Abstract

 
 

References (72)



 
 

Citations (7)



 


 



How Inefficient are Simple Asset-Allocation Strategies?


Lorenzo Garlappi


University of British Columbia - Sauder School of Business

Victor DeMiguel


London Business School

Raman Uppal


EDHEC Business School; Centre for Economic Policy Research (CEPR)

February 2005


Abstract:     
In this paper, we wish to evaluate the performance of simple asset-allocation strategies such as allocating 1/N to each of the N assets available. To do this, we compare the out-of-sample performance of such simple allocation rules to about ten models of optimal asset-allocation (including both static and dynamic models) for ten data sets. We find that the simple assetallocation rule of 1/N is not very inefficient. In fact, it performs quite well out-of-sample: it typically has a higher Sharpe ratio, a higher certainty equivalent value, and a lower turnover than the policies from the optimal asset allocation. The intuition for the good performance of the 1/N policy is that the loss from naive rather than optimal diversification is smaller than the loss arising from having to optimize using moments that have been estimated with error. Simulations show that the performance of policies from optimizing models relative to the 1/N rule improves with the length of the estimation window (which reduces estimation error) and also with N (which increases the gains from optimal diversification). But, even with an estimation window of 50 years, the difference in the performance of the 1/N policy and the policies from models of optimal asset allocation is not statistically significant.

Number of Pages in PDF File: 77

Keywords: Portfolio choice, asset allocation, investment management

JEL Classification: G11

working papers series


Download This Paper

Date posted: March 3, 2005  

Suggested Citation

Garlappi, Lorenzo, DeMiguel, Victor and Uppal , Raman, How Inefficient are Simple Asset-Allocation Strategies? (February 2005). Available at SSRN: http://ssrn.com/abstract=676997 or http://dx.doi.org/10.2139/ssrn.676997

Contact Information

Lorenzo Garlappi
University of British Columbia (UBC) - Sauder School of Business ( email )
2053 Main Mall
Vancouver, BC V6T 1Z2
Canada
Victor DeMiguel (Contact Author)
London Business School ( email )
Sussex Place
Regent's Park
London, London NW1 4SA
United Kingdom
Raman Uppal
EDHEC Business School ( email )
10 Fleet Place, Ludgate
London, EC4M 7RB
United Kingdom
+44 20 7871 6744 (Phone)
90-98 Goswell Road
London, EC1V 7RR
United Kingdom
Feedback to SSRN (Beta)


Paper statistics
Abstract Views: 3,266
Downloads: 855
Download Rank: 2,668
References:  72
Citations:  7

© 2013 Social Science Electronic Publishing, Inc. All Rights Reserved.  FAQ   Terms of Use   Privacy Policy   Copyright
This page was processed by apollo7 in 0.531 seconds