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

TL;DR

This paper explores the use of psycho-linguistic features from financial news articles to predict stock prices in the Indian market, employing various machine learning models and feature selection methods.

Contribution

It introduces a hybrid approach combining psycholinguistic variables and multiple intelligent models for stock prediction in the Indian context.

Findings

GMDH and GRNN outperform other models statistically.

Feature selection improves prediction accuracy.

Psycholinguistic features are effective predictors.

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.