EŞİK DEĞERİNİN HİSSE SENEDİ FİYAT TAHMİN PERFORMANSINA ETKİSİNİN İNCELENMESİ

Yapay sinir ağları hisse senedi fiyat tahmininde başarı ile uygulanmaktadır. Eşik değerleri yapay sinir ağlarının performansını etkileyebilecek önemli bir unsurdur. Bununla beraber yapay sinir ağları modelinde yer alan eşik değerinin tahmin performansı üzerindeki etkisinin incelendiği bir çalışmaya literatürde rastlanma-mıştır. Bu çalışmanın amacı eşik değerinin hisse senedi fiyat tahmin performansı üzerindeki etkisini incelemektir. Bu amaçla, BIST100 endeksinde listelenen 100 adet hisse senedine ilişkin 1 Ocak 2014 ve 30 Haziran 2015 tarihleri arasındaki tarihi fiyat ve işlem hacmi bilgileri kullanılmıştır. Bu verilerden yola çıkmak suretiyle 201 adet teknik gösterge hesaplanmıştır. Yapay sinir ağlarındaki aktivasyon fonksiyonu çeşidi, ara katmanda yer alması gereken nöron sayısı ve değişken seçimi, popülasyon tabanlı meta-sezgisel bir yöntem olan Harmoni Arama algoritması ile optimize edilmiştir. Performans ölçüsü olarak RMSE ve doğru tahmin oranı değerleri kullanılmıştır. Sonuçta eşik değerinin yer aldığı modeller ile eşik değerinin yer almadığı modeller arasında istatistiksel açıdan anlamlı bir performans farklılığı tespit edilememiştir. Bununla beraber eşik değerinin yer almadığı modellerin eğitiminin daha kısa sürede tamamlandığı belirlenmiştir.

Anahtar Kelimeler:

Yapay sinir ağları, hisse senedi fiyat tahmini, harmoni arama, eşik değeri

EXAMINING THE EFFECT OF BIAS ON STOCK PRICE PREDICTION PERFORMANCE

Artificial Neural Network models are successfully applied in the field of stock price predicting. Bias values are important factor that can affect the performance of neural networks. Although its importance, the effect of bias on stock price forecasting has not been investigated in literature. The purpose of this study is to examine the effect of bias on stock price predicting performance. For this purpose, historical price and volume information of stocks listed in BIST100 Index is used. By means of these data, 201 technical indicators are calculated. Activation function type in forecasting model and number of neurons in the hidden layer and variable selection are optimized with the Harmony Search Algorithm, a population-based meta-heuristic optimization method. RMSE and hit rate measurements are used as performance indicators. As a result, no statistically significant performance difference was found between biased and un-biased neural network models. However, it has been determined that the training of the models for un-biased models has been completed in a shorter time.

Keywords:

Artificial neural networks, stock price predicting, harmony search, bias value,

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