Causal and Predictive Modeling of Short-Horizon Market Risk and Systematic Alpha Generation Using Hybrid Machine Learning Ensembles

ArXiv ID: 2510.22348 “View on arXiv”

Authors: Aryan Ranjan

Abstract

We present a systematic trading framework that forecasts short-horizon market risk, identifies its underlying drivers, and generates alpha using a hybrid machine learning ensemble built to trade on the resulting signal. The framework integrates neural networks with tree-based voting models to predict five-day drawdowns in the S&P 500 ETF, leveraging a cross-asset feature set spanning equities, fixed income, foreign exchange, commodities, and volatility markets. Interpretable feature attribution methods reveal the key macroeconomic and microstructural factors that differentiate high-risk (crash) from benign (non-crash) weekly regimes. Empirical results show a Sharpe ratio of 2.51 and an annualized CAPM alpha of +0.28, with a market beta of 0.51, indicating that the model delivers substantial systematic alpha with limited directional exposure during the 2005–2025 backtest period. Overall, the findings underscore the effectiveness of hybrid ensemble architectures in capturing nonlinear risk dynamics and identifying interpretable, potentially causal drivers, providing a robust blueprint for machine learning-driven alpha generation in systematic trading.

Keywords:

Complexity vs Empirical Score

  • Math Complexity: 8.5/10
  • Empirical Rigor: 9.0/10
  • Quadrant: Holy Grail
  • Why: The paper employs advanced statistical measures like Hurst exponents and Kullback-Leibler divergence within a hybrid ML architecture, while providing a fully backtested strategy with specific metrics (Sharpe 2.51, alpha +0.28) over a 20-year period using cross-asset data.
  flowchart TD
A["Research Goal: Forecast short-horizon market risk & generate systematic alpha using ML"] --> B["Data: Cross-asset features 2005-2025 S&P 500 ETF"]
B --> C["Methodology: Hybrid ML Ensemble<br/>Neural Networks + Tree-Based Voting"]
C --> D["Process: Predict 5-day drawdowns & identify key drivers"]
D --> E["Outcomes: Sharpe 2.51, CAPM Alpha +0.28, Beta 0.51"]
E --> F["Conclusion: Interpretable, causal drivers for robust alpha generation"]