EXPLORING THE INTERPRETABILITY AND PREDICTIVE POWER OF MACHINE LEARNING MODELS IN TECHNOLOGY INDICES: A CASE STUDY

Year 2025, Volume: 27 Issue: 49, 743 - 757, 29.08.2025

Ahmet Akusta , Mehmet Nuri Salur

https://doi.org/10.18493/kmusekad.1530152

Abstract

The paper is a comprehensive study of the performance evaluation of Aselsan in the Borsa Istanbul Technology Index, explaining the interpretability and predictability power of the machine learning models. The study encapsulates the technical indicators and the index data as variables and is conducted in a dataset of 600 days between November 20, 2020, and April 10, 2023. The data was split into two subsets, with 85% allocated to the training subset and 15% to the validation subset. Model training is conducted using the Orthogonal Matching Pursuit (OMP) algorithm. After the training, the model validates its prediction using previously unseen data. The results of the model's findings at this stage indicate the model's strong capacity to predict and robustly predict movements in Aselsan stock prices. Additionally, the model has an interpretability capacity that helps the user understand the decision process and the reasons behind the predictions.

Keywords

Interpretability , Predictive Power , Stock Price , Machine Learning , Technology Indices

TEKNOLOJİ ENDEKSLERİNDE MAKİNE ÖĞRENİMİ MODELLERİNİN YORUMLANABİLİRLİĞİNİN VE TAHMİN GÜCÜNÜN ARAŞTIRILMASI: BİR VAK’A ÇALIŞMASI

Abstract

Bu çalışma, Aselsan'ın Borsa İstanbul Teknoloji Endeksi'ndeki performans değerlendirmesini, makine öğrenmesi modellerinin yorumlanabilirlik ve öngörülebilirlik gücünü açıklayan kapsamlı bir çalışmadır. Çalışma, teknik göstergeleri ve endeks verilerini değişkenler olarak içermekte olup, 20 Kasım 2020 ile 10 Nisan 2023 tarihleri arasındaki 600 günlük bir veri setinde gerçekleştirilmektedir. Veri seti, eğitim alt kümesi için %85 ve doğrulama alt kümesi için %15 olmak üzere 85:15 veri bölünme oranıyla kullanılmıştır. Model eğitimi, Orthogonal Matching Pursuit (OMP) algoritması kullanılarak gerçekleştirilmiştir. Eğitim sonrasında model, daha önce görülmemiş verileri kullanarak tahminlerini doğrulamaktadır. Bu aşamadaki bulgular, modelin Aselsan hisse senedi fiyatlarındaki hareketleri öngörebilme ve güvenilir bir şekilde tahmin edebilme yeteneğinin güçlü olduğuna işaret etmektedir. Ayrıca model, kullanıcının karar sürecini anlamasına ve tahminlerin arkasındaki nedenleri görmesine yardımcı olan bir yorumlanabilirlik kapasitesine sahiptir.

Keywords

Yorumlanabilirlik , Tahmin Gücü , Hisse Fiyatı , Makine Öğrenimi , Teknoloji Endeksi

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