FORECASTING THE BIST 30 INDEX SHARES USING ARTIFICIAL INTELLIGENCE TECHNIQUES

Year 2023, , 270 - 286, 31.03.2023

Mehmet Harun Songün , Murat Akbalık

https://doi.org/10.29106/fesa.1230607

Abstract

In this article, supervised Machine Learning model was used to statistically calculate the future value of the shares in the BIST 30 index as of January 2022. The future price forecasting is calculated by using the daily opening, closing, low price, high price and volume data of the stocks in the BIST 30 index. December 2003-January 2022 was used as the daily return range. The data set covers the pandemic period that started in 2020 and the post-economic crisis period in Turkey in 2001. Different from the studies in the literature in terms of scope, two different time frame price predictions were applied to each of 30 different data sets. Data sets covers minumum 2915 and maximum 4707 trading days. Decision Treet algorithm, one of the Artificial Intelligence and Machine Learning algorithms, is used to predict the future price.

Keywords

Artificial Intelligence, Decision Tree, Price Forecast

BIST 30 ENDEKSİ PAYLARININ YAPAY ZEKA YÖNTEMİYLE TAHMİNİ ÜZERİNE BİR ARAŞTIRMA

Abstract

Bu makalede, 7 Ocak 2022 cuma günü itibarıyla Borsa İstanbul 30 endeksinde işlem gören payların gelecekteki değerini matematiksel model ve algoritmalarla hesaplamak için denetimli Makine Öğrenimi modeli kullanılmıştır. Gelecekteki fiyat öngörüsünü BIST 30 endeksinde yer alan payların Aralık 2003-Ocak 2022 tarihleri arasındaki günlük açılış, kapanış, düşük fiyat, yüksek fiyat ve hacim verileri kullanılarak hesaplanmıştır. Veri seti 2020 yılında başlayan pandemi dönemini ve 2001 yılında Türkiye’de yaşanan ekonomik kriz sonrası dönemi kapsamaktadır.Literatürde yer alan çalışmalardan kapsam bakımından farklı olarak her biri en az 2915 en çok 4707 işlem gününü kapsayan 30 farklı veri setine iki farklı zaman dilimi fiyat öngörüsü uygulanmıştır. Gelecek dönem fiyat öngörüsünde bulunabilmek için Yapay Zeka,Makine Öğrenimi algoritmalarından olan Karar Ağacı algoritması kullanılmıştır.

Keywords

Yapay Zeka, Karar Ağac ı, Fiyat Öngörüsü, Bist30, Artificial Intelligence, Decision Tree, Price Forecast

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