Decision by Supervised Learning with Deep Ensembles: A Practical Framework for Robust Portfolio Optimization

ArXiv ID: 2503.13544 “View on arXiv”

Authors: Unknown

Abstract

We propose Decision by Supervised Learning (DSL), a practical framework for robust portfolio optimization. DSL reframes portfolio construction as a supervised learning problem: models are trained to predict optimal portfolio weights, using cross-entropy loss and portfolios constructed by maximizing the Sharpe or Sortino ratio. To further enhance stability and reliability, DSL employs Deep Ensemble methods, substantially reducing variance in portfolio allocations. Through comprehensive backtesting across diverse market universes and neural architectures, shows superior performance compared to both traditional strategies and leading machine learning-based methods, including Prediction-Focused Learning and End-to-End Learning. We show that increasing the ensemble size leads to higher median returns and more stable risk-adjusted performance. The code is available at https://github.com/DSLwDE/DSLwDE.

Keywords: Decision by Supervised Learning (DSL), Deep Ensemble Methods, Cross-Entropy Loss, Portfolio Optimization, Sharpe/Sortino Ratio Maximization, Portfolio Management

Complexity vs Empirical Score

• Math Complexity: 3.0/10
• Empirical Rigor: 9.0/10
• Quadrant: Street Traders
• Why: The paper is mathematically straightforward, using standard supervised learning frameworks and ensemble methods without advanced derivations, but demonstrates high empirical rigor through comprehensive backtests, diverse datasets, and an open-source GitHub repository.

  flowchart TD
    A["Research Goal: Robust Portfolio Optimization via Supervised Learning"] --> B["Methodology: Decision by Supervised Learning DSL Deep Ensemble"]
    B --> C{"Input: Historical Market Data"}
    C --> D["Compute Target Portfolios<br>Maximizing Sharpe/Sortino Ratio"]
    D --> E["Train Ensemble Models<br>Cross-Entropy Loss on Weights"]
    E --> F["Ensemble Aggregation<br>Reduce Variance & Improve Stability"]
    F --> G["Backtest on Diverse Universes"]
    G --> H["Key Findings: Superior Risk-Adjusted Returns<br>Stability Increases with Ensemble Size"]