标题： 基于波动率和因果推理的预测方向交易框架 显示英文标题

标题： A Framework for Predictive Directional Trading Based on Volatility and Causal Inference

摘要： 目的：本研究引入了一种新框架，用于识别和利用金融市场中的预测性领先-滞后关系。我们提出了一种综合方法，结合先进的统计方法与机器学习模型，以增强对股票之间预测关系的识别和利用。方法：我们采用高斯混合模型（GMM）根据三年期的中等历史波动率特征对九只著名股票进行聚类。从结果聚类中，我们构建了一个多阶段因果推理管道，包括格兰杰因果检验（GCT）、定制的彼得-克拉克瞬时条件独立性（PCMCI）检验以及有效转移熵（ETE），以识别稳健的预测联系。随后，动态时间规整（DTW）和K-最近邻（KNN）分类器被用于确定最佳交易执行时间滞后。该策略进行了严格的回测。结果：从2023年6月8日到2023年8月12日测试的基于波动率的交易策略表现出显著的有效性。投资组合总回报率为15.38%，明显优于比较买入并持有策略的10.39%回报率。关键绩效指标，包括最高达2.17的夏普比率和某些组合最高达100%的胜率，证实了该策略的可行性。结论：这项研究为从基于波动率的因果关系中识别盈利交易机会提供了一种系统且稳健的方法。研究结果对金融建模的学术研究和算法交易的实际应用具有重要意义，为开发稳健的数据驱动策略提供了一种结构化方法。 显示英文摘要

摘要： 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.