NDLI: Bacterial Foraging Optimization Approach to Portfolio Optimization

Content Provider	Springer Nature Link
Author	Kao, Yucheng Cheng, Hsiu Tzu
Copyright Year	2013
Abstract	In this paper we propose a heuristic approach based on bacterial foraging optimization (BFO) in order to find the efficient frontier associated with the portfolio optimization (PO) problem. The PO model with cardinality and bounding constraints is a mixed quadratic and integer programming problem for which no exact algorithms can solve in an efficient way. Consequently, various heuristic algorithms, such as genetic algorithms and particle swarm optimization, have been proposed in the past. This paper aims to examine the potential of a BFO algorithm in solving the PO problem. BFO is a new swarm intelligence technique that has been successfully applied to several real world problems. Through three operations, chemotaxis, reproduction, and elimination-dispersal, the proposed BFO algorithm can effectively solve a PO problem. The performance of the proposed approach was evaluated in computational tests on five benchmark data sets, and the results were compared to those obtained from existing heuristic algorithms. The proposed BFO algorithm is found to be superior to previous heuristic algorithms in terms of solution quality and time.
Starting Page	453
Ending Page	470
Page Count	18
File Format	PDF
ISSN	09277099
Journal	Computational Economics
Volume Number	42
Issue Number	4
e-ISSN	15729974
Language	English
Publisher	Springer US
Publisher Date	2013-01-09
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Bacterial foraging optimization Swarm intelligence Portfolio optimization Efficient frontier Economic Theory
Content Type	Text
Resource Type	Article
Subject	Economics, Econometrics and Finance Computer Science Applications

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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