Purpose: This study introduces the Hybrid Volatility-Driven Statistical Arbitrage (HVSA) framework, an integrated quantitative strategy designed to enhance stock market trend prediction and exploit mean-reversion opportunities. The methodology synergistically combines advanced time-series econometrics with machine learning to generate robust and non-spurious trading signals, specifically focusing on mid-range volatility assets.

Methods: The HVSA framework employs a multi-stage approach. First, the Gaussian Mixture Model is utilized to cluster a large universe of assets based on their drift-independent realized volatility profiles, isolating those in optimal, tradable volatility regimes. Second, the Granger Causality Test is applied to the resultant clusters to rigorously identify predictive, causal linkages between asset pairs, moving beyond simple co-integration. The extracted causal features, along with advanced Volatility-of-Volatility metrics, are then fed into a Deep Neural Network classifier, which is trained using an adaptive resampling protocol to predict the directional trend of the arbitrage spread. The entire strategy is validated using a stringent forwardtesting protocol, which accounts for realistic market constraints and transaction costs.

Results: Empirical evaluation demonstrates that the HVSA framework achieves superior risk-adjusted returns compared to traditional benchmarks. The strategy’s predictive power, rooted in verified causal volatility dependencies, resulted in an Annualized Sharpe Ratio of 2.31 and a low Maximum Drawdown of 4.2% during the forwardtesting period. The Deep Neural Network, continuously retrained via an adaptive rolling-window scheme, proved highly effective in capturing the non-linear patterns of mean reversion, a task where simpler linear models often fail.

Conclusion: The HVSA framework provides compelling evidence that the integration of statistically-rigorous volatility analysis with advanced, adaptively trained machine learning classification is crucial for developing robust statistical arbitrage strategies. This hybrid methodology successfully enhances the predictability of stock market trends and offers a viable pathway for generating alpha in contemporary financial markets.

Statistical Arbitrage, Volatility, Machine Learning, Deep Neural Networks, Time-Series Econometrics, Granger Causality, Forwardtesting

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