Integrated Prediction and Multi-period Portfolio Optimization

• URL: http://arxiv.org/abs/2512.11273v2
• Date: Mon, 15 Dec 2025 02:16:24 GMT
• Title: Integrated Prediction and Multi-period Portfolio Optimization
• Authors: Yuxuan Linghu, Zhiyuan Liu, Qi Deng,
• Abstract summary: Multi-period portfolio optimization accounts for transaction costs, path-dependent risks, and the intertemporal structure of trading decisions.<n>This paper introduces IPMO, a model for multi-period mean-variance portfolio optimization with turnover penalties.<n>For scalability, we introduce a mirror-descent fixed-point (MDFP) differentiation scheme that avoids factorizing the Karush-Kuhn-Tucker (KKT) systems.
• Score: 29.582959310549594
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-period portfolio optimization is important for real portfolio management, as it accounts for transaction costs, path-dependent risks, and the intertemporal structure of trading decisions that single-period models cannot capture. Classical methods usually follow a two-stage framework: machine learning algorithms are employed to produce forecasts that closely fit the realized returns, and the predicted values are then used in a downstream portfolio optimization problem to determine the asset weights. This separation leads to a fundamental misalignment between predictions and decision outcomes, while also ignoring the impact of transaction costs. To bridge this gap, recent studies have proposed the idea of end-to-end learning, integrating the two stages into a single pipeline. This paper introduces IPMO (Integrated Prediction and Multi-period Portfolio Optimization), a model for multi-period mean-variance portfolio optimization with turnover penalties. The predictor generates multi-period return forecasts that parameterize a differentiable convex optimization layer, which in turn drives learning via portfolio performance. For scalability, we introduce a mirror-descent fixed-point (MDFP) differentiation scheme that avoids factorizing the Karush-Kuhn-Tucker (KKT) systems, which thus yields stable implicit gradients and nearly scale-insensitive runtime as the decision horizon grows. In experiments with real market data and two representative time-series prediction models, the IPMO method consistently outperforms the two-stage benchmarks in risk-adjusted performance net of transaction costs and achieves more coherent allocation paths. Our results show that integrating machine learning prediction with optimization in the multi-period setting improves financial outcomes and remains computationally tractable.

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