The optimization of a quadratic function subject to linear constraints
01 Mar 1956-Naval Research Logistics Quarterly (Wiley Subscription Services, Inc., A Wiley Company)-Vol. 3, pp 111-133
About: This article is published in Naval Research Logistics Quarterly.The article was published on 1956-03-01. It has received 501 citations till now. The article focuses on the topics: Quadratic programming & Quadratically constrained quadratic program.
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3,154 citations
TL;DR: In this article, the authors evaluate 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.
Abstract: We evaluate 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 14 models we evaluate 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, our 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 25 assets and about 6000 months for a portfolio with 50 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. The Author 2007. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org, Oxford University Press.
2,809 citations
Cites background from "The optimization of a quadratic fun..."
...…because it does not rely either on estimation of the moments of asset returns or on 2 Some of the results on mean-variance portfolio choice in Markowitz (1952, 1956, 1959) and Roy (1952) had already been anticipated in 1940 by de Finetti, an English translation of which is now available in…...
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TL;DR: Preliminary evidence suggests that the relatively few parameters used by the model can lead to very nearly the same results obtained with much larger sets of relationships among securities, as well as the possibility of low-cost analysis.
Abstract: This paper describes the advantages of using a particular model of the relationships among securities for practical applications of the Markowitz portfolio analysis technique. A computer program has been developed to take full advantage of the model: 2,000 securities can be analyzed at an extremely low cost—as little as 2% of that associated with standard quadratic programming codes. Moreover, preliminary evidence suggests that the relatively few parameters used by the model can lead to very nearly the same results obtained with much larger sets of relationships among securities. The possibility of low-cost analysis, coupled with a likelihood that a relatively small amount of information need be sacrificed make the model an attractive candidate for initial practical applications of the Markowitz technique.
2,545 citations
Cites background from "The optimization of a quadratic fun..."
...The number of securities analyzed This will affect the extent of the computation in step (2) and the number of computations in step (3)....
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...Introduction Markowitz has suggested that the process of portfolio selection be approached by (1) making probabilistic estimates of the future performances of securities, (2) analyzing those estimates to determine an efficient set of portfolios and (3) selecting from that set the portfolios best suited to the investor's preferences [1, 2, 3]....
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01 Jan 1961TL;DR: In place of a survey or evaluation of industrial studies, two broad issues which are relevant to all such applications will be discussed, including the use of linear programming models as guides to data collection and analysis and prognosis of fruitful areas of additional research, especially those which appear to have been opened by industrial applications.
Abstract: An accelerating increase in linear programming applications to industrial problems has made it virtually impossible to keep abreast of them, not only because of their number and diversity but also because of the conditions under which many are carried out. Industrial and governmental secrecy is often present. Other conditions also bar access to ascertainment and assessment of the pattern of applications. Lack of a tradition for publication is one. Failure to ascertain the general significance of particular findings is another, as is discouragement arising from the fact that similar applications have previously been published by others. Immediate remedies are not available for these difficulties. Presumably conventions such as this will help, over a period of time, by encouraging informal contacts between interested persons.
A talk on “industrial applications of linear programming” must be altered to suit these circumstances. In place of a survey or evaluation of industrial studies, two broad issues which are relevant to all such applications will be discussed. These are, 1 use of linear programming models as guides to data collection and 2 analysis and prognosis of fruitful areas of additional research, especially those which appear to have been opened by industrial applications.
1,763 citations
TL;DR: In this paper, the concept of proper efficiency was introduced to eliminate efficient points of a certain anomalous nature in the problem of vector maximization, which is related in spirit to the notion of "proper" efficiency introduced by Kuhn and Tucker in their celebrated paper of 1950.
1,272 citations