Prediction of Borsa Istanbul 100 Index Direction via Deep Learning Based Image Classification Approach

Year 2023, Volume: Vol:8 Issue: Issue:2, 93 - 101, 20.12.2023

Yahya Altuntaş , Fatih Kocamaz

https://doi.org/10.53070/bbd.1399935

Abstract

Stock market indices are used as a significant reference by investors when making investment decisions, as they reflect the overall economic performance. Additionally, portfolio management companies' investment funds that replicate indices can be bought and sold in real time, similar to stocks. Therefore, predicting the future direction of the stock market index is of critical importance for investors. In this study, a deep learning-based image classification approach is proposed for predicting the direction of the Borsa Istanbul 100 (BIST100) index. Initially, daily BIST100 index values were labeled as up or down by comparing them with the index value of the following day. The labeled data was then transformed into graphic images using technical analysis indicators. For the prediction model, pre-trained Convolutional Neural Network (CNN) models, namely AlexNet, GoogLeNet, and ResNet-50, were fine-tuned to adapt to the problem. Fine-tuned AlexNet, GoogLeNet, and ResNet-50 models achieved accuracy values of 54.22%, 53.01%, and 54.62%, respectively, in predicting the direction of the BIST100 index during the three-year out-of-sample period. Additionally, the Naive comparison method was used to evaluate the performance of the models. Experimental results indicate that all three fine-tuned CNN models outperformed the Naive comparison method.

Keywords

BIST100, Financial time series analysis, Deep learning, Convolutional neural networks, Transfer learning.

Derin Öğrenme Tabanlı Görüntü Sınıflandırma Yaklaşımı ile Borsa İstanbul 100 Endeks Yönünün Tahmini

Abstract

Borsa endeksleri, genel ekonomik performansı yansıttıkları için yatırımcılar tarafından yatırım kararları alırken önemli bir referans olarak kullanılırlar. Ayrıca, portföy yönetim şirketlerinin endeksleri replike eden yatırım fonları, hisse senetleri gibi gerçek zamanlı olarak alınıp satılabilir. Bu nedenle, borsa endeksinin gelecekteki yönünü tahmin etmek, yatırımcılar için kritik bir öneme sahiptir. Bu çalışmada, Borsa İstanbul 100 (BIST100) endeksinin yön tahmininde derin öğrenme tabanlı bir görüntü sınıflandırma yaklaşımı önerilmiştir. İlk olarak, günlük BIST100 endeks değerleri bir sonraki günün endeks değeri ile karşılaştırılarak yukarı ve aşağı şeklinde etiketlendi. Etiketlenen veriler daha sonra teknik analiz göstergeleri kullanılarak grafik görüntülerine dönüştürüldü. Tahmin modeli için, ön-eğitimli evrişimsel sinir ağı (CNN) modelleri olan AlexNet, GoogLeNet ve ResNet-50, problemle uyumlu hale getirilmek üzere ince ayarlandı. Eğitim dışı bırakılan üç yıllık dönem boyunca ince-ayarlama yapılmış AlexNet, GoogLeNet ve ResNet-50 modelleri sırasıyla %54,22, %53,01 ve %54,62 doğruluk değerleri ile BIST100 endeks yönünü tahmin edebilmiştir. Ayrıca, modellerin performansını değerlendirmek için Naive karşılaştırma yöntemi kullanıldı. Deneysel sonuçlar, ince ayarlı her üç CNN modelinin Naive karşılaştırma yönteminden daha iyi sonuçlar elde ettiğini göstermektedir.

Keywords

BIST100, Finansal zaman serisi analizi, Derin öğrenme, Evrişimsel sinir ağları, Transfer öğrenme.

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