Neural Network-Driven Volatility Drag Mitigation under Aggressive Leverage

2026-07-25

AI Summary

This study addresses the challenges of risk instability and poor capital efficiency under high leverage caused by volatility drag by proposing a lightweight, end-to-end neural network for solving the global minimum variance portfolio. The architecture decouples model complexity from both the lookback window length and the number of assets through a five-parameter hyperbolic weighted moving average, a saturating exponential transformation, a bidirectional gated recurrent unit for feature denoising, and a marginal volatility network. With only 2,175 learnable parameters, the model achieves the lowest realized out-of-sample portfolio variance, substantially enhancing parameter efficiency and resilience to over-leveraging. It enables higher leverage while effectively controlling drawdowns and demonstrates a material improvement in capital efficiency, as validated in a high-fidelity trading simulator.

Abstract

This paper introduces a compact reformulation of a modular end-to-end neural network for global minimum-variance portfolio optimization that decouples model complexity from both look-back window length and universe size. A five-parameter hyperbolic weighted moving average combined with a saturating exponential replaces the original 2,400-parameter lag-transformation layer, and a bidirectional gated-recurrent-unit eigencleaning module together with a streamlined marginal-volatility network reduce total learnable parameters from 39,586 to just 2,175. In out-of-sample tests against state-of-the-art nonlinear-shrinkage and risk-parity benchmarks, the compact network attains the lowest realized portfolio variance without compromising expected return. Under long-only constraints, the variance reduction supports substantially higher leverage while maintaining comparable drawdown control. Validation in a high-fidelity trading simulator that incorporates realistic margin-call dynamics confirms enhanced over-leverage resilience. These findings demonstrate that end-to-end variance-minimization architectures can achieve substantial parameter efficiency and robust capital-efficiency gains without sacrificing risk-adjusted performance.

Problem

• volatility drag
• leverage
• portfolio optimization
• risk-adjusted performance
• capital efficiency

Innovation

• parameter-efficient neural networks
• volatility drag mitigation
• minimum-variance portfolio
• leverage resilience
• end-to-end portfolio optimization