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

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Advances in Intelligent Computing

Part of the book series: Studies in Computational Intelligence ((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.

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Author information

Authors and Affiliations

  1. Department of Computer Science & Engineering, Assam University, Silchar, India

    Somnath Mukhopadhyay

  2. Department of Economics and Finance, Calcutta Business School, Kolkata, India

    Tamal Datta Chaudhuri

Authors
  1. Somnath Mukhopadhyay
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  2. Tamal Datta Chaudhuri
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Corresponding author

Correspondence to Somnath Mukhopadhyay .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India

    J. K. Mandal

  2. Department of Computer and System Sciences, Visva-Bharati University, Santiniketan, Bolpur, West Bengal, India

    Paramartha Dutta

  3. Department of Computer Science and Engineering, Assam University, Silchar, Assam, India

    Somnath Mukhopadhyay

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Mukhopadhyay, S., Chaudhuri, T.D. (2019). Different Length Genetic Algorithm-Based Clustering of Indian Stocks for Portfolio Optimization. In: Mandal, J., Dutta, P., Mukhopadhyay, S. (eds) Advances in Intelligent Computing . Studies in Computational Intelligence, vol 687. Springer, Singapore. https://doi.org/10.1007/978-981-10-8974-9_3

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