CSPO：基于伪波动性优化的跨市场协同股票价格走势预测
摘要
作为金融市场的基石，股票价格走势的预测始终是量化金融中的前沿挑战之一。新兴的基于学习的方法在捕捉现代市场的复杂且不断演变的数据模式方面取得了显著进展。随着股票市场的快速扩张， 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.
引用
@article{arxiv.2503.22740,
title = {CSPO: Cross-Market Synergistic Stock Price Movement Forecasting with Pseudo-volatility Optimization},
author = {Sida Lin and Yankai Chen and Yiyan Qi and Chenhao Ma and Bokai Cao and Yifei Zhang and Xue Liu and Jian Guo},
journal= {arXiv preprint arXiv:2503.22740},
year = {2025}
}