Neural Network-Driven Volatility Drag Mitigation under Aggressive Leverage

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.

7 pages, 2 figures, 1 table. Published in ICAIF '25. Code: this https URL

Portfolio Management (q-fin.PM); Machine Learning (cs.LG)

arXiv:2607.23068 [q-fin.PM]

(or arXiv:2607.23068v1 [q-fin.PM] for this version)

https://doi.org/10.48550/arXiv.2607.23068 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

Proceedings of the 6th ACM International Conference on AI in Finance (ICAIF '25), pp. 449-455, 2025

https://doi.org/10.1145/3768292.3770370 Focus to learn more DOI(s) linking to related resources

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