A Practitioner’s Guide to AI+ML in Portfolio Investing

ArXiv ID: 2509.25456 “View on arXiv”

Authors: Mehmet Caner Qingliang Fan

Abstract

In this review, we provide practical guidance on some of the main machine learning tools used in portfolio weight formation. This is not an exhaustive list, but a fraction of the ones used and have some statistical analysis behind it. All this research is essentially tied to precision matrix of excess asset returns. Our main point is that the techniques should be used in conjunction with outlined objective functions. In other words, there should be joint analysis of Machine Learning (ML) technique with the possible portfolio choice-objective functions in terms of test period Sharpe Ratio or returns. The ML method with the best objective function should provide the weight for portfolio formation. Empirically we analyze five time periods of interest, that are out-sample and show performance of some ML-Artificial Intelligence (AI) methods. We see that nodewise regression with Global Minimum Variance portfolio based weights deliver very good Sharpe Ratio and returns across five time periods in this century we analyze. We cover three downturns, and 2 long term investment spans.

Keywords: machine learning in finance, portfolio weight formation, precision matrix, nodewise regression, Sharpe Ratio maximization, Portfolio Management

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 6.5/10
• Quadrant: Holy Grail
• Why: The paper uses advanced econometrics (precision matrix estimation, shrinkage estimators, nodewise regression) with heavy formulas and derivations, while also presenting an empirical analysis across multiple time periods with performance metrics like Sharpe Ratio.

  flowchart TD
    A["Research Goal: Guide for using ML/AI in portfolio weight formation via Precision Matrix of excess asset returns"] --> B["Key Methodology: <br>1. Select ML Technique<br>2. Choose Objective Function<br>3. Joint Analysis"]
    B --> C["Data: Excess Asset Returns"]
    C --> D["Computational Process: <br>Construct Precision Matrix using <br>Nodewise Regression"]
    D --> E["Portfolio Formation: <br>Apply weights to Global Minimum Variance Portfolio"]
    E --> F["Evaluation: <br>Test Period Sharpe Ratio & Returns"]
    F --> G["Key Findings: <br>Nodewise Regression + GMVP delivers <br>superior Sharpe & Returns across 5 time periods (3 downturns, 2 long spans)"]