Predicting Stock Prices Using Machine Learning Methods and Deep Learning Algorithms: The Sample of the Istanbul Stock Exchange

Authors : Uğur DEMİREL, Handan ÇAM, Ramazan ÜNLÜ

Pages : 63-82

Doi:10.35378/gujs.679103

View : 13 | Download : 7

Publication Date : 2021-03-01

Article Type : Research Paper

Abstract :Stock market prediction in financial and commodity markets is a major challenge for speculators, investors, and companies but also profitable with an accurate prediction. Thus, obtaining accurate prediction results becomes extremely important especially while the stock market is essentially volatile, nonlinear, complicated, adaptive, nonparametric and unpredictable in nature. This study aims to forecast the opening and closing stock prices of 42 firms listed in Istanbul Stock Exchange National 100 Index insert ignore into journalissuearticles values(ISE-100); using well-known machine learning methods, Multilayer Perceptrons insert ignore into journalissuearticles values(MLP); and Support Vector Machines insert ignore into journalissuearticles values(SVM); models and deep learning algorithm, Long Short Term Memory insert ignore into journalissuearticles values(LSTM); by comparing their forecasting performances. The analysis includes 9 years of data from 01.01.2010 to 01.01.2019. For each firm 2249 data for the opening and 2249 for the closing stock prices were established as daily data sets. Forecasting performance of these methods was evaluated by applying different criteria for each model: root mean squared error insert ignore into journalissuearticles values(RMSE);, mean squared error insert ignore into journalissuearticles values(MSE); and R-squared insert ignore into journalissuearticles values(R2);. The results of this study show that MLP and LSTM models become advantageous in estimating the opening and closing stock prices comparing to SVM model.
Keywords : Stock market prices, Multiple layer perceptron, Support vector machines, Long short term memory