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A machine learning approach to risk based asset allocation in portfolio optimization.

Sanjay Agal1, Krishna Raulji2, Niyati Dhirubhai Odedra3

  • 1Department of Artificial Intelligence and Data Science, Faculty of Engineering and Technology, Parul University, Vadodara, Gujarat, India. sanjay.agal32685@paruluniversity.ac.in.

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Summary
This summary is machine-generated.

This study introduces a novel machine learning framework for dynamic risk-based asset allocation, outperforming traditional and deep learning methods. The adaptive strategy enhances risk-adjusted returns and reduces drawdowns in volatile markets.

Keywords:
Adaptive risk budgetingDifferentiable portfolio optimizationDynamic covariance forecastingExplainable AI in financeMachine learning scalabilityMulti-asset allocationNeural financial engineeringRegime-switching modelsSparse attention mechanismsTemporal pattern recognition

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Area of Science:

  • Quantitative Finance
  • Machine Learning
  • Computational Finance

Background:

  • Traditional portfolio optimization methods face limitations due to static risk budgets and historical data reliance.
  • Existing approaches struggle to adapt dynamically to evolving market conditions and real-time indicators.

Purpose of the Study:

  • Introduce a novel machine learning framework for dynamic risk-based asset allocation.
  • Address limitations of conventional portfolio optimization by enabling adaptive risk constraints.
  • Achieve superior risk-adjusted performance, computational efficiency, and model interpretability.

Main Methods:

  • Integration of Long Short-Term Memory (LSTM) networks for volatility forecasting.
  • Implementation of differentiable risk budgeting layers and regime-switching mechanisms.
  • End-to-end training of portfolio weights with adaptive risk constraints and sparse attention mechanisms.

Main Results:

  • Achieved a Sharpe ratio of 1.38, outperforming traditional risk parity (55%) and deep learning (23%) strategies.
  • Demonstrated computational efficiency, processing 50-asset portfolios in under 25 milliseconds.
  • Reduced maximum drawdowns by 41% during stress periods and showed proactive risk management during the COVID-19 crisis.

Conclusions:

  • The novel framework establishes a new paradigm for portfolio optimization, bridging finance theory and practice.
  • The model's ability to navigate complex markets with efficiency and interpretability suggests institutional readiness.
  • Provides portfolio managers with a robust tool for adaptive, risk-aware investment strategies.