  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.
