Accurate stock price forecasting has consistently remained a pivotal yet challenging FinTech task that underpins quantitative trading and investment decision making. Recent efforts have been dedicated to modeling various complex relationships among stocks in the stock market toward more reliable stock price forecasting.These methods depend heavily on strong static prior assumptions by modeling either temporal dependencies within individual stocks or spatial dependencies across different stocks based on predefined structures, while the complex market dynamics that drive stock price movements remain unexplored. To alleviate this issue, we propose a novel game-theoretic modeling method that captures heterogeneous investor interactions for stock price forecasting. The core idea is to embed game-theoretic mechanisms into the heterogeneous graph structure to finely model the dynamic strategic interactions among heterogeneous investors with respect to target stocks. Additionally, temporal positional encoding is adopted to reflect the differentiated influences of each game event at different time steps within the time window on future stock price movements. Leveraging heterogeneous graph networks, we proxy the intricate dynamics of the stock market through investor games and enable real-time information propagation and node updates among all nodes. Extensive experiments conducted on two real-world benchmark dataset demonstrate that our method effectively outperforms state-of-the-art stock price forecasting methods.

翻译：准确的股票价格预测始终是支撑量化交易与投资决策的关键且充满挑战的金融科技任务。近期研究致力于刻画股票市场中股票间的多种复杂关系以实现更可靠的股价预测。这些方法严重依赖于静态先验假设，要么基于预定义结构建模单只股票的时间依赖关系，要么建模不同股票间的空间依赖关系，但驱动股价波动的复杂市场动态仍未被探索。为解决此问题，我们提出一种新颖的博弈论建模方法，通过捕捉异质投资者互动进行股票价格预测。其核心思想是将博弈论机制嵌入异构图结构中，精细模拟异质投资者针对目标股票的动态策略互动。此外，采用时间位置编码来反映时间窗口内各博弈事件在不同时间步对股价未来走势的差异化影响力。借助异构图网络，我们通过投资者博弈代理股票市场的复杂动态，实现所有节点间的实时信息传播与节点更新。在两个真实基准数据集上的大量实验表明，我们的方法有效超越了现有最先进的股票价格预测方法。