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A Time Series Analysis-Based Stock Price Prediction Using Machine Learning and Deep Learning Models

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  • Sidra Mehtab
  • Jaydip Sen

Abstract

Prediction of future movement of stock prices has always been a challenging task for the researchers. While the advocates of the efficient market hypothesis (EMH) believe that it is impossible to design any predictive framework that can accurately predict the movement of stock prices, there are seminal work in the literature that have clearly demonstrated that the seemingly random movement patterns in the time series of a stock price can be predicted with a high level of accuracy. Design of such predictive models requires choice of appropriate variables, right transformation methods of the variables, and tuning of the parameters of the models. In this work, we present a very robust and accurate framework of stock price prediction that consists of an agglomeration of statistical, machine learning and deep learning models. We use the daily stock price data, collected at five minutes interval of time, of a very well known company that is listed in the National Stock Exchange (NSE) of India. The granular data is aggregated into three slots in a day, and the aggregated data is used for building and training the forecasting models. We contend that the agglomerative approach of model building that uses a combination of statistical, machine learning, and deep learning approaches, can very effectively learn from the volatile and random movement patterns in a stock price data. We build eight classification and eight regression models based on statistical and machine learning approaches. In addition to these models, a deep learning regression model using a long-and-short-term memory (LSTM) network is also built. Extensive results have been presented on the performance of these models, and the results are critically analyzed.

Suggested Citation

  • 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.
  • Handle: RePEc:arx:papers:2004.11697
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    References listed on IDEAS

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    1. Basalto, N. & Bellotti, R. & De Carlo, F. & Facchi, P. & Pascazio, S., 2005. "Clustering stock market companies via chaotic map synchronization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 345(1), pages 196-206.
    2. 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.
    3. 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.
    4. Hutchinson, James M & Lo, Andrew W & Poggio, Tomaso, 1994. "A Nonparametric Approach to Pricing and Hedging Derivative Securities via Learning Networks," Journal of Finance, American Finance Association, vol. 49(3), pages 851-889, July.
    5. Basu, Sanjoy, 1983. "The relationship between earnings' yield, market value and return for NYSE common stocks : Further evidence," Journal of Financial Economics, Elsevier, vol. 12(1), pages 129-156, June.
    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. Bentes, Sónia R. & Menezes, Rui & Mendes, Diana A., 2008. "Long memory and volatility clustering: Is the empirical evidence consistent across stock markets?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(15), pages 3826-3830.
    8. 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.
    9. M. Hanias & P. Curtis & E. Thalassinos, 2012. "Time Series Prediction with Neural Networks for the Athens Stock Exchange Indicator," European Research Studies Journal, European Research Studies Journal, vol. 0(2), pages 23-32.
    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. Jaffe, Jeffrey & Keim, Donald B & Westerfield, Randolph, 1989. " Earnings Yields, Market Values, and Stock Returns," Journal of Finance, American Finance Association, vol. 44(1), pages 135-148, March.
    12. Sidra Mehtab & Jaydip Sen, 2020. "Stock Price Prediction Using Convolutional Neural Networks on a Multivariate Timeseries," Papers 2001.09769, arXiv.org.
    13. Fama, Eugene F & French, Kenneth R, 1995. "Size and Book-to-Market Factors in Earnings and Returns," Journal of Finance, American Finance Association, vol. 50(1), pages 131-155, March.
    14. Chui, Andy C. W. & Wei, K. C. John, 1998. "Book-to-market, firm size, and the turn-of-the-year effect: Evidence from Pacific-Basin emerging markets," Pacific-Basin Finance Journal, Elsevier, vol. 6(3-4), pages 275-293, August.
    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.
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    Citations

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    Cited by:

    1. 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.
    2. Jaydip Sen, 2022. "Designing Efficient Pair-Trading Strategies Using Cointegration for the Indian Stock Market," Papers 2211.07080, arXiv.org.
    3. Jaydip Sen & Saikat Mondal & Gourab Nath, 2022. "Robust Portfolio Design and Stock Price Prediction Using an Optimized LSTM Model," Papers 2204.01850, arXiv.org.
    4. 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.
    5. Jaydip Sen & Abhishek Dutta, 2022. "Design and Analysis of Optimized Portfolios for Selected Sectors of the Indian Stock Market," Papers 2210.03943, 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 & 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.
    8. Abhiraj Sen & Jaydip Sen, 2023. "Performance Evaluation of Equal-Weight Portfolio and Optimum Risk Portfolio on Indian Stocks," Papers 2309.13696, arXiv.org.
    9. Zheng Fang & Jianying Xie & Ruiming Peng & Sheng Wang, 2021. "Climate Finance: Mapping Air Pollution and Finance Market in Time Series," Econometrics, MDPI, vol. 9(4), pages 1-15, December.
    10. Jaydip Sen & Arup Dasgupta & Subhasis Dasgupta & Sayantani Roychoudhury, 2023. "A Portfolio Rebalancing Approach for the Indian Stock Market," Papers 2310.09770, arXiv.org.
    11. 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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