发表机构
Beijing University of Posts and Telecommunications（北京邮电大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对股票预测中开盘价捷径导致模型退化的问题，提出仅依赖价格模态的条件化开盘先验与证据框架，通过条件化重参数化和残差建模提炼有效信息，在四个真实数据集上显著优于现有方法。
AI 中文摘要
股票价格预测长期以来一直是量化金融的核心问题。尽管近期方法越来越多地利用新闻等外部模态来提升预测性能，但价格序列内部的微观结构本身也包含关键信息。目标日的开盘价是隔夜信息在市场中的首次集中体现，对日内演变和最终收盘方向具有重要价值。然而，一旦引入开盘信息，开盘方向与收盘方向往往呈现高度相关性，使得模型容易退化为一种开盘价捷径，即简单地从开盘方向外推收盘方向。为解决这一问题，我们提出了\cope{}（条件化开盘先验与证据），一种仅依赖价格模态的捷径感知预测框架。\cope{}将目标日开盘状态建模为观测到的条件变量，并将收盘方向预测重参数化为在开盘方向条件下的延续/反转判别。此外，我们将输入证据分解为开盘条件、个体历史状态和上下文支持，并在该条件下对历史证据和上下文证据进行残差建模，以提炼对最终收盘判断真正有效的信息。在四个真实市场数据集上的系统性实验表明，\cope{}显著优于现有方法。消融研究和捷径敏感性分析进一步验证了显式建模开盘信息的有效性，而开盘时点回测显示预测准确性与交易收益具有一致性。
英文摘要
Stock price prediction has long been a central problem in quantitative finance. While recent methods increasingly leverage external modalities such as news to enhance predictive performance, the microstructure within price sequences itself contains crucial information. The target-day opening price represents the first concentrated realization of overnight information in the market and holds significant value for intraday evolution and the eventual closing direction. However, once opening information is introduced, the opening and closing directions often exhibit high correlation, making the model prone to degenerating into an opening-price shortcut that simply extrapolates the closing direction from the opening direction. To address this, we propose \cope{} (\textbf{C}onditional \textbf{O}pening \textbf{P}rior and \textbf{E}vidence), a shortcut-aware prediction framework that relies solely on the price modality. \cope{} models the target-day opening state as an observed conditioning variable and reparameterizes the closing-direction prediction into a continuation/reversal discrimination conditioned on the opening direction. Furthermore, we decompose the input evidence into opening condition, individual historical state, and contextual support, and perform residual modeling of historical and contextual evidence under this condition to distill information truly effective for the final closing judgment. Systematic experiments on four real-market datasets demonstrate that \cope{} significantly outperforms existing methods. Ablation studies and shortcut-sensitive analyses further validate the effectiveness of explicitly modeling opening information, while opening-time backtests reveal that predictive accuracy and trading returns align.
