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Hierarchical Risk Parity and Minimum Variance Portfolio Design on NIFTY 50 Stocks

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  • Jaydip Sen
  • Sidra Mehtab
  • Abhishek Dutta
  • Saikat Mondal

Abstract

Portfolio design and optimization have been always an area of research that has attracted a lot of attention from researchers from the finance domain. Designing an optimum portfolio is a complex task since it involves accurate forecasting of future stock returns and risks and making a suitable tradeoff between them. This paper proposes a systematic approach to designing portfolios using two algorithms, the critical line algorithm, and the hierarchical risk parity algorithm on eight sectors of the Indian stock market. While the portfolios are designed using the stock price data from Jan 1, 2016, to Dec 31, 2020, they are tested on the data from Jan 1, 2021, to Aug 26, 2021. The backtesting results of the portfolios indicate while the performance of the CLA algorithm is superior on the training data, the HRP algorithm has outperformed the CLA algorithm on the test data.

Suggested Citation

  • Jaydip Sen & Sidra Mehtab & Abhishek Dutta & Saikat Mondal, 2022. "Hierarchical Risk Parity and Minimum Variance Portfolio Design on NIFTY 50 Stocks," Papers 2202.02728, arXiv.org.
    • Handle: RePEc:arx:papers:2202.02728
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    References listed on IDEAS

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    1. Liu, Keyan & Zhou, Jianan & Dong, Dayong, 2021. "Improving stock price prediction using the long short-term memory model combined with online social networks," Journal of Behavioral and Experimental Finance, Elsevier, vol. 30(C).
    2. 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.
    3. 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.
    4. 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.
    5. Sidra Mehtab & Jaydip Sen & Abhishek Dutta, 2020. "Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models," Papers 2009.10819, arXiv.org.
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