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

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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).