Abstract
The stock market, as a cornerstone of the financial markets, places forecasting stock price movements at the forefront of challenges in quantitative finance. Emerging learning-based approaches have made significant progress in capturing the intricate and ever-evolving data patterns of modern markets. With the rapid expansion of the stock market, it presents two characteristics, i.e., stock exogeneity and volatility heterogeneity, that heighten the complexity of price forecasting. Specifically, while stock exogeneity reflects the influence of external market factors on price movements, volatility heterogeneity showcases the varying difficulty in movement forecasting against price fluctuations. In this work, we introduce the framework of Cross-market Synergy with Pseudo-volatility Optimization (CSPO). Specifically, CSPO implements an effective deep neural architecture to leverage external futures knowledge. This enriches stock embeddings with cross-market insights and thus enhances the CSPO's predictive capability. Furthermore, CSPO incorporates pseudo-volatility to model stock-specific forecasting confidence, enabling a dynamic adaptation of its optimization process to improve accuracy and robustness. Our extensive experiments, encompassing industrial evaluation and public benchmarking, highlight CSPO's superior performance over existing methods and effectiveness of all proposed modules contained therein.
Abstract (translated)
股市作为金融市场的重要基石，将预测股价变动视为数量金融领域的主要挑战之一。基于学习的方法在捕捉现代市场复杂且不断变化的数据模式方面取得了显著进展。随着股市的迅速扩张，它呈现出两个特性：即股票外生性和波动性异质性，这增加了价格预测的复杂度。具体而言，股票外生性反映了外部市场因素对股价变动的影响，而波动性异质性则展示了在面对不同价格波动时进行预测难度的不同。 在此研究中，我们提出了跨市场协同伪波动优化（Cross-market Synergy with Pseudo-volatility Optimization, CSPO）框架。具体来说，CSPO 实现了一种有效的深度神经网络架构来利用外部期货知识，这丰富了股票嵌入信息并融入了跨市场的见解，从而增强了 CSPO 的预测能力。此外，CSPO 还采用伪波动率建模特定股票的预测信心水平，使其优化过程能够根据实际情况动态调整以提高准确性和鲁棒性。 我们进行了广泛的实验，包括工业评估和公共基准测试，结果表明与现有方法相比，CSPO 在性能上具有显著优势，并证实了其内部所有模块的有效性。
URL
https://arxiv.org/abs/2503.22740
