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

arXiv: https://arxiv.org/abs/2507.09347 · HTML: https://mlsanity.com/paper/2507.09347/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 22 references that are in the map.
- First posted on arXiv 12 Jul 2025.

## Builds on (most cited first, 4 of 4)

- [A Logic-based Multi-agent System for Ethical Monitoring and Evaluation of Dialogues](https://mlsanity.com/paper/2109.08294/) (2021)
- [Dataset Optimization Strategies for MalwareTraffic Detection](https://mlsanity.com/paper/2009.11347/) (2020)
- [A Stock Trading System for a Medium Volatile Asset using Multi Layer Perceptron](https://mlsanity.com/paper/2201.12286/) (2022)
- [DNN-ForwardTesting: A New Trading Strategy Validation using Statistical Timeseries Analysis and Deep Neural Networks](https://mlsanity.com/paper/2210.11532/) (2022)

## Abstract (arXiv)

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.