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Analysis of Sectoral Profitability of the Indian Stock Market Using an LSTM Regression Model

Author

Listed:
  • Jaydip Sen
  • Saikat Mondal
  • Sidra Mehtab

Abstract

Predictive model design for accurately predicting future stock prices has always been considered an interesting and challenging research problem. The task becomes complex due to the volatile and stochastic nature of the stock prices in the real world which is affected by numerous controllable and uncontrollable variables. This paper presents an optimized predictive model built on long-and-short-term memory (LSTM) architecture for automatically extracting past stock prices from the web over a specified time interval and predicting their future prices for a specified forecast horizon, and forecasts the future stock prices. The model is deployed for making buy and sell transactions based on its predicted results for 70 important stocks from seven different sectors listed in the National Stock Exchange (NSE) of India. The profitability of each sector is derived based on the total profit yielded by the stocks in that sector over a period from Jan 1, 2010 to Aug 26, 2021. The sectors are compared based on their profitability values. The prediction accuracy of the model is also evaluated for each sector. The results indicate that the model is highly accurate in predicting future stock prices.

Suggested Citation

  • 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.
    • Handle: RePEc:arx:papers:2111.04976
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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, 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.
    3. 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.
    4. Jaydip Sen & Abhishek Dutta & Sidra Mehtab, 2021. "Stock Portfolio Optimization Using a Deep Learning LSTM Model," Papers 2111.04709, arXiv.org.
    5. 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.
    6. Jaydip Sen & Tamal Datta Chaudhuri, 2016. "Decomposition of Time Series Data of Stock Markets and its Implications for Prediction: An Application for the Indian Auto Sector," Papers 1601.02407, arXiv.org.
    7. 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.
    8. Sidra Mehtab & Jaydip Sen, 2020. "Stock Price Prediction Using CNN and LSTM-Based Deep Learning Models," Papers 2010.13891, arXiv.org.
    9. B. Y. Qu & Q. Zhou & J. M. Xiao & J. J. Liang & P. N. Suganthan, 2017. "Large-Scale Portfolio Optimization Using Multiobjective Evolutionary Algorithms and Preselection Methods," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-14, February.
    10. Alejandro Reveiz‐Herault, 2016. "An Active Asset Management Investment Process for Drawdown‐Averse Investors," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 23(1-2), pages 85-96, January.
    11. 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.
    12. Wei Bao & Jun Yue & Yulei Rao, 2017. "A deep learning framework for financial time series using stacked autoencoders and long-short term memory," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-24, July.
    13. 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.
    14. Jaydip Sen & Sidra Mehtab, 2021. "Design and Analysis of Robust Deep Learning Models for Stock Price Prediction," Papers 2106.09664, arXiv.org.
    15. Wang, Lu & Ma, Feng & Liu, Jing & Yang, Lin, 2020. "Forecasting stock price volatility: New evidence from the GARCH-MIDAS model," International Journal of Forecasting, Elsevier, vol. 36(2), pages 684-694.
    16. Jaydip Sen & Tamal Datta Chaudhuri, 2017. "A Time Series Analysis-Based Forecasting Framework for the Indian Healthcare Sector," Papers 1705.01144, arXiv.org.
    17. 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.
    18. 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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    Cited by:

    1. 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.
    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 & 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.

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