Paper: arXiv 2311.01985

Abstract

We construct the maximally predictable portfolio (MPP) of stocks using machine learning. Solving for the optimal constrained weights in the multi-asset MPP gives portfolios with a high monthly coefficient of determination, given the sample covariance matrix of predicted return errors from a machine learning model. Various models for the covariance matrix are tested. The MPPs of S&P 500 index constituents with estimated returns from Elastic Net, Random Forest, and Support Vector Regression models can outperform or underperform the index depending on the time period. Portfolios that take advantage of the high predictability of the MPP’s returns and employ a Kelly criterion style strategy consistently outperform the benchmark.

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced matrix algebra, convex optimization, and rigorous derivations (e.g., eigenvectors, KKT conditions) but is grounded in a detailed, multi-stage empirical implementation with a strict holdout set, out-of-sample validation, and specific ML models for S&P 500 stocks.

Research Flowchart

  flowchart TD
  A["Research Goal<br>Maximize Portfolio Predictability"] --> B["Input: S&P 500 Constituents & Historical Data"]
  B --> C["Compute Predicted Returns<br>Elastic Net | Random Forest | SVR"]
  C --> D["Estimate Covariance Matrix<br>of Prediction Errors"]
  D --> E["Optimization<br>Construct Maximally Predictable Portfolio MPP"]
  E --> F["Strategy: MPP + Kelly Criterion"]
  F --> G["Outcome<br>Consistent Outperformance vs Benchmark"]
  G --> H["Key Finding<br>High R<sup>2</sup> enables predictive strategy"]
  
  style A fill:#e1f5fe
  style H fill:#e8f5e8