Integrated Prediction and Multi-period Portfolio Optimization

arXiv:2512.11273 · cs.CE, cs.LG · Submitted 2025-12-12 · Read on arXiv

cs.CE, cs.LG

Submitted: 2025-12-12

Updated: 2026-09-09

Comments: 23 pages, 6 figures, and 4 tables

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 8× in this paper · explained in 2 episodes
• Mirror Descent — 7× in this paper
• Accuracy — 6× in this paper · explained in 1 episode
• Adam — 4× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 3× in this paper · explained in 2 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• Reinforcement Learning — 3× in this paper · explained in 4 episodes
• Two-Stage Framework — 3× in this paper · explained in 2 episodes
• Computational overhead — 2× in this paper · explained in 2 episodes
• Distribution Shift — 2× in this paper · explained in 5 episodes
• Model Predictive Control (MPC) — 2× in this paper · explained in 5 episodes
• Time Series Forecasting — 2× in this paper · explained in 3 episodes
• Computational Tractability — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Convolutional Neural Network — 1× in this paper
• Core — 1× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Degeneracy — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Integrated Prediction and Multi-Period Optimization — 1× in this paper
• Interpretability — 1× in this paper · explained in 15 episodes
• Long Short-Term Memory — 1× in this paper
• Loss Function — 1× in this paper · explained in 2 episodes
• Predictive Modeling — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• System Identification — 1× in this paper · explained in 3 episodes

Sources

• End-to-End Large Portfolio Optimization for Variance Minimization with Neural Networks through Covariance Cleaning
• Reducing Estimation Risk in Mean-Variance Portfolios with Machine Learning
• Return Prediction for Mean-Variance Portfolio Selection: How Decision-Focused Learning Shapes Forecasting Models
• Revisiting Long-term Time Series Forecasting: An Investigation on Linear Mapping
• Differentiation Through Black-Box Quadratic Programming Solvers

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