no pre-defined relationships between stocks are given. The FinGAT model consists of three key components. First, a hierarchical learning component is designed to capture short-term and long-term sequential patterns from the time series data of stock prices. This allows for a comprehensive understanding of the temporal dynamics in stock price movements. Secondly, fully-connected graphs are constructed both between individual stocks and between sectors. These graphs serve as representations of the latent interactions among stocks and sectors. Graph attention networks are employed to learn these interactions effectively. Lastly, a multi-task objective is formulated to jointly recommend profitable stocks and predict their movement. By integrating these two tasks into a single framework, FinGAT can leverage the shared information between them to enhance its recommendation performance. The authors conducted experiments using datasets from Taiwan Stock, S&P 500, and NASDAQ markets. The results demonstrate that FinGAT outperforms state-of-the-art methods in terms of recommendation performance for profitable stocks. Overall, this study presents an innovative approach to recommending profitable stocks by leveraging deep learning techniques and graph attention networks. By considering both temporal patterns in stock prices and latent interactions among stocks and sectors, FinGAT offers valuable insights for investors and companies in the FinTech domain.

• Financial technology (FinTech) is a prominent area of interest for investors and companies
• Traditional stock analysis in FinTech focuses on predicting stock prices, but lacks emphasis on recommending profitable stocks
• Existing approaches to modeling time series of stock prices often overlook relationships between stocks and sectors or rely on predefined relationships that fail to capture underlying interactions and influences among stock prices
• The authors propose a novel deep learning-based model called Financial Graph Attention Networks (FinGAT) to address these limitations
• FinGAT aims to recommend the top-K profitable stocks based on return ratio using both time series data of stock prices and sector information
• FinGAT operates under the assumption that no pre-defined relationships between stocks are given
• The FinGAT model consists of three key components: hierarchical learning component, fully-connected graphs representing interactions among stocks and sectors, and a multi-task objective for recommending profitable stocks and predicting their movement
• Experiments using datasets from Taiwan Stock, S&P 500, and NASDAQ markets show that FinGAT outperforms state-of-the-art methods in terms of recommendation performance for profitable stocks
• FinGAT leverages deep learning techniques and graph attention networks to offer valuable insights for investors and companies in the FinTech domain.

Exploring Profitable Stock Recommendations with Financial Graph Attention Networks

In the world of finance, predicting stock prices has traditionally been a major focus for investors and companies. However, there is a lack of emphasis on recommending profitable stocks. Existing approaches to modeling time series data often overlook the relationships between stocks and sectors or rely on predefined relationships that fail to capture the underlying interactions and influences among stock prices. To address these limitations, researchers have proposed a novel deep learning-based model called Financial Graph Attention Networks (FinGAT). The objective of FinGAT is to recommend top-K profitable stocks based on return ratio using both time series data of stock prices and sector information.

What is FinGAT?

FinGAT operates under the assumption that no pre-defined relationships between stocks are given. It consists of three key components: hierarchical learning component, fully-connected graphs, and multi-task objectives. The hierarchical learning component captures short-term and long-term sequential patterns from the time series data of stock prices in order to gain an understanding of temporal dynamics in stock price movements. Fully connected graphs are constructed both between individual stocks and between sectors as representations of latent interactions among them; graph attention networks are employed to learn these interactions effectively. Lastly, a multi-task objective is formulated which jointly recommends profitable stocks while predicting their movement; this allows for leveraging shared information between tasks to enhance recommendation performance.

Experimental Results

The authors conducted experiments using datasets from Taiwan Stock, S&P 500, and NASDAQ markets; results demonstrate that FinGAT outperforms state-of-the art methods in terms of recommendation performance for profitable stocks. This study presents an innovative approach by considering both temporal patterns in stock prices as well as latent interactions among stocks and sectors - offering valuable insights for investors and companies in the FinTech domain.

Conclusion

Financial technology (FinTech) has become increasingly important for investors seeking better returns on their investments; however traditional models often overlook important factors such as relationships between different assets or pre defined assumptions about how they interact with each other which can lead to inaccurate predictions or recommendations about potential investments opportunities . In this paper we presented an innovative approach called Financial Graph Attention Networks (FinGAT) which leverages deep learning techniques combined with graph attention networks to recommend top K profitable stocks based on return ratio by taking into account both temporal patterns in stock prices as well as latent interactions among them . The experimental results show that it outperforms existing methods , providing valuable insights into financial technology domain .