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Analysis and prediction of Indian stock market: a machine-learning approach

Author

Listed:
  • Shilpa Srivastava

    (Christ (Deemed to be University), Delhi NCR)

  • Millie Pant

    (IIT Roorkee)

  • Varuna Gupta

    (Christ (Deemed to be University), Delhi NCR)

Abstract

Prediction of financial stock market is a challenging task because of its volatile and non- linear nature. The presence of different factors like psychological, sentimental state, rational or irrational behaviour of investors make the stock market more dynamic. With the inculcation of algorithms based on artificial intelligence, deep learning algorithms, the prediction of movement of financial stock market is revolutionized in the recent years. The purpose of using these algorithms is to help the investors for taking decisions related to the Stock Pricing. A model has been proposed to predict the direction of movement of Indian stock market in the near future. This model makes use of historical Indian stock data of companies in nifty 50 since they came existence along with some financial and social indicators like financial news and tweets related to stocks. After pre-processing and normalization various machine learning algorithms like LSTM, support vector machines, KNearest neighbour, random forest, gradient boosting regressor are applied on this time series data to produce better accuracy and to minimize the RMSE error. This model has the ability to reduce major losses to the investors who invest in stock market. The social indicators will give an insight for predicting the direction of stock market. The LSTM network will make use of historical closing prices, tweets and trading volume.

Suggested Citation

  • Shilpa Srivastava & Millie Pant & Varuna Gupta, 2023. "Analysis and prediction of Indian stock market: a machine-learning approach," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(4), pages 1567-1585, August.
  • Handle: RePEc:spr:ijsaem:v:14:y:2023:i:4:d:10.1007_s13198-023-01934-z
    DOI: 10.1007/s13198-023-01934-z
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    References listed on IDEAS

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    1. Sharma, Chandradew & Banerjee, Kinjal, 2015. "A study of correlations in the stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 432(C), pages 321-330.
    2. Renault, Thomas, 2017. "Intraday online investor sentiment and return patterns in the U.S. stock market," Journal of Banking & Finance, Elsevier, vol. 84(C), pages 25-40.
    3. Thomas Renault, 2017. "Intraday online investor sentiment and return patterns in the U.S. stock market," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-03205113, HAL.
    4. Liu, Qingbai & Wang, Chuanjie & Zhang, Ping & Zheng, Kaixin, 2021. "Detecting stock market manipulation via machine learning: Evidence from China Securities Regulatory Commission punishment cases," International Review of Financial Analysis, Elsevier, vol. 78(C).
    5. Chandradew Sharma & Kinjal Banerjee, 2015. "A Study of Correlations in the Stock Market," Papers 1504.05844, arXiv.org.
    6. Sun, Andrew & Lachanski, Michael & Fabozzi, Frank J., 2016. "Trade the tweet: Social media text mining and sparse matrix factorization for stock market prediction," International Review of Financial Analysis, Elsevier, vol. 48(C), pages 272-281.
    7. Lee, Hsiu-Chuan & Lee, Yun-Huan & Lu, Yang-Cheng & Wang, Yu-Chun, 2020. "States of psychological anchors and price behavior of Japanese yen futures," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    Full references (including those not matched with items on IDEAS)

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