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Different Length Genetic Algorithm-Based Clustering of Indian Stocks for Portfolio Optimization

  • Somnath Mukhopadhyay
  • Tamal Datta Chaudhuri
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 687)

Abstract

In this chapter, we propose a model for portfolio construction using different length genetic algorithm (GA)-based clustering of Indian stocks. First, stocks of different companies, chosen from different industries, are classified based on their returns per unit of risk using an unsupervised method of different length genetic algorithm. Then, the centroids of the algorithm are again classified by the same algorithm. So vertical clustering (clustering of stocks by returns per unit of risk for each day) followed by horizontal clustering (clustering of the centroids over time) eventually produces a limited number of stocks. The Markowitz model is applied to determine the weights of the stocks in the portfolio. The results are also compared with some well-known existing algorithms. They indicate that the proposed GA-based clustering algorithm outperforms all the other algorithms.

Keywords

Different length genetic algorithm Horizontal clustering Markowitz model Portfolio optimization Return Risk Vertical clustering 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer Science & EngineeringAssam UniversitySilcharIndia
  2. 2.Department of Economics and FinanceCalcutta Business SchoolKolkataIndia

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