
Contents
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
23 On the Psychology of the Recognition Heuristic: Retrieval Primacy as a Key Determinant of Its Use
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
1. DESCRIPTION OF THE ASSET-ALLOCATION MODELS CONSIDERED 1. DESCRIPTION OF THE ASSET-ALLOCATION MODELS CONSIDERED
-
1.1 Naive Portfolio 1.1 Naive Portfolio
-
1.2 Sample-Based Mean-Variance Portfolio 1.2 Sample-Based Mean-Variance Portfolio
-
1.3 Bayesian Approach to Estimation Error 1.3 Bayesian Approach to Estimation Error
-
1.3.1 Bayesian diffuse-prior portfolio 1.3.1 Bayesian diffuse-prior portfolio
-
1.3.2 Bayes-Stein shrinkage portfolio 1.3.2 Bayes-Stein shrinkage portfolio
-
1.3.3 Bayesian portfolio based on belief in an asset-pricing model 1.3.3 Bayesian portfolio based on belief in an asset-pricing model
-
-
1.4 Portfolios with Moment Restrictions 1.4 Portfolios with Moment Restrictions
-
1.4.1 Minimum-variance portfolio 1.4.1 Minimum-variance portfolio
-
1.4.2 Value-weighted portfolio implied by the market model 1.4.2 Value-weighted portfolio implied by the market model
-
1.4.3 Portfolio implied by asset-pricing models with unobservable factors 1.4.3 Portfolio implied by asset-pricing models with unobservable factors
-
-
1.5 Shortsale-Constrained Portfolios 1.5 Shortsale-Constrained Portfolios
-
1.6 Optimal Combination of Portfolios 1.6 Optimal Combination of Portfolios
-
1.6.1 The Kan and Zhou (2007) three-fund portfolio 1.6.1 The Kan and Zhou (2007) three-fund portfolio
-
1.6.2 Mixture of equally weighted and minimum-variance portfolios 1.6.2 Mixture of equally weighted and minimum-variance portfolios
-
-
-
2. METHODOLOGY FOR EVALUATING PERFORMANCE 2. METHODOLOGY FOR EVALUATING PERFORMANCE
-
3. RESULTS FROM THE SEVEN EMPIRICAL DATASETS CONSIDERED 3. RESULTS FROM THE SEVEN EMPIRICAL DATASETS CONSIDERED
-
3.1 Sharpe Ratios 3.1 Sharpe Ratios
-
3.2 Certainty Equivalent Returns 3.2 Certainty Equivalent Returns
-
3.3 Portfolio Turnover 3.3 Portfolio Turnover
-
3.4 Summary of Findings from the Empirical Datasets 3.4 Summary of Findings from the Empirical Datasets
-
-
4. RESULTS FROM STUDYING ANALYTICALLY THE ESTIMATION ERROR 4. RESULTS FROM STUDYING ANALYTICALLY THE ESTIMATION ERROR
-
5. RESULTS FOR OTHER SPECIFICATIONS: ROBUSTNESS CHECKS 5. RESULTS FOR OTHER SPECIFICATIONS: ROBUSTNESS CHECKS
-
6. CONCLUSIONS 6. CONCLUSIONS
-
ACKNOWLEDGMENTS ACKNOWLEDGMENTS
-
NOTES NOTES
-
APPENDIX A APPENDIX A
-
Description of the Seven Empirical Datasets Description of the Seven Empirical Datasets
-
A.1 Sector portfolios A.1 Sector portfolios
-
A.2 Industry portfolios A.2 Industry portfolios
-
A.3 International equity indexes A.3 International equity indexes
-
A.4 MKT, SMB, and HML portfolios A.4 MKT, SMB, and HML portfolios
-
A.5 Size- and book-to-market-sorted portfolios A.5 Size- and book-to-market-sorted portfolios
-
-
-
-
-
-
-
-
-
-
-
-
-
34 Optimal versus Naive Diversification: How Inefficient Is the 1/N Portfolio Strategy?
Get access-
Published:April 2011
Cite
Abstract
This chapter evaluates the out-of-sample performance of the sample-based mean-variance model, and its extensions designed to reduce estimation error, relative to the naive 1/N portfolio. Of the fourteen models the chapter evaluates across seven empirical datasets, none is consistently better than the 1/N rule in terms of Sharpe ratio, certainty-equivalent return, or turnover, which indicates that, out of sample, the gain from optimal diversification is more than offset by estimation error. Based on parameters calibrated to the US equity market, the analytical results and simulations show that the estimation window needed for the sample-based mean-variance strategy and its extensions to outperform the 1/N benchmark is around 3000 months for a portfolio with Twenty-five assets and about 6000 months for a portfolio with fifty assets. This suggests that there are still many “miles to go” before the gains promised by optimal portfolio choice can actually be realized out of sample.
Sign in
Personal account
- Sign in with email/username & password
- Get email alerts
- Save searches
- Purchase content
- Activate your purchase/trial code
- Add your ORCID iD
Purchase
Our books are available by subscription or purchase to libraries and institutions.
Purchasing informationMonth: | Total Views: |
---|---|
October 2022 | 11 |
November 2022 | 2 |
December 2022 | 3 |
January 2023 | 4 |
February 2023 | 8 |
March 2023 | 21 |
April 2023 | 11 |
May 2023 | 5 |
June 2023 | 2 |
July 2023 | 4 |
August 2023 | 1 |
September 2023 | 2 |
October 2023 | 5 |
November 2023 | 7 |
December 2023 | 2 |
February 2024 | 1 |
March 2024 | 3 |
April 2024 | 17 |
May 2024 | 5 |
June 2024 | 8 |
July 2024 | 7 |
August 2024 | 2 |
September 2024 | 2 |
November 2024 | 4 |
December 2024 | 2 |
January 2025 | 2 |
March 2025 | 11 |
April 2025 | 7 |
May 2025 | 2 |
Get help with access
Institutional access
Access to content on Oxford Academic is often provided through institutional subscriptions and purchases. If you are a member of an institution with an active account, you may be able to access content in one of the following ways:
IP based access
Typically, access is provided across an institutional network to a range of IP addresses. This authentication occurs automatically, and it is not possible to sign out of an IP authenticated account.
Sign in through your institution
Choose this option to get remote access when outside your institution. Shibboleth/Open Athens technology is used to provide single sign-on between your institution’s website and Oxford Academic.
If your institution is not listed or you cannot sign in to your institution’s website, please contact your librarian or administrator.
Sign in with a library card
Enter your library card number to sign in. If you cannot sign in, please contact your librarian.
Society Members
Society member access to a journal is achieved in one of the following ways:
Sign in through society site
Many societies offer single sign-on between the society website and Oxford Academic. If you see ‘Sign in through society site’ in the sign in pane within a journal:
If you do not have a society account or have forgotten your username or password, please contact your society.
Sign in using a personal account
Some societies use Oxford Academic personal accounts to provide access to their members. See below.
Personal account
A personal account can be used to get email alerts, save searches, purchase content, and activate subscriptions.
Some societies use Oxford Academic personal accounts to provide access to their members.
Viewing your signed in accounts
Click the account icon in the top right to:
Signed in but can't access content
Oxford Academic is home to a wide variety of products. The institutional subscription may not cover the content that you are trying to access. If you believe you should have access to that content, please contact your librarian.
Institutional account management
For librarians and administrators, your personal account also provides access to institutional account management. Here you will find options to view and activate subscriptions, manage institutional settings and access options, access usage statistics, and more.