An Integrated Wavelet Transform-Convolutional Neural Network Based Deep Learning Approach and An Application On Stock Exchange Estimation

Öz

Stock market estimation is one of the most studied topics in financial estimation studies. Developing a better estimation tool or methodology for the stock market, which is a risk-intensive investment tool, is invaluable for investors. In this study, a stock market estimation study was carried out with the daily data of Borsa Istanbul (BIST) Industrial index (XUSIN). In this context, the opening prices of the 5,000-day index (31.12.2001-31.12.2021) were utilized and three models were developed for estimating. In Model 1, estimations were conducted with values that 1 day behind; in Model 2, with values that 3 days behind and in Model 3, with values that 7 days behind, respectively. Convolutional Neural Network (CNN) and Wavelet Transform Convolutional Neural Network (WTCNN), which are deep learning methods whose effectiveness has been demonstrated in many studies, were utilized as estimation methods. Thus, the effect of wavelet transforms which decomposes dataset into subsets for a stationary situation for estimation performance was investigated. It was concluded that the estimation success could be increased with the DDESA approach and it could be used as an effective estimation tool.

Anahtar Kelimeler

• Stock Exchange
• Estimation
• Deep Learning
• Wavelet Transform
• Convolutional Neural Network

Bütünleşik Dalgacık Dönüşümü-Evrişimsel Sinir Ağları Tabanlı Derin Öğrenme Yaklaşımı ve Borsa Tahmini Üzerine Bir Uygulama

Öz

Finansal tahminleme çalışmalarında üzerinde en fazla çalışılan konulardan biri borsa tahminidir. Risk yoğun bir yatırım aracı olan borsa için iyi bir tahmin aracının veya metodolojisinin geliştirilebilmesi yatırımcılar için paha biçilemez önemdedir. Bu çalışmada, Borsa İstanbul Sınai endeksi günlük verisi ile bir borsa tahmini çalışması gerçekleştirilmiş ve borsanın açık olduğu 5.000 günlük (31.12.2001-31.12.2021) endeks açılış fiyatları kullanılarak tahmin için 3 model kurulmuştur. Model 1.’de 1 gün geriden gelen değerlerle, Model 2.’de 3 gün geriden gelen değerlerle ve Model 3.’de ise 7 gün geriden gelen değerlerle tahmin yapmıştır. Tahmin yöntemi olarak etkinliği pek çok çalışmada ortaya konulmuş bir derin öğrenme yöntemi olan Evrişimsel Sinir Ağları (ESA) ve Dalgacık Dönüşümü (DD) ile önişleme tabi tutulmuş ESA (DDESA) yöntemleri kullanılmıştır. Böylece durağan bir durum için veri kümesini alt kümelere ayrıştıran dalgacık dönüşümünün tahmin performansına etkisi araştırılmıştır. Çalışmanın sonucunda DDESA yaklaşımı ile tahmin başarısının artırılabildiği ve etkin bir tahminleme aracı olarak kullanılabileceği sonucuna ulaşılmıştır.

Anahtar Kelimeler

• Borsa
• Tahminleme
• Derin Öğrenme
• Dalgacık Dönüşümü
• Evrişimsel Sinir Ağları

Kaynakça

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