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Predicting Indian Stock Market Using the Psycho-Linguistic Features of Financial News

Author

Listed:
  • B. Shravan Kumar

    (Institute for Development and Research in Banking Technology (IDRBT)
    University of Hyderabad)

  • Vadlamani Ravi

    (Institute for Development and Research in Banking Technology (IDRBT))

  • Rishabh Miglani

    (Indian Institute of Technology Kharagpur)

Abstract

Financial forecasting using news articles is an emerging field. In this paper, we proposed hybrid intelligent models for stock market prediction using the psycholinguistic variables (LIWC and TAALES) extracted from news articles as predictor variables. For prediction purpose, we employed various intelligent techniques such as Multilayer Perceptron, Group Method of Data Handling (GMDH), General Regression Neural Network (GRNN), Random Forest, Quantile Regression Random Forest, Classification and regression tree and Support Vector Regression. We experimented on the data of 12 companies’ stocks, which are listed in Bombay Stock Exchange. We employed Chi squared and maximum relevance and minimum redundancy feature selection techniques on the psycho-linguistic features obtained from the news articles etc. After extensive experimentation, using Diebold-Mariano test, we conclude that GMDH and GRNN are statistically the best techniques in that order with respect to the MAPE and NRMSE values.

Suggested Citation

  • B. Shravan Kumar & Vadlamani Ravi & Rishabh Miglani, 2021. "Predicting Indian Stock Market Using the Psycho-Linguistic Features of Financial News," Annals of Data Science, Springer, vol. 8(3), pages 517-558, September.
    • Handle: RePEc:spr:aodasc:v:8:y:2021:i:3:d:10.1007_s40745-020-00272-2
    DOI: 10.1007/s40745-020-00272-2
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    References listed on IDEAS

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    1. Hao Chen & Qiulan Wan & Yurong Wang, 2014. "Refined Diebold-Mariano Test Methods for the Evaluation of Wind Power Forecasting Models," Energies, MDPI, vol. 7(7), pages 1-14, July.
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    Cited by:

    1. Yuancheng Si & Saralees Nadarajah, 2023. "A Statistical Analysis of Chinese Stock Indices Returns From Approach of Parametric Distributions Fitting," Annals of Data Science, Springer, vol. 10(1), pages 73-88, February.
    2. Deeksha Chandola & Akshit Mehta & Shikha Singh & Vinay Anand Tikkiwal & Himanshu Agrawal, 2023. "Forecasting Directional Movement of Stock Prices using Deep Learning," Annals of Data Science, Springer, vol. 10(5), pages 1361-1378, October.

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