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Profitability Analysis in Stock Investment Using an LSTM-Based Deep Learning Model

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  • Jaydip Sen
  • Abhishek Dutta
  • Sidra Mehtab

Abstract

Designing robust systems for precise prediction of future prices of stocks has always been considered a very challenging research problem. Even more challenging is to build a system for constructing an optimum portfolio of stocks based on the forecasted future stock prices. We present a deep learning-based regression model built on a long-and-short-term memory network (LSTM) network that automatically scraps the web and extracts historical stock prices based on a stock's ticker name for a specified pair of start and end dates, and forecasts the future stock prices. We deploy the model on 75 significant stocks chosen from 15 critical sectors of the Indian stock market. For each of the stocks, the model is evaluated for its forecast accuracy. Moreover, the predicted values of the stock prices are used as the basis for investment decisions, and the returns on the investments are computed. Extensive results are presented on the performance of the model. The analysis of the results demonstrates the efficacy and effectiveness of the system and enables us to compare the profitability of the sectors from the point of view of the investors in the stock market.

Suggested Citation

  • Jaydip Sen & Abhishek Dutta & Sidra Mehtab, 2021. "Profitability Analysis in Stock Investment Using an LSTM-Based Deep Learning Model," Papers 2104.06259, arXiv.org.
    • Handle: RePEc:arx:papers:2104.06259
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    References listed on IDEAS

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    1. 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.
    2. Sidra Mehtab & Jaydip Sen, 2020. "Stock Price Prediction Using CNN and LSTM-Based Deep Learning Models," Papers 2010.13891, 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. 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.
    5. Chen, Wang & Ma, Feng & Wei, Yu & Liu, Jing, 2020. "Forecasting oil price volatility using high-frequency data: New evidence," International Review of Economics & Finance, Elsevier, vol. 66(C), pages 1-12.
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    Cited by:

    1. Jaydip Sen & Abhishek Dutta & Sidra Mehtab, 2021. "Stock Portfolio Optimization Using a Deep Learning LSTM Model," Papers 2111.04709, arXiv.org.
    2. 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.
    3. 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.
    4. Jaydip Sen & Rajdeep Sen & Abhishek Dutta, 2021. "Machine Learning in Finance-Emerging Trends and Challenges," Papers 2110.11999, arXiv.org.
    5. 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.
    6. Jaydip Sen & Sidra Mehtab, 2021. "Design and Analysis of Robust Deep Learning Models for Stock Price Prediction," Papers 2106.09664, arXiv.org.

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