Stock Price Prediction

Abstract

In this study, the last two years' hourly opening and closing prices of the banks' stocks traded on BIST-30 were used as the dataset. The research is aimed to predict the closing prices of these stocks in the light of machine learning. In this context, the authors propose a new method containing ensemble learning algorithms and fuzzy clustering technics for predicting stock prices. With this method, authors aim to find stocks which are similar characteristics with test sets and model them together. Thanks to this method, authors aim to improve modelling success. For comparing the results, authors also create models with classical machine learning methods such as support vector machines, random forest, and boosting type new generation algorithms such as extreme gradient boosting and catboost.