基于波动率与因果推断的预测性交易框架，提升股票间领先滞后关系识别精度。
A Framework for Predictive Directional Trading Based on Volatility and Causal Inference
- 用高斯混合模型按波动率聚类九只股票，构建多阶段因果推断流程。
- 6月到8月回测中收益达15.38%，显著高于买持有策略的10.39%。
- 适合量化交易研究者和算法交易开发者参考，尤其关注因果关系建模。
本文提出一种新型框架，用于识别金融市场的预测性领先-滞后关系。采用高斯混合模型（GMM）对九只主要股票在过去三年的中期波动率特征进行聚类。基于聚类结果，构建包含格兰杰因果检验（GCT）、定制化Peter-Clark瞬时条件独立性（PCMCI）测试和有效转移熵（ETE）的多阶段因果推断流程，以识别稳健的预测性关联。随后利用动态时间规整（DTW）和K近邻（KNN）分类器确定最优交易时滞。该策略在2023年6月8日至8月12日期间进行了严格回测，总收益率达15.38%，显著优于对比的买入并持有策略的10.39%。关键绩效指标显示，夏普比率最高达2.17，部分股票对胜率高达100%，验证了策略可行性。本研究为基于波动率的因果关系挖掘提供了系统化、可复现的交易方法，对金融建模研究与算法交易实践均有重要价值。
原文摘要 · Abstract (English)
Purpose: This study introduces a novel framework for identifying and exploiting predictive lead-lag relationships in financial markets. We propose an integrated approach that combines advanced statistical methodologies with machine learning models to enhance the identification and exploitation of predictive relationships between equities. Methods: We employed a Gaussian Mixture Model (GMM) to cluster nine prominent stocks based on their mid-range historical volatility profiles over a three-year period. From the resulting clusters, we constructed a multi-stage causal inference pipeline, incorporating the Granger Causality Test (GCT), a customised Peter-Clark Momentary Conditional Independence (PCMCI) test, and Effective Transfer Entropy (ETE) to identify robust, predictive linkages. Subsequently, Dynamic Time Warping (DTW) and a K-Nearest Neighbours (KNN) classifier were utilised to determine the optimal time lag for trade execution. The resulting strategy was rigorously backtested. Results: The proposed volatility-based trading strategy, tested from 8 June 2023 to 12 August 2023, demonstrated substantial efficacy. The portfolio yielded a total return of 15.38%, significantly outperforming the 10.39% return of a comparative Buy-and-Hold strategy. Key performance metrics, including a Sharpe Ratio up to 2.17 and a win rate up to 100% for certain pairs, confirmed the strategy's viability. Conclusion: This research contributes a systematic and robust methodology for identifying profitable trading opportunities derived from volatility-based causal relationships. The findings have significant implications for both academic research in financial modelling and the practical application of algorithmic trading, offering a structured approach to developing resilient, data-driven strategies.
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