Machine Learning (ML)

Variable selection for minimum-variance portfolios ArXiv ID: 2508.14986 “View on arXiv” Authors: Guilherme V. Moura, André P. Santos, Hudson S. Torrent Abstract Machine learning (ML) methods have been successfully employed in identifying variables that can predict the equity premium of individual stocks. In this paper, we investigate if ML can also be helpful in selecting variables relevant for optimal portfolio choice. To address this question, we parameterize minimum-variance portfolio weights as a function of a large pool of firm-level characteristics as well as their second-order and cross-product transformations, yielding a total of 4,610 predictors. We find that the gains from employing ML to select relevant predictors are substantial: minimum-variance portfolios achieve lower risk relative to sparse specifications commonly considered in the literature, especially when non-linear terms are added to the predictor space. Moreover, some of the selected predictors that help decreasing portfolio risk also increase returns, leading to minimum-variance portfolios with good performance in terms of Shape ratios in some situations. Our evidence suggests that ad-hoc sparsity can be detrimental to the performance of minimum-variance characteristics-based portfolios. ...

Advances in Financial Machine Learning: Lecture 8/10 (seminar slides) ArXiv ID: ssrn-3270269 “View on arXiv” Authors: Unknown Abstract Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform Keywords: Machine Learning (ML), Predictive Analytics, Algorithmic Trading, Big Data, Equities Complexity vs Empirical Score Math Complexity: 8.0/10 Empirical Rigor: 3.0/10 Quadrant: Lab Rats Why: The excerpt features advanced statistical methods and formal derivations for detecting structural breaks and entropy estimation, but it lacks implementation details, backtests, or code, focusing instead on theoretical presentations suitable for academic exploration. flowchart TD Q["Research Goal: Can ML beat markets?"] D["Input: Big Data Equities"] P["Computational Process: Algorithmic Trading Models"] F["Outcome: Predictive Analytics"] E["Key Finding: Risk/Overfitting Constraints"] Q --> D D --> P P --> F F --> E

Advances in Financial Machine Learning: Lecture 5/10 (seminar slides) ArXiv ID: ssrn-3257497 “View on arXiv” Authors: Unknown Abstract Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform Keywords: Machine Learning (ML), Algorithmic Trading, Data Science, Predictive Analytics, Multi-Asset Complexity vs Empirical Score Math Complexity: 6.5/10 Empirical Rigor: 4.0/10 Quadrant: Lab Rats Why: The material features advanced statistical derivations, hypothesis testing, and combinatorial math for backtesting methods like CPCV, warranting a high math score. However, it lacks concrete code, dataset specifics, or reported backtest results, focusing instead on methodological warnings and theoretical frameworks, resulting in moderate empirical rigor. flowchart TD A["Research Goal: Assess ML Efficacy in Multi-Asset Algorithmic Trading"] --> B["Data Acquisition & Cleaning"] B --> C["Feature Engineering & Time-Series Splitting"] C --> D["Computational Process: Ensemble ML Models"] D --> E["Key Finding 1: ML Outperforms Traditional Econometrics"] D --> F["Key Finding 2: Meta-Labeling Improves Risk Management"] E --> G["Outcome: Enhanced Predictive Analytics for Financial Markets"] F --> G