Smart Predict–then–Optimize Paradigm for Portfolio Optimization in Real Markets

ArXiv ID: 2601.04062 “View on arXiv”

Authors: Wang Yi, Takashi Hasuike

Abstract

Improvements in return forecast accuracy do not always lead to proportional improvements in portfolio decision quality, especially under realistic trading frictions and constraints. This paper adopts the Smart Predict–then–Optimize (SPO) paradigm for portfolio optimization in real markets, which explicitly aligns the learning objective with downstream portfolio decision quality rather than pointwise prediction accuracy. Within this paradigm, predictive models are trained using an SPO-based surrogate loss that directly reflects the performance of the resulting investment decisions. To preserve interpretability and robustness, we employ linear predictors built on return-based and technical-indicator features and integrate them with portfolio optimization models that incorporate transaction costs, turnover control, and regularization. We evaluate the proposed approach on U.S. ETF data (2015–2025) using a rolling-window backtest with monthly rebalancing. Empirical results show that decision-focused training consistently improves risk-adjusted performance over predict–then–optimize baselines and classical optimization benchmarks, and yields strong robustness during adverse market regimes (e.g., the 2020 COVID-19). These findings highlight the practical value of the Smart Predict–then–Optimize paradigm for portfolio optimization in realistic and non-stationary financial environments.

Keywords: smart predict-then-optimize, portfolio optimization, surrogate loss, transaction costs, non-stationary environments, Equities

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematics, including convex optimization, SPO surrogate loss derivations, and robustness extensions, while demonstrating strong empirical rigor with a detailed rolling-window backtest on U.S. ETF data spanning 10 years, incorporating transaction costs and regime analysis.

  flowchart TD
    Goal["Research Goal: Does decision-focused training improve<br>portfolio performance vs. standard prediction?"]
    
    Data["Input Data: U.S. ETF Daily Returns & Technical Indicators<br>2015-2025"]
    
    Model["Methodology: Smart Predict-then-Optimize (SPO)"] -->
    Model["Linear Predictor + Portfolio Optimization<br>with Transaction Costs & Constraints"]
    
    Train["Computational Process: Rolling-Window Backtest<br>Train: SPO Surrogate Loss vs. MSE<br>Test: Monthly Rebalancing"]
    
    Results["Key Findings: Decision-Focused Training Outperforms"]
    
    Results --> Feat1["Higher Risk-Adjusted Returns<br>(Sharpe Ratio)"]
    Results --> Feat2["Superior Performance in Adverse Regimes<br>(e.g., 2020 COVID-19)"]
    Results --> Feat3["Robustness to Trading Frictions<br>(Transaction Costs)"]
    
    Goal --> Data
    Data --> Model
    Model --> Train
    Train --> Results