A Hybrid Machine Learning Framework for Systemic Financial Crisis Detection and Dynamic Asset Allocation

Authors:

Thakur Shubham

B.Tech — Artificial Intelligence & Machine Learning Parul University, Vadodara, Gujarat, India

Final Year Data Science Project · April 22, 2026

 Abstract:

Predicting systemic financial crises and executing optimal asset reallocation during severe market stress remains a formidable challenge in algorithmic trading and quantitative risk management. Traditional classification models frequently fail in these environments due to high false-positive rates (the “boy who cried wolf” paradigm) and severe class imbalance, which often manifests as a rigid bias toward historically dominant safe havens like Gold. This paper proposes a personal-level, non-commercial Hybrid Machine Learning pipeline designed to resolve these structural flaws. The architecture consists of two stages: a Random Forest Classifier trained to detect Black Swan events using a refined set of 11 macroeconomic indicators, and a Multi-Output Random Forest Regressor that forecasts the 3-month expected returns of four primary asset classes (S&P 500, Gold, US Dollar, and 10-Year Treasury Bonds). By forecasting exact percentage returns rather than categorising “winning” assets, the framework natively circumvents safe-haven bias without relying on synthetic data generation techniques such as SMOTE. Stress testing against historical scenarios, including the 2020 pandemic crash and theoretical deflationary liquidity crunches, demonstrates that the model executes highly rational, dynamic reallocations. Validated through strict time-series cross-validation over a 26-year timeline, the framework effectively preserves capital while maintaining a near-zero false-positive detection threshold.

Keywords: Black Swan Detection · Random Forest · Multi-Output Regression · Asset Allocation · Systemic Risk · Macroeconomic Indicators · Quantitative Finance · Walk-Forward Validation