Asset Allocation Under Predictability and Parameter Uncertainty Using LASSO

Computational Management Science, 2020, 17(2):179-201

27 Pages Posted: 19 Oct 2018 Last revised: 31 Aug 2020

See all articles by Andrea Rigamonti

Andrea Rigamonti

Free University of Bozen-Bolzano

Alex Weissensteiner

Free University of Bolzano Bozen

Date Written: July 18, 2019

Abstract

We consider a short-term investor who exploits return predictability in stocks and bonds to maximize mean-variance utility. Since the true parameters are unknown, we resort to portfolio optimization in form of linear regression with LASSO in order to mitigate problems related to estimation errors as done by Li (2015). As standard cross-validation relies on the assumption of i.i.d. returns, we propose a new type of cross-validation that selects λ from simulated returns sampled from a multivariate normal distribution. We find an inverse U-shaped relationship between selected λ and expected utility, and we show that the optimal value of λ declines as the number of observations used to estimate the parameters increases. We finally show how our strategy outperforms some commonly employed benchmarks.

Keywords: LASSO, cross-validation, return predictability, parameter uncertainty, portfolio selection

JEL Classification: G11

Suggested Citation

Rigamonti, Andrea and Weissensteiner, Alex, Asset Allocation Under Predictability and Parameter Uncertainty Using LASSO (July 18, 2019). Computational Management Science, 2020, 17(2):179-201, Available at SSRN: https://ssrn.com/abstract=3257749 or http://dx.doi.org/10.2139/ssrn.3257749

Andrea Rigamonti (Contact Author)

Free University of Bozen-Bolzano ( email )

Universitätsplatz 1
Bozen-Bolzano, BZ 39100
Italy

Alex Weissensteiner

Free University of Bolzano Bozen ( email )

Universitätsplatz 1
Bolzano, 39100
+39 0471 013496 (Phone)

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