Accession Number:

ADA576359

Title:

Portfolio Optimization by Means of Multiple Tandem Certainty-Uncertainty Searches: A Technical Description

Descriptive Note:

Technical rept.

Corporate Author:

RAND ARROYO CENTER SANTA MONICA CA

Personal Author(s):

Report Date:

2013-03-15

Pagination or Media Count:

73.0

Abstract:

Conducting optimization under conditions of uncertainty has long been a very difficult problem. Thus, when analysts have done optimization under uncertainty, they have introduced severe limitations to restrict how uncertainties can be factored in. This paper describes a new approach to optimization under uncertainty that is aimed at finding the optimal solution to a problem by designing a number of search algorithms or schemes in a way that reduces the dimensionality constraints that analysts have had to contend with until now. The specific purpose of this paper is to convert a provisional patent application entitled Portfolio Optimization by Means of a Ranking and Competing Search by the author into a published volume available for public use. The provisional patent application was filed with the United States Patent and Trademark Office on July 30, 2012. Given the goal of making this a publication for public use, this paper has been structured differently -- more in accord with a scientific paper than a patent application. Also, some background materials and examples from the authors past studies have been added to illustrate the new approach and contrast it and its associated algorithms with those of existing approaches, and some editorial changes have been made to make it easier for general audiences to comprehend. The ideas and techniques presented in this paper may be used by anyone for any purpose with citation. This paper may be of interest to designers of optimization search algorithms. Businesses in both the public and private sectors may also find this paper of use, because they can incorporate the new approach and the developed algorithms into their optimization models to better deal with the future, which is rife with uncertainty.

Subject Categories:

  • Statistics and Probability
  • Operations Research
  • Computer Programming and Software

Distribution Statement:

APPROVED FOR PUBLIC RELEASE