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Design and Analysis of Optimized Portfolios for Selected Sectors of the Indian Stock Market

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  • Jaydip Sen
  • Abhishek Dutta

Abstract

Portfolio optimization is a challenging problem that has attracted considerable attention and effort from researchers. The optimization of stock portfolios is a particularly hard problem since the stock prices are volatile and estimation of their future volatilities and values, in most cases, is very difficult, if not impossible. This work uses three ratios, the Sharpe ratio, the Sortino ratio, and the Calmar ratio, for designing the mean-variance optimized portfolios for six important sectors listed in the National Stock Exchange (NSE) of India. Three portfolios are designed for each sector maximizing the ratios based on the historical prices of the ten most important stocks of each sector from Jan 1, 2017, to Dec 31, 2020. The evaluation of the portfolios is done based on their cumulative returns over the test period from Jan 1, 2021, to Dec 31, 2021. The ratio that yields the maximum cumulative returns for both the training and the test periods for the majority of the sectors is identified. The sectors that exhibit the maximum cumulative returns for the same ratio are also identified. The results provide useful insights for investors in the stock market in making their investment decisions based on the current return and risks associated with the six sectors and their stocks.

Suggested Citation

  • Jaydip Sen & Abhishek Dutta, 2022. "Design and Analysis of Optimized Portfolios for Selected Sectors of the Indian Stock Market," Papers 2210.03943, arXiv.org.
    • Handle: RePEc:arx:papers:2210.03943
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    File URL: http://arxiv.org/pdf/2210.03943
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    References listed on IDEAS

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    1. Ananda Chatterjee & Hrisav Bhowmick & Jaydip Sen, 2021. "Stock Price Prediction Using Time Series, Econometric, Machine Learning, and Deep Learning Models," Papers 2111.01137, arXiv.org.
    2. 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.
    3. Sidra Mehtab & Jaydip Sen, 2020. "Stock Price Prediction Using CNN and LSTM-Based Deep Learning Models," Papers 2010.13891, arXiv.org.
    4. Marco Corazza & Giacomo di Tollo & Giovanni Fasano & Raffaele Pesenti, 2021. "A novel hybrid PSO-based metaheuristic for costly portfolio selection problems," Annals of Operations Research, Springer, vol. 304(1), pages 109-137, September.
    5. Sidra Mehtab & Jaydip Sen, 2019. "A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processing," Papers 1912.07700, arXiv.org.
    6. 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.
    7. Jaydip Sen & Sidra Mehtab & Abhishek Dutta, 2021. "Volatility Modeling of Stocks from Selected Sectors of the Indian Economy Using GARCH," Papers 2105.13898, arXiv.org.
    8. Massimiliano Kaucic & Mojtaba Moradi & Mohmmad Mirzazadeh, 2019. "Portfolio optimization by improved NSGA-II and SPEA 2 based on different risk measures," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-28, December.
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    Cited by:

    1. Jaydip Sen & Subhasis Dasgupta, 2023. "Portfolio Optimization: A Comparative Study," Papers 2307.05048, arXiv.org.
    2. Janki Mistry & Ritesh Ashok Khatwani, 2023. "Examining the superiority of the Sharpe single-index model of portfolio selection: A study of the Indian mid-cap sector," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-9, December.

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