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

TL;DR

This paper introduces a decision-focused predictive modeling approach for portfolio optimization that directly aligns model training with investment decision quality, improving risk-adjusted returns in real market conditions.

Contribution

It proposes a novel Smart Predict--then--Optimize paradigm using surrogate loss functions for portfolio decision-making, enhancing robustness and performance over traditional methods.

Findings

Decision-focused training improves risk-adjusted returns

Method shows robustness during market downturns

Outperforms classical optimization benchmarks

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.