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Predicting Indian stock market using the psycho-linguistic features of financial news

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  • B. Shravan Kumar
  • Vadlamani Ravi
  • Rishabh Miglani

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 (MLP), Group Method of Data Handling (GMDH), General Regression Neural Network (GRNN), Random Forest (RF), Quantile Regression Random Forest (QRRF), Classification and regression tree (CART) and Support Vector Regression (SVR). We experimented on the data of 12 companies stocks, which are listed in the Bombay Stock Exchange (BSE). We employed chi-squared and maximum relevance and minimum redundancy (MRMR) feature selection techniques on the psycho-linguistic features obtained from the new articles etc. After extensive experimentation, using the 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, 2019. "Predicting Indian stock market using the psycho-linguistic features of financial news," Papers 1911.06193, arXiv.org.
  • Handle: RePEc:arx:papers:1911.06193
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    References listed on IDEAS

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    1. Engle, Robert F & Ng, Victor K, 1993. "Measuring and Testing the Impact of News on Volatility," Journal of Finance, American Finance Association, vol. 48(5), pages 1749-1778, December.
    2. Martin D. D. Evans & Richard K. Lyons, 2017. "How is Macro News Transmitted to Exchange Rates?," World Scientific Book Chapters, in: Studies in Foreign Exchange Economics, chapter 14, pages 547-596, World Scientific Publishing Co. Pte. Ltd..
    3. Hao Chen & Qiulan Wan & Yurong Wang, 2014. "Refined Diebold-Mariano Test Methods for the Evaluation of Wind Power Forecasting Models," Energies, MDPI, Open Access Journal, vol. 7(7), pages 1-14, July.
    4. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    5. Flores, Benito E, 1986. "A pragmatic view of accuracy measurement in forecasting," Omega, Elsevier, vol. 14(2), pages 93-98.
    6. Chatrath, Arjun & Miao, Hong & Ramchander, Sanjay & Villupuram, Sriram, 2014. "Currency jumps, cojumps and the role of macro news," Journal of International Money and Finance, Elsevier, vol. 40(C), pages 42-62.
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