Maximally Machine-Learnable Portfolios

Philippe Goulet Coulombe and Maximilian Gobel
Additional contact information
Philippe Goulet Coulombe: University of Quebec in Montreal
Maximilian Gobel: Bocconi University

No 23-01, Working Papers from Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management

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 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.

Pages: 46 pages
Date: 2023-04, Revised 2023-04
New Economics Papers: this item is included in nep-big, nep-cmp, nep-des and nep-fmk
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://chairemacro.esg.uqam.ca/wp-content/uploads ... _MACEP_v230424-1.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bbh:wpaper:23-01

Access Statistics for this paper

More papers in Working Papers from Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management Contact information at EDIRC.
Bibliographic data for series maintained by Dalibor Stevanovic and Alain Guay ().