EXAMINING THE EFFECT OF BIAS ON STOCK PRICE PREDICTION PERFORMANCE

Year 2017, Volume: 35 Issue: 4, 97 - 113, 29.12.2017

Mehmet Özçalıcı

https://doi.org/10.17065/huniibf.372406

Abstract

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

EŞİK DEĞERİNİN HİSSE SENEDİ FİYAT TAHMİN PERFORMANSINA ETKİSİNİN İNCELENMESİ

Abstract

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. 

Keywords

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

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