arXiv Machine Learning July 28, 2026 By Christian Bongiorno, Efstratios Manolakis, Rosario Nunzio Mantegna

Neural Network-Driven Volatility Drag Mitigation under Aggressive Leverage

Read the original on arXiv Machine Learning →

arXiv:2607. 23068v1 Announce Type: cross 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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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arXiv Machine Learning

6d ago

Regime-Gated Residual Mixture-of-Experts for Cross-Sectional Volatility Forecasting

arXiv:2608. 12251v1 Announce Type: cross Abstract: Financial volatility is regime dependent, yet incorporating regime information into neural networks can also destabilize training.

By Junyi Ye, Gargi Vijay Borde

arXiv Machine Learning

Jul 14

Learning Predictive Ambiguity Sets for Decision-Focused Distributionally Robust Optimization

arXiv:2607. 09820v1 Announce Type: new Abstract: Predict-then-optimize systems usually compress uncertainty into a point forecast and then solve a downstream optimization problem as if the forecast were reliable.

By Junjie Guo

arXiv Machine Learning

Aug 5

Neural Networks with Local Converging Inputs for Efficient Options Pricing Models

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By Harris Cobb, Wenbo Hao, Yingjie Liu

arXiv Machine Learning

Jul 23

STN-TGAT: Top-K Portfolio Construction via Prior-Guided Graph Attention with Learnable Soft-Threshold Sparsification

arXiv:2607. 19385v1 Announce Type: new Abstract: This paper tackles the problem of stock ranking and portfolio construction under realistic investment settings by jointly modeling temporal dynamics and cross-sectional dependencies.

By Haoran Guo, Yutong Lu, Li Zhang

llms benchmarks

arXiv Machine Learning

Jun 29

Adaptive Momentum and Nonlinear Damping for Neural Network Training

arXiv:2602. 00334v2 Announce Type: replace Abstract: Momentum Stochastic Gradient Descent (mSGD) relies on a fixed momentum coefficient shared across all parameters, failing to account for the heterogeneous structure of modern loss landscapes.

By Aikaterini Karoni, Rajit Rajpal, Benedict Leimkuhler, Gabriel Stoltz

arXiv Machine Learning

Jul 13

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility

arXiv:2510. 04487v5 Announce Type: replace Abstract: While accuracy is a critical requirement for time series forecasting, an equally important desideratum is reasonable forecast volatility across forecast creation dates (FCDs).

By Willa Potosnak, Malcolm Wolff, Mengfei Cao, Ruijun Ma, Tatiana Konstantinova, Dmitry Efimov, Michael W. Mahoney, Boris Oreshkin, Kin G. Olivares

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Regime-Gated Residual Mixture-of-Experts for Cross-Sectional Volatility Forecasting

Learning Predictive Ambiguity Sets for Decision-Focused Distributionally Robust Optimization

Neural Networks with Local Converging Inputs for Efficient Options Pricing Models

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