Can We Give the Maximum Sharpe Ratio Portfolio a Chance?

Winfried Pohlmeier; Katya Kazak

doi:10.1007/978-3-031-69111-9_16

Can We Give the Maximum Sharpe Ratio Portfolio a Chance?

Winfried Pohlmeier^*, Katya Kazak

^*Corresponding author for this work

Economics

Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed) › peer-review

Abstract

This chapter studies the applicability of the maximum Sharpe ratio (MaxSR) portfolio strategy in real-world settings. As shown by Okhrin and Schmidt the plug-in estimated weights show abysmal distributional properties such that it renders an application impossible for financial practitioners. In this chapter we propose a double regularization approach for the MaxSR portfolio strategy based on the bagged pretested portfolio selection (BPPS) algorithm. We show that for certain settings the doubly shrunken portfolio weights strongly mitigate the adverse properties of the plug-in estimated weights and can beat the popular 1/N benchmark strategy.

Original language	English
Title of host publication	Advanced Statistical Methods in Process Monitoring, Finance, and Environmental Science
Subtitle of host publication	Essays in Honour of Wolfgang Schmid
Editors	Sven Knoth, Yarema Okhrin, Philipp Otto
Publisher	Springer
Pages	337–366
Number of pages	30
Edition	1
ISBN (Electronic)	9783031691119
ISBN (Print)	9783031691102, 9783031691133
DOIs	https://doi.org/10.1007/978-3-031-69111-9_16
Publication status	Published - 23 Oct 2024

Access to Document

10.1007/978-3-031-69111-9_16

Cite this

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title = "Can We Give the Maximum Sharpe Ratio Portfolio a Chance?",

abstract = "This chapter studies the applicability of the maximum Sharpe ratio (MaxSR) portfolio strategy in real-world settings. As shown by Okhrin and Schmidt the plug-in estimated weights show abysmal distributional properties such that it renders an application impossible for financial practitioners. In this chapter we propose a double regularization approach for the MaxSR portfolio strategy based on the bagged pretested portfolio selection (BPPS) algorithm. We show that for certain settings the doubly shrunken portfolio weights strongly mitigate the adverse properties of the plug-in estimated weights and can beat the popular 1/N benchmark strategy.",

author = "Winfried Pohlmeier and Katya Kazak",

year = "2024",

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doi = "10.1007/978-3-031-69111-9_16",

language = "English",

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pages = "337–366",

editor = "Sven Knoth and Yarema Okhrin and Philipp Otto",

booktitle = "Advanced Statistical Methods in Process Monitoring, Finance, and Environmental Science",

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Can We Give the Maximum Sharpe Ratio Portfolio a Chance? / Pohlmeier, Winfried; Kazak, Katya.
Advanced Statistical Methods in Process Monitoring, Finance, and Environmental Science: Essays in Honour of Wolfgang Schmid. ed. / Sven Knoth; Yarema Okhrin; Philipp Otto. 1. ed. Springer, 2024. p. 337–366.

Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed) › peer-review