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Portfolio Optimization: A Comparative Study

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  • Jaydip Sen
  • Subhasis Dasgupta

Abstract

Portfolio optimization has been an area that has attracted considerable attention from the financial research community. Designing a profitable portfolio is a challenging task involving precise forecasting of future stock returns and risks. This chapter presents a comparative study of three portfolio design approaches, the mean-variance portfolio (MVP), hierarchical risk parity (HRP)-based portfolio, and autoencoder-based portfolio. These three approaches to portfolio design are applied to the historical prices of stocks chosen from ten thematic sectors listed on the National Stock Exchange (NSE) of India. The portfolios are designed using the stock price data from January 1, 2018, to December 31, 2021, and their performances are tested on the out-of-sample data from January 1, 2022, to December 31, 2022. Extensive results are analyzed on the performance of the portfolios. It is observed that the performance of the MVP portfolio is the best on the out-of-sample data for the risk-adjusted returns. However, the autoencoder portfolios outperformed their counterparts on annual returns.

Suggested Citation

  • Jaydip Sen & Subhasis Dasgupta, 2023. "Portfolio Optimization: A Comparative Study," Papers 2307.05048, arXiv.org.
    • Handle: RePEc:arx:papers:2307.05048
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    References listed on IDEAS

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    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, 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, 2021. "Accurate Stock Price Forecasting Using Robust and Optimized Deep Learning Models," Papers 2103.15096, arXiv.org.
    5. Charl Maree & Christian W. Omlin, 2022. "Balancing Profit, Risk, and Sustainability for Portfolio Management," Papers 2207.02134, arXiv.org.
    6. 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.
    7. Sidra Mehtab & Jaydip Sen, 2020. "Stock Price Prediction Using CNN and LSTM-Based Deep Learning Models," Papers 2010.13891, arXiv.org.
    8. 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.
    9. Jaydip Sen, 2022. "Designing Efficient Pair-Trading Strategies Using Cointegration for the Indian Stock Market," Papers 2211.07080, arXiv.org.
    10. Haoran Wang & Shi Yu, 2021. "Robo-Advising: Enhancing Investment with Inverse Optimization and Deep Reinforcement Learning," Papers 2105.09264, 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 & Abhishek Dutta, 2022. "Design and Analysis of Optimized Portfolios for Selected Sectors of the Indian Stock Market," Papers 2210.03943, 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. Audrino, Francesco & Sigrist, Fabio & Ballinari, Daniele, 2020. "The impact of sentiment and attention measures on stock market volatility," International Journal of Forecasting, Elsevier, vol. 36(2), pages 334-357.
    15. 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.
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    Cited by:

    1. Kamila Zaman & Alberto Marchisio & Muhammad Kashif & Muhammad Shafique, 2024. "PO-QA: A Framework for Portfolio Optimization using Quantum Algorithms," Papers 2407.19857, arXiv.org.

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