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

B. Shravan Kumar (), Vadlamani Ravi () and Rishabh Miglani ()
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B. Shravan Kumar: Institute for Development and Research in Banking Technology (IDRBT)
Vadlamani Ravi: Institute for Development and Research in Banking Technology (IDRBT)
Rishabh Miglani: Indian Institute of Technology Kharagpur

Annals of Data Science, 2021, vol. 8, issue 3, No 5, 517-558

Abstract: 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.

Keywords: Text mining; Stock market prediction; News articles; Psycholinguistic features; MAPE; NRMSE (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s40745-020-00272-2

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