PREDICTING STOCK PRICES WITH EXTREME LEARNING MACHINES

Year 2017, Volume: 35 Issue: 1, 67 - 88, 31.03.2017

Mehmet Özçalıcı

https://doi.org/10.17065/huniibf.303305

Cited By: 3

Abstract

There are various
benefits of forecasting stock prices before they actually occur. Artificial
Neural Network is one of the methods that is used for forecasting stock prices.
However, the difficulties such as the low speed of training process and the
complexity of determining the parameters make it difficult to implement. In
order to eliminate these difficulties a new method named Extreme Learning
Machine (ELM) is developed. The performance of ELM with different activation
functions have not examined in stock price forecasting problem. In this study,
ELM models with fourteen different activation functions are designed and their
performance are compared with statistical and financial measurements. 12
technical indicators are calculated using the historical price and volume
information belong to Goodyear, Amazon and Wal-Mart as well as SP500 Index. The
output of the model is the closing price of the next day. The performances of
the models are compared with regular artificial neural network models. The
stock prices are correctly forecasted up to 59.32% hit rate. Moreover, higher
paper-returns are obtained from passive buy&hold strategy. The results
obtained in this study shows that ELM is a powerful alternative for stock price
forecasting. 

Keywords

Extreme learning machine, artificial neural networks, forecasting

AŞIRI ÖĞRENME MAKİNELERİ İLE HİSSE SENEDİ FİYAT TAHMİNİ

Abstract

Hisse senedi fiyatlarının
doğru bir şekilde tahmin edilebilmesinin birçok faydalı tarafları mevcuttur.
Yapay sinir ağları hisse senedi fiyat tahmininde başarıyla kullanılan
yöntemlerden biridir. Fakat öğrenme sürecinin yavaş olması, kullanıcının birçok
parametreye karar vermek zorunda olması bu yöntemin dezavantajlarını
oluşturmaktadır. Bu olumsuz durumları ortadan kaldırmak için Aşırı Öğrenme
Makineleri (AÖM) adı verilen yöntem geliştirilmiştir. Literatürde henüz farklı
çeşitlerde aktivasyon fonksiyonu içeren AÖM modellerinin hisse senedi tahmin
performansı üzerindeki etkisi incelenmemiştir. Bu çalışmada on dört farklı
aktivasyon fonksiyonunun kullanıldığı modeller oluşturulmuş ve modellerin
performansları istatistik-sel ve finansal açıdan değerlendirmeye tabi
tutulmuştur. Çalışmada Goodyear, Amazon ve Wal-Mart isimli şirketlere ve SP500
Endeksine ait geçmiş fiyat ve işlem hacmi bilgilerini kullanmak suretiyle 12
adet teknik gösterge hesaplanmış ve modellerin girdi değişkeni olarak
kullanılmıştır. Çıktı değişkeni ise bir sonraki güne ait kapanış fiyatlarını
içermektedir. Modellerin performansı geleneksel yapay sinir ağları modeli ile
karşılaştırılmıştır. Sonuçta %59.32 ‘ye varan oranda fiyatların yönü doğru bir
şekilde öngörülebilmiştir. Ayrıca kâğıt üzerinde, al ve tut stratejisinden daha
yüksek oranda getiriler elde edilmiştir. Sonuçlar, AÖM modelinin hisse senedi
fiyat tahmininde güçlü bir alternatif olduğunu göstermektedir. 

Keywords

Aşırı öğrenme makineleri, yapay sinir ağları, fiyat tahmini

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