WHEN MACHINES LEARN TECHNICAL ANALYSIS: AN APPLICATION ON TECHNICAL ANALYSIS WITH MACHINE LEARNING IN BORSA ISTANBUL

Year 2025, Volume: 27 Issue: IERFM 2025 Özel Sayı, 275 - 302, 14.03.2025

Yunus Emre Akdoğan

https://doi.org/10.26468/trakyasobed.1514346

Abstract

There are two approaches to analyzing the value of a stock in financial markets: fundamental analysis and technical analysis. While fundamental analysis focuses on finding the intrinsic value of a stock based on a company's financial condition and current market conditions, technical analysis focuses on identifying trading signals in patterns by examining historical price behavior and statistics. Although technical analysis, which is based on the assumption that past price movements can be an indicator for future price movements, has a predefined set of rules, the interpretation of the results is closely related to the experience of the analyst. Therefore, the interpretive part of technical analysis has a subjective dimension. This subjective dimension and predefined set of rules indicate that machine learning methods with experience-based learning logic can be an important tool in identifying trading signals or predicting price movements. The aim of this study is to investigate the potential use of machine learning algorithms that use technical analysis indicators of stocks traded in Borsa Istanbul as input to predict trading signals and price movements. In the study, technical analysis indicators are analyzed with models based on machine learning methods and the results are compared. The findings show that the addition of machine learning methods to technical analysis strategies increases the predictive power of trading signals and price movements.

Keywords

Technical Analysis, Machine Learning, Artificial Intelligence

Makineler Teknik Analiz Öğrendiğinde: Borsa İstanbul’da Makine Öğrenmesi ile Teknik Analiz Üzerine Bir Uygulama

Abstract

Finansal piyasalarda bir hisse senedinin değerini analiz etmek için biri temel analiz diğeri teknik analiz olmak üzere iki yaklaşım vardır. Temel analiz bir şirketin mali durumuna ve mevcut piyasa koşullarına bağlı olarak hisse senedinin içsel değerini bulmaya yönelirken teknik analiz tarihsel fiyat davranışlarını ve istatistiklerini inceleyerek örüntülerdeki işlem sinyallerini belirlemeye odaklanmaktadır. Geçmişteki fiyat hareketlerinin gelecekteki fiyat hareketleri için bir gösterge olabileceği varsayımına dayalı olan teknik analizde her ne kadar önceden tanımlanmış kurallar seti olsa da sonuçların yorumlanması analistin deneyimi ile yakından ilişkilidir. Dolayısıyla teknik analizin yoruma açık kısmı öznel bir boyuta sahiptir. Bu öznel boyut ve önceden tanımlanmış kurallar seti, deneyime dayalı öğrenme mantığına sahip makine öğrenmesi yöntemlerinin, işlem sinyallerinin belirlenmesi ya da fiyat hareketlerinin tahmin edilmesinde önemli bir araç olabileceğine işaret etmektedir. Bu çalışmanın amacı girdi olarak Borsa İstanbul’da işlem gören hisse senetlerinin teknik analiz göstergelerini kullanan makine öğrenmesi algoritmalarının, işlem sinyallerini ve fiyat hareketlerini tahmin etmek için potansiyel kullanımını araştırmaktır. Çalışmada teknik analiz göstergeleri makine öğrenmesi yöntemlerine dayalı modeller ile analiz edilmekte ve sonuçlar karşılaştırılmaktadır. Bulgular, makine öğrenmesi yöntemlerinin teknik analiz stratejilerine eklenmesinin, işlem sinyallerini ve fiyat hareketlerini tahmin gücünü artırdığını göstermektedir.

Keywords

Teknik Analiz, Makine Öğrenmesi, Yapay Zekâ

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