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A Comparative Study of Portfolio Optimization Methods for the Indian Stock Market

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Listed:
  • Jaydip Sen
  • Arup Dasgupta
  • Partha Pratim Sengupta
  • Sayantani Roy Choudhury

Abstract

This chapter presents a comparative study of the three portfolio optimization methods, MVP, HRP, and HERC, on the Indian stock market, particularly focusing on the stocks chosen from 15 sectors listed on the National Stock Exchange of India. The top stocks of each cluster are identified based on their free-float market capitalization from the report of the NSE published on July 1, 2022 (NSE Website). For each sector, three portfolios are designed on stock prices from July 1, 2019, to June 30, 2022, following three portfolio optimization approaches. The portfolios are tested over the period from July 1, 2022, to June 30, 2023. For the evaluation of the performances of the portfolios, three metrics are used. These three metrics are cumulative returns, annual volatilities, and Sharpe ratios. For each sector, the portfolios that yield the highest cumulative return, the lowest volatility, and the maximum Sharpe Ratio over the training and the test periods are identified.

Suggested Citation

  • Jaydip Sen & Arup Dasgupta & Partha Pratim Sengupta & Sayantani Roy Choudhury, 2023. "A Comparative Study of Portfolio Optimization Methods for the Indian Stock Market," Papers 2310.14748, arXiv.org.
    • Handle: RePEc:arx:papers:2310.14748
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    References listed on IDEAS

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    1. Sidra Mehtab & Jaydip Sen, 2020. "A Time Series Analysis-Based Stock Price Prediction Using Machine Learning and Deep Learning Models," Papers 2004.11697, arXiv.org, revised May 2021.
    2. Sidra Mehtab & Jaydip Sen & Abhishek Dutta, 2020. "Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models," Papers 2009.10819, arXiv.org.
    3. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    4. Jaydip Sen & Tamal Datta Chaudhuri, 2018. "Understanding the sectors of Indian economy for portfolio choice," International Journal of Business Forecasting and Marketing Intelligence, Inderscience Enterprises Ltd, vol. 4(2), pages 178-222.
    5. Sidra Mehtab & Jaydip Sen, 2020. "Stock Price Prediction Using CNN and LSTM-Based Deep Learning Models," Papers 2010.13891, arXiv.org.
    6. Flori, Andrea & Regoli, Daniele, 2021. "Revealing Pairs-trading opportunities with long short-term memory networks," European Journal of Operational Research, Elsevier, vol. 295(2), pages 772-791.
    7. Jaydip SEN & Tamal DATTA CHAUDHURI, 2016. "An Alternative Framework for Time Series Decomposition and Forecastingand its Relevance for Portfolio Choice – A Comparative Study of the Indian Consumer Durable and Small Cap Sectors," Journal of Economics Library, KSP Journals, vol. 3(2), pages 303-326, June.
    8. Jaydip Sen, 2018. "Stock composition of mutual funds and fund style: a time series decomposition approach towards testing for consistency," International Journal of Business Forecasting and Marketing Intelligence, Inderscience Enterprises Ltd, vol. 4(3), pages 235-292.
    9. Jaydip SEN & Tamal DATTA CHAUDHURI, 2017. "A Predictive Analysis of the Indian FMCG Sector using Time Series Decomposition - Based Approach," Journal of Economics Library, KSP Journals, vol. 4(2), pages 206-226, June.
    10. Jaydip Sen & Sidra Mehtab, 2021. "Design and Analysis of Robust Deep Learning Models for Stock Price Prediction," Papers 2106.09664, arXiv.org.
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