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Optimal Granularity for Portfolio Choice

25 Pages Posted: 25 Apr 2016 Last revised: 15 Jul 2016

Nicole Branger

University of Muenster - Finance Center Muenster

Katarina Lucivjanska

University of Pavol Jozef Šafárik in Kosice

Alex Weissensteiner

Free University of Bolzano/Bozen

Date Written: July 13, 2016

Abstract

Many optimization-based portfolio rules fail to beat the simple 1/N rule out-of-sample because of parameter uncertainty. In this paper we suggest a grouping strategy in which we first form groups of equally weighted stocks and then optimize over the resulting groups only. In a simplified setting we show analytically how to optimize the trade-off between drawbacks from parameter uncertainty and drawbacks from deviating from the overall optimal asset allocation. We illustrate that the optimal group size depends on the volatility of the assets, on the number of observations and on how much the optimal asset allocation differs from 1/N. Out of sample back-tests confirm the validity of our grouping strategy empirically.

Keywords: mean-variance optimization, the 1/N rule, parameter uncertainty, optimal portfolio granularity

JEL Classification: G1, G11

Suggested Citation

Branger, Nicole and Lucivjanska, Katarina and Weissensteiner, Alex, Optimal Granularity for Portfolio Choice (July 13, 2016). Available at SSRN: https://ssrn.com/abstract=2769736

Nicole Branger

University of Muenster - Finance Center Muenster ( email )

Universitatsstr. 14-16
Muenster, 48143
Germany
+49 251 83 29779 (Phone)
+49 251 83 22867 (Fax)

HOME PAGE: http://www.wiwi.uni-muenster.de/fcm/fcm/das-finance-center/details.php?weobjectID=162

Katarina Lucivjanska (Contact Author)

University of Pavol Jozef Šafárik in Kosice ( email )

Šrobárova 2
Košice, 041 32
Slovakia

Alex Weissensteiner

Free University of Bolzano/Bozen ( email )

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

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