Maximally Machine-Learnable Portfolios

ArXiv ID: 2306.05568 “View on arXiv”

Authors: Unknown

Abstract

When it comes to stock returns, any form of predictability can bolster risk-adjusted profitability. We develop a collaborative machine learning algorithm that optimizes portfolio weights so that the resulting synthetic security is maximally predictable. Precisely, we introduce MACE, a multivariate extension of Alternating Conditional Expectations that achieves the aforementioned goal by wielding a Random Forest on one side of the equation, and a constrained Ridge Regression on the other. There are two key improvements with respect to Lo and MacKinlay’s original maximally predictable portfolio approach. First, it accommodates for any (nonlinear) forecasting algorithm and predictor set. Second, it handles large portfolios. We conduct exercises at the daily and monthly frequency and report significant increases in predictability and profitability using very little conditioning information. Interestingly, predictability is found in bad as well as good times, and MACE successfully navigates the debacle of 2022.

Keywords: Alternating Conditional Expectations, Random Forest, Ridge Regression, Portfolio Optimization, Return Predictability, Equities

Complexity vs Empirical Score

  • Math Complexity: 8.5/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail
  • Why: The paper introduces MACE, a novel multivariate extension of Alternating Conditional Expectations, involving advanced concepts like constrained Ridge Regression and Random Forests, which requires dense mathematical formulation. It demonstrates strong empirical rigor through backtests across daily and monthly frequencies, using specific datasets (e.g., NASDAQ stocks) and reporting performance metrics like R² and Sharpe ratios, including results from the challenging 2022 period.
  flowchart TD
    A["Research Goal<br>Maximally Machine-Learnable Portfolios"] --> B["Key Methodology<br>MACE Algorithm"]
    B --> C{"Process Flow"}
    C --> D["Input: Asset Returns<br>&amp; Predictors"]
    C --> E["Random Forest<br>Nonlinear Feature Extraction"]
    C --> F["Constrained Ridge Regression<br>Portfolio Optimization"]
    D &amp; E &amp; F --> G["Output: Optimized Portfolio Weights"]
    G --> H["Key Findings &amp; Outcomes"]
    
    H --> I["Predictability in<br>Good &amp; Bad Times"]
    H --> J["Significant Profitability<br>Boost"]
    H --> K["Robust Performance<br>e.g., 2022 Debacle"]