Mustafa GÖÇKEN, Mehmet ÖZÇALICI, Aslı BORU, Ayşe Tuğba DOSDOĞRU

Melez Aşırı Öğrenme Makinesi ve Türevi ile Hisse Senedi Fiyatı Tahmini

Çok yüksek getiri elde etme potansiyeline sahip olması nedeniyle doğru ve etkili hisse senedi fiyatı tahmini yatırımcılar için caziptir. Bununla birlikte, borsanın karmaşık, evrimsel ve doğrusal olmayan yapısı nedeniyle, modern iş dünyasında hâlâ karmaşık bir iştir. Bu nedenle, iki melez model, HS-ELM olarak adlandırılan Harmoni Araması (HS) tabanlı aşırı öğrenme makinesi (ELM) ve HS-RELM olarak adlandırılan HS tabanlı tekrarlı aşırı öğrenme makinesi (RELM), günlük hisse senedi fiyatı tahminini doğru ve hızlı bir şekilde elde etmek için önerilmiştir. Bu çalışma, hisse senedi fiyatı tahmini alanına yeni bir yön vermekte ve BIST50 Endeksinde bulunan farklı hisse senetleri üzerinde uygulanması ile HS-ELM ve HS-RELM'nin hisse senedi fiyat tahmininde nasıl yapılandırılması gerektiği konusunda bazı öneriler sunmaktadır. Performans ölçümlerinin sonuçları, her iki önerilen modelin hisse senetleri fiyat tahminine pratik uygulanabilirliği açısından oldukça yararlı olduğunu göstermesine rağmen HS-RELM modelinin performansının HS-ELM modelinin performansından daha iyi olduğu gözlemlenmiştir.

Anahtar Kelimeler:

Aşırı öğrenme makinesi, Tekrarlı aşırı öğrenme makinesi, Harmoni araması, Hisse senedi fiyatı tahmini

A Hybrid Extreme Learning Machine and its Variant for Stock Price Prediction

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:

Extreme learning machine, Recurrent extreme learning machine, Harmony search, Stock price prediction,

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