IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2305.17523.html
   My bibliography  Save this paper

A Comparative Analysis of Portfolio Optimization Using Mean-Variance, Hierarchical Risk Parity, and Reinforcement Learning Approaches on the Indian Stock Market

Author

Listed:
  • Jaydip Sen
  • Aditya Jaiswal
  • Anshuman Pathak
  • Atish Kumar Majee
  • Kushagra Kumar
  • Manas Kumar Sarkar
  • Soubhik Maji

Abstract

This paper presents a comparative analysis of the performances of three portfolio optimization approaches. Three approaches of portfolio optimization that are considered in this work are the mean-variance portfolio (MVP), hierarchical risk parity (HRP) portfolio, and reinforcement learning-based portfolio. The portfolios are trained and tested over several stock data and their performances are compared on their annual returns, annual risks, and Sharpe ratios. In the reinforcement learning-based portfolio design approach, the deep Q learning technique has been utilized. Due to the large number of possible states, the construction of the Q-table is done using a deep neural network. The historical prices of the 50 premier stocks from the Indian stock market, known as the NIFTY50 stocks, and several stocks from 10 important sectors of the Indian stock market are used to create the environment for training the agent.

Suggested Citation

  • Jaydip Sen & Aditya Jaiswal & Anshuman Pathak & Atish Kumar Majee & Kushagra Kumar & Manas Kumar Sarkar & Soubhik Maji, 2023. "A Comparative Analysis of Portfolio Optimization Using Mean-Variance, Hierarchical Risk Parity, and Reinforcement Learning Approaches on the Indian Stock Market," Papers 2305.17523, arXiv.org.
    • Handle: RePEc:arx:papers:2305.17523
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2305.17523
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jaydip Sen & Abhishek Dutta & Sidra Mehtab, 2021. "Stock Portfolio Optimization Using a Deep Learning LSTM Model," Papers 2111.04709, arXiv.org.
    2. Ananda Chatterjee & Hrisav Bhowmick & Jaydip Sen, 2022. "Stock Volatility Prediction using Time Series and Deep Learning Approach," Papers 2210.02126, arXiv.org.
    3. Sidra Mehtab & Jaydip Sen & Abhishek Dutta, 2020. "Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models," Papers 2009.10819, arXiv.org.
    4. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    5. 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.
    6. Sidra Mehtab & Jaydip Sen, 2020. "Stock Price Prediction Using CNN and LSTM-Based Deep Learning Models," Papers 2010.13891, arXiv.org.
    7. Merton, Robert C., 1976. "Option pricing when underlying stock returns are discontinuous," Journal of Financial Economics, Elsevier, vol. 3(1-2), pages 125-144.
    8. Jaydip Sen, 2022. "Designing Efficient Pair-Trading Strategies Using Cointegration for the Indian Stock Market," Papers 2211.07080, arXiv.org.
    9. Ledoit, Olivier & Wolf, Michael, 2003. "Improved estimation of the covariance matrix of stock returns with an application to portfolio selection," Journal of Empirical Finance, Elsevier, vol. 10(5), pages 603-621, December.
    10. Jaydip Sen & Tamal Datta Chaudhuri, 2017. "A Time Series Analysis-Based Forecasting Framework for the Indian Healthcare Sector," Papers 1705.01144, arXiv.org.
    11. 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.
    12. Jaydip Sen & Saikat Mondal & Sidra Mehtab, 2021. "Analysis of Sectoral Profitability of the Indian Stock Market Using an LSTM Regression Model," Papers 2111.04976, arXiv.org.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tian Tian & Ricky Cooper & Jiahao Deng & Qingquan Zhang, 2024. "Transforming Investment Strategies and Strategic Decision-Making: Unveiling a Novel Methodology for Enhanced Performance and Risk Management in Financial Markets," Papers 2405.01892, arXiv.org.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jaydip Sen & Arpit Awad & Aaditya Raj & Gourav Ray & Pusparna Chakraborty & Sanket Das & Subhasmita Mishra, 2022. "Stock Performance Evaluation for Portfolio Design from Different Sectors of the Indian Stock Market," Papers 2208.07166, arXiv.org.
    2. Jaydip Sen & Ashwin Kumar R S & Geetha Joseph & Kaushik Muthukrishnan & Koushik Tulasi & Praveen Varukolu, 2022. "Precise Stock Price Prediction for Robust Portfolio Design from Selected Sectors of the Indian Stock Market," Papers 2201.05570, arXiv.org.
    3. Jaydip Sen & Saikat Mondal & Sidra Mehtab, 2021. "Analysis of Sectoral Profitability of the Indian Stock Market Using an LSTM Regression Model," Papers 2111.04976, arXiv.org.
    4. Jaydip Sen & Sidra Mehtab, 2021. "Design and Analysis of Robust Deep Learning Models for Stock Price Prediction," Papers 2106.09664, arXiv.org.
    5. 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.
    6. Jaydip Sen & Subhasis Dasgupta, 2023. "Portfolio Optimization: A Comparative Study," Papers 2307.05048, arXiv.org.
    7. Abhiraj Sen & Jaydip Sen, 2023. "Performance Evaluation of Equal-Weight Portfolio and Optimum Risk Portfolio on Indian Stocks," Papers 2309.13696, arXiv.org.
    8. Jaydip Sen & Hetvi Waghela & Sneha Rakshit, 2024. "Exploring Sectoral Profitability in the Indian Stock Market Using Deep Learning," Papers 2407.01572, arXiv.org.
    9. Jaydip Sen & Arup Dasgupta & Subhasis Dasgupta & Sayantani Roychoudhury, 2023. "A Portfolio Rebalancing Approach for the Indian Stock Market," Papers 2310.09770, arXiv.org.
    10. 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.
    11. 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.
    12. 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.
    13. Sidra Mehtab & Jaydip Sen, 2020. "Stock Price Prediction Using Convolutional Neural Networks on a Multivariate Timeseries," Papers 2001.09769, arXiv.org.
    14. Jaydip Sen & Abhishek Dutta, 2022. "Design and Analysis of Optimized Portfolios for Selected Sectors of the Indian Stock Market," Papers 2210.03943, arXiv.org.
    15. Sidra Mehtab & Jaydip Sen & Abhishek Dutta, 2020. "Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models," Papers 2009.10819, arXiv.org.
    16. Jaydip Sen & Abhishek Dutta, 2022. "A Comparative Study of Hierarchical Risk Parity Portfolio and Eigen Portfolio on the NIFTY 50 Stocks," Papers 2210.00984, arXiv.org.
    17. Tasnim Uddin Chowdhury & Md. Shahidul Islam, 2021. "ARIMA Time Series Analysis in Forecasting Daily Stock Price of Chittagong Stock Exchange (CSE)," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 5(6), pages 214-233, June.
    18. Jaydip Sen, 2022. "Designing Efficient Pair-Trading Strategies Using Cointegration for the Indian Stock Market," Papers 2211.07080, arXiv.org.
    19. Lian, Yu-Min & Chen, Jun-Home, 2019. "Portfolio selection in a multi-asset, incomplete-market economy," The Quarterly Review of Economics and Finance, Elsevier, vol. 71(C), pages 228-238.
    20. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2005. "Volatility Forecasting," PIER Working Paper Archive 05-011, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2305.17523. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.