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Journals / Hacettepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi / 2017 / Cilt: 35 - Sayı: 1

PREDICTING STOCK PRICES WITH EXTREME LEARNING MACHINES

AŞIRI ÖĞRENME MAKİNELERİ İLE HİSSE SENEDİ FİYAT TAHMİNİ

Mehmet ÖZÇALICI
Pages
67–89
Publication Date
2022-10-18
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results obtained in this study shows that ELM is a powerful alternative for stock price forecasting.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:There are various benefits of forecasting stock prices before they actually occur.
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Key Findings & Data:The performance of ELM with different activation functions have not examined in stock price forecasting problem.
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Conclusion & Impact:The results obtained in this study shows that ELM is a powerful alternative for stock price forecasting.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Aşırı öğrenme makineleri · yapay sinir ağları · fiyat tahmini

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.

Özet

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.

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Cite This Article

ÖZÇALICI, M. (2022). PREDICTING STOCK PRICES WITH EXTREME LEARNING MACHINES. Hacettepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, Cilt: 35 - Sayı: 1, 67–89. https://ajindex.com/articles/533009

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