Affiliations: Department of Computer Engineering, Faculty of Çorlu Engineering, Namık Kemal University, TR-59860 Çorlu, Tekirdağ, Turkey. E-mail: ptufekci@nku.edu.tr
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.
