Chen, H.; Wang, T.; Konpang, J.; Sirikham, A. Learning A-Share Stock Recommendation From Stock Graph and Historical Price Simultaneously. Preprints2024, 2024100269. https://doi.org/10.20944/preprints202410.0269.v1
APA Style
Chen, H., Wang, T., Konpang, J., & Sirikham, A. (2024). Learning A-Share Stock Recommendation From Stock Graph and Historical Price Simultaneously. Preprints. https://doi.org/10.20944/preprints202410.0269.v1
Chicago/Turabian Style
Chen, H., Jessada Konpang and Adisorn Sirikham. 2024 "Learning A-Share Stock Recommendation From Stock Graph and Historical Price Simultaneously" Preprints. https://doi.org/10.20944/preprints202410.0269.v1
Abstract
The Chinese stock market, marked by rapid growth and significant volatility, presents unique challenges for investors and analysts. A-share stocks, traded on the Shanghai and Shenzhen exchanges, are crucial to China’s financial system and offer opportunities for both domestic and international investors. Accurate stock recommendation tools are vital for informed decision-making, especially given the ongoing regulatory changes and economic reforms in China. Current stock recommendation methods often fall short, as they typically fail to capture the complex inter-company relationships and rely heavily on financial reports, neglecting the potential of unlabeled data and historical price trends. In response, we propose a novel approach that combines graph-based structures with historical price data to develop self-learned stock embeddings for A-share recommendations. Our method leverages self-supervised learning, bypassing the need for human-generated labels and autonomously uncovering latent relationships and patterns within the data. This dual-input strategy enhances the understanding of market dynamics, leading to more accurate stock predictions. Our contributions include a novel framework for label-free stock recommendations with modeling stock connections and pricing information, and empirical evidence demonstrating the robustness and adaptability of our approach in the volatile Chinese stock market.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
