A toolkit for exploiting contemporaneous stock correlations

Kazuhiro Hiraki and Chuanping Sun

Journal of Empirical Finance, 2022, vol. 65, issue C, 99-124

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 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.

Keywords: Portfolio optimization; LASSO; Machine learning; 1/N portfolio strategy; Stock correlation; Norm constraints; Model confidence set (search for similar items in EconPapers)
JEL-codes: C61 G11 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:empfin:v:65:y:2022:i:c:p:99-124

DOI: 10.1016/j.jempfin.2021.11.003

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