Classification-based prediction models for stock price index movement

Abstract

Stock price prediction with high accuracy may offer significant opportunities for the investors who make decisions on making profit or having high gains over the stocks in stock markets. In this study, four predictive models have been developed for classification task in predicting the direction of movement in the sessional, daily, weekly, and monthly Istanbul Stock Exchange National (ISEN) 100 Index using five years of data. Multilayer perceptron (MLP), which comprises artificial neural networks (ANN), Logistic Regression (LR), and Bagging of Logistic Regression (BLR) classification techniques are used in the models. During the prediction, four datasets are used and the following factors are taken into account: data of macroeconomic indicators, gold prices, oil prices, foreign exchange prices, stock price indexes in various countries, and the data of the ISEN 100 index for past sessions and prior days, which are used as input variables in the datasets. In connection with that, the most effective factors of these input variables were determined by using some feature selection methods. As a result, prediction performances showed that using reduced datasets consisting of only selected the most important features induced a predictive model of each dataset for classification modelling with a better predictive accuracy than using original datasets. Experimental results showed that prediction performances of the models, which are 64.13%, 63.09%, 81.54%, and 100% for the sessional, daily, weekly, and monthly datasets respectively, were found by MLP significantly better than the other classifiers used in this study.