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Stock Portfolio Optimization Using a Deep Learning LSTM Model

Author

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  • Jaydip Sen
  • Abhishek Dutta
  • Sidra Mehtab

Abstract

Predicting future stock prices and their movement patterns is a complex problem. Hence, building a portfolio of capital assets using the predicted prices to achieve the optimization between its return and risk is an even more difficult task. This work has carried out an analysis of the time series of the historical prices of the top five stocks from the nine different sectors of the Indian stock market from January 1, 2016, to December 31, 2020. Optimum portfolios are built for each of these sectors. For predicting future stock prices, a long-and-short-term memory (LSTM) model is also designed and fine-tuned. After five months of the portfolio construction, the actual and the predicted returns and risks of each portfolio are computed. The predicted and the actual returns of each portfolio are found to be high, indicating the high precision of the LSTM model.

Suggested Citation

  • Jaydip Sen & Abhishek Dutta & Sidra Mehtab, 2021. "Stock Portfolio Optimization Using a Deep Learning LSTM Model," Papers 2111.04709, arXiv.org.
    • Handle: RePEc:arx:papers:2111.04709
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    References listed on IDEAS

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    1. 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.
    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 & Abhishek Dutta & Sidra Mehtab, 2021. "Profitability Analysis in Stock Investment Using an LSTM-Based Deep Learning Model," Papers 2104.06259, arXiv.org.
    5. 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.
    6. Jaydip Sen & Sidra Mehtab, 2021. "Accurate Stock Price Forecasting Using Robust and Optimized Deep Learning Models," Papers 2103.15096, arXiv.org.
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    Citations

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    Cited by:

    1. Jaydip Sen, 2022. "Designing Efficient Pair-Trading Strategies Using Cointegration for the Indian Stock Market," Papers 2211.07080, arXiv.org.
    2. 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.
    3. Jaydip Sen & Subhasis Dasgupta, 2023. "Portfolio Optimization: A Comparative Study," Papers 2307.05048, arXiv.org.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. Kiran Bisht & Arun Kumar, 2022. "Stock Portfolio Selection Hybridizing Fuzzy Base-Criterion Method and Evidence Theory in Triangular Fuzzy Environment," SN Operations Research Forum, Springer, vol. 3(4), pages 1-32, December.
    9. Hyeonseok Moon & Taemin Lee & Jaehyung Seo & Chanjun Park & Sugyeong Eo & Imatitikua D. Aiyanyo & Jeongbae Park & Aram So & Kyoungwha Ok & Kinam Park, 2022. "Return on Advertising Spend Prediction with Task Decomposition-Based LSTM Model," Mathematics, MDPI, vol. 10(10), pages 1-12, May.

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