Maximally Machine-Learnable Portfolios
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 16 of its 77 references that are in the map. First posted on arXiv 8 Jun 2023.
Builds on
16 of 77 references.
References · 16
- 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
- 2018Reconciling modern machine learning practice and the bias-variance trade-offcited by 919
- 2019Benign Overfitting in Linear Regressioncited by 544
- 2019Surprises in High-Dimensional Ridgeless Least Squares Interpolationcited by 508
- 2016Generalized Random Forestscited by 309
- 2022Why do tree-based models still outperform deep learning on tabular data?cited by 198
- 2021Do We Really Need Deep Learning Models for Time Series Forecasting?cited by 45
- 2019Deep Learning in Asset Pricingcited by 41
- 2015Nonparametric Canonical Correlation Analysiscited by 22
- 2020How is Machine Learning Useful for Macroeconomic Forecasting?cited by 18
- 2020The Macroeconomy as a Random Forestcited by 15
- 2022Debiased Machine Learning without Sample-Splitting for Stable Estimatorscited by 13
4 more
- 2020Macroeconomic Data Transformations Mattercited by 7
- 2020To Bag is to Prunecited by 5
- 2022A Neural Phillips Curve and a Deep Output Gapcited by 5
- 2018Making Sense of Random Forest Probabilities: a Kernel Perspectivecited by 3
Built on by
Citing papers per year: 2023: 2; 2024: 1.
3 papers.