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Risk-Based Portfolio Optimization on Some Selected Sectors of the Indian Stock Market

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Big Data, Machine Learning, and Applications (BigDML 2021)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1053))

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Abstract

Designing portfolios with optimum future return and risk have always proved to be a very difficult research problem since the precise estimation of the future returns and volatilities of stocks poses a great challenge. This paper presents an approach to portfolio design using two risk-based methods, the hierarchical risk parity (HRP) and the hierarchical equal risk contribution (HERC). These two methods are applied to five important sectors of the National Stock Exchange (NSE) of India. The portfolios are built on the stock prices for the period January 1, 2016–December 31, 2020, and their performances are evaluated for the period January 1, 2021–November 1, 2021. The results show that the HRP portfolio's performance in the five sectors is superior to its HERC counterpart in all the five sectors.

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Authors and Affiliations

  1. Praxis Business School, Kolkata, 700104, India

    Jaydip Sen & Abhishek Dutta

Authors
  1. Jaydip Sen
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  2. Abhishek Dutta
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Corresponding author

Correspondence to Jaydip Sen .

Editor information

Editors and Affiliations

  1. Computer Science & Engineering, National Institute of Technology Silchar, Silchar, Assam, India

    Malaya Dutta Borah

  2. Computer Science & Engineering, National Institute of Technology Silchar, Silchar, Assam, India

    Dolendro Singh Laiphrakpam

  3. Computer Science & Engineering, Indian Institute of Technology Ropar, Rupnagar, Punjab, India

    Nitin Auluck

  4. Department of Automation and Applied Informatics, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Sen, J., Dutta, A. (2024). Risk-Based Portfolio Optimization on Some Selected Sectors of the Indian Stock Market. In: Borah, M.D., Laiphrakpam, D.S., Auluck, N., Balas, V.E. (eds) Big Data, Machine Learning, and Applications. BigDML 2021. Lecture Notes in Electrical Engineering, vol 1053. Springer, Singapore. https://doi.org/10.1007/978-981-99-3481-2_58

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  • DOI: https://doi.org/10.1007/978-981-99-3481-2_58

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-3480-5

  • Online ISBN: 978-981-99-3481-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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