Supervised portfolios. Issue 12 (2nd December 2022)

• Record Type: Journal Article
• Authors: Chevalier, Guillaume; Coqueret, Guillaume; Raffinot, Thomas
• Is Part Of: Quantitative finance. Volume 22:Issue 12(2022)
• Page Start: 2275
• Page End: 2295
• Publication Date: 2022-12-02
• Subjects: Portfolio choice -- Supervised learning -- Boosted trees -- Asset allocation; Finance -- Periodicals; Business mathematics -- Periodicals; Finance -- Mathematical models -- Periodicals; Investments -- Mathematics -- Periodicals; Economics -- Periodicals; Finances -- Modèles mathématiques -- Périodiques; 332.015118
• DOI: 10.1080/14697688.2022.2122543
• Languages: English
• ISSNs: 1469-7688

Abstract

Abstract : We propose an asset allocation strategy that engineers optimal weights before feeding them to a supervised learning algorithm. In contrast to the traditional approaches, the machine is able to learn risk measures, preferences, and constraints beyond simple expected returns, within a flexible, forward-looking, and non-linear framework. Our empirical analysis illustrates that predicting the optimal weights directly instead of the traditional two-step approach leads to more stable portfolios with statistically better risk-adjusted performance measures.