Melez Aşırı Öğrenme Makinesi ve Türevi ile Hisse Senedi Fiyatı Tahmini

Abstract

Accurate and effective stock price prediction is appealing for investors due to the potential of obtaining a very high return. However, it is still a challenging task in the modern business world because of the complex, evolutionary, and nonlinear nature of stock market. Therefore, we proposed two hybrid models, which are Harmony Search (HS) based Extreme Learning Machine (ELM) that is denoted as HS-ELM and HS based Recurrent Extreme Learning Machine (RELM) that is represented as HS-RELM, to provide accurate and fast one-day ahead stock price prediction. This study provides a new direction in the field of stock price prediction and offers some suggestions on how to configure HS-ELM and HS-RELM for performing stock price prediction, with an application on stocks listed in BIST50 Index. The results of the performance measures show that although both proposed models are very helpful for the practical applicability of the stock market, HS-RELM model is more powerful than HS-ELM model. Keywords