South Asian Journal of Management

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An Investigation of Portfolio Optimization using Modified NSGA-II Algorithm


Affiliations
1 Xavier Institute of Management, Xavier Square, Jayadev Vihar, Bhubaneswar 751013, Odisha, India
2 Information Systems, Xavier Institute of Management, Xavier Square, Jayadev Vihar, Bhubaneswar, 751013, Odisha, India
3 Department of Mining Engineering, Indian Institute of Technology, Kharagpur 721302, West Bengal, India
4 Indian Institute of Management Sambalpur, Jyoti Vihar, Burla, Sambalpur 768019, Odisha, India
     

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This study aims to utilize the optimization framework of Markowitz and Random Immigration Non-dominated Sorting Genetic Algorithm-II (RINSGA-II) to trace portfolio with extreme values, which are Pareto optimal. Using dataset from OR-library, this study tested the efficacy of the modified algorithm against NSGA-II. The results indicate that random immigration NSGA-II is efficient to trace the extreme values. The suggested optimization algorithm of random immigration NSGA-II replicates the efficient frontier of OR-library and gives better spread compared to NSGA-II. Finally, to our best of knowledge this is the first study to adopt random immigration NSGA-II to construct an optimized portfolio with additional constraints.

Keywords

Genetic Algorithm, Heuristics, NSGA-II, Portfolio Optimization, Random Immigration.
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  • An Investigation of Portfolio Optimization using Modified NSGA-II Algorithm

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Authors

Biplab Mahapatra
Xavier Institute of Management, Xavier Square, Jayadev Vihar, Bhubaneswar 751013, Odisha, India
Sanjay Mohapatra
Information Systems, Xavier Institute of Management, Xavier Square, Jayadev Vihar, Bhubaneswar, 751013, Odisha, India
Biswajit Samanta
Department of Mining Engineering, Indian Institute of Technology, Kharagpur 721302, West Bengal, India
Soumya Guha Deb
Indian Institute of Management Sambalpur, Jyoti Vihar, Burla, Sambalpur 768019, Odisha, India

Abstract


This study aims to utilize the optimization framework of Markowitz and Random Immigration Non-dominated Sorting Genetic Algorithm-II (RINSGA-II) to trace portfolio with extreme values, which are Pareto optimal. Using dataset from OR-library, this study tested the efficacy of the modified algorithm against NSGA-II. The results indicate that random immigration NSGA-II is efficient to trace the extreme values. The suggested optimization algorithm of random immigration NSGA-II replicates the efficient frontier of OR-library and gives better spread compared to NSGA-II. Finally, to our best of knowledge this is the first study to adopt random immigration NSGA-II to construct an optimized portfolio with additional constraints.

Keywords


Genetic Algorithm, Heuristics, NSGA-II, Portfolio Optimization, Random Immigration.

References