City Research Online

Abstract

Contemporaneous correlations are important for portfolio optimization problems. We propose a newly developed machine learning tool, the OWL shrinkage method, which explicitly exploits stocks’ contemporaneous correlations by assigning similar positions to correlated stocks (the grouping property). We find strong evidence that OWL-based portfolio strategies outperform other benchmark strategies in the literature when stocks exhibit strong correlations. In particular, the OWL shrinkage method bridges the gap between the naive (but well performing) 1/N portfolio strategy (DeMiguel et al., 2009b) and the portfolio optimization framework: our OWL-based portfolio strategies yield very similar portfolio weights to (yet not the same as) the 1/N portfolio strategy, but outperform the 1/N portfolio strategy in terms of both the Sharpe ratio and turnovers. We also show that the superior performance in Sharpe ratio against the 1/N portfolio is significant.

Publication Type:	Article
Additional Information:	© 2021.This article has been accepted for publication in Journal of Empirical Finance by Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
Publisher Keywords:	Portfolio Optimization, LASSO, Machine Learning, 1/N Portfolio Strategy, Stock Correlation, Norm Constraints, Model Confidence Set
Subjects:	H Social Sciences > HB Economic Theory H Social Sciences > HG Finance
Departments:	Bayes Business School > Finance
SWORD Depositor:	Symplectic Administrator

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