LSR-IGRU：基于长期与短期关系及改进GRU的股票趋势预测
LSR-IGRU: Stock Trend Prediction Based on Long Short-Term Relationships and Improved GRU
AI总结：
本文提出LSR-IGRU模型，利用二级行业与隔夜价格构建股票长短期关系，并改进GRU输入以融合关系和时序信息，在中美数据集及实盘算法交易中验证了更优预测与收益表现。
摘要：
股票价格预测是金融领域中一项具有挑战性的问题，并受到广泛关注。近年来，随着深度学习和图神经网络等技术的快速发展，更多研究方法开始关注挖掘股票之间的相互关系。然而，现有方法大多聚焦于股票的短期动态关系，并将关系信息与时间信息直接融合。它们往往忽视了股票市场中股票之间复杂的非线性动态特征以及潜在的高阶交互关系。因此，本文提出一种名为LSR-IGRU的股票价格趋势预测模型，该模型基于股票的长期与短期关系以及改进的GRU输入。首先，我们构建股票之间的长期与短期关系矩阵，其中首次利用二级行业信息来捕捉股票的长期关系，并利用隔夜价格信息建立短期关系。接着，我们改进GRU模型在每一步的输入，使模型能够更有效地融合时间信息与长期和短期关系信息，从而显著提高预测股票趋势变化的准确性。最后，通过在中国和美国股票市场的多个数据集上进行大量实验，我们验证了所提出的LSR-IGRU模型相对于当前最先进基线模型的优越性。我们还将所提出的模型应用于一家金融公司的算法交易系统，与其他基线方法相比，实现了显著更高的投资组合累计收益。我们的源代码已发布于https://github.com/ZP1481616577/Baselines_LSR-IGRU。
英文摘要：
Stock price prediction is a challenging problem in the field of finance and receives widespread attention. In recent years, with the rapid development of technologies such as deep learning and graph neural networks, more research methods have begun to focus on exploring the interrelationships between stocks. However, existing methods mostly focus on the short-term dynamic relationships of stocks and directly integrating relationship information with temporal information. They often overlook the complex nonlinear dynamic characteristics and potential higher-order interaction relationships among stocks in the stock market. Therefore, we propose a stock price trend prediction model named LSR-IGRU in this paper, which is based on long short-term stock relationships and an improved GRU input. Firstly, we construct a long short-term relationship matrix between stocks, where secondary industry information is employed for the first time to capture long-term relationships of stocks, and overnight price information is utilized to establish short-term relationships. Next, we improve the inputs of the GRU model at each step, enabling the model to more effectively integrate temporal information and long short-term relationship information, thereby significantly improving the accuracy of predicting stock trend changes. Finally, through extensive experiments on multiple datasets from stock markets in China and the United States, we validate the superiority of the proposed LSR-IGRU model over the current state-of-the-art baseline models. We also apply the proposed model to the algorithmic trading system of a financial company, achieving significantly higher cumulative portfolio returns compared to other baseline methods. Our sources are released at https://github.com/ZP1481616577/Baselines_LSR-IGRU.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
