Makine Öğrenmesi Algoritmalarıyla Hisse Senedi Kapanış Fiyat Tahmini: BIST’te Yer Alan PETKM Hisse Senedi Örneği

Year 2023, Volume: 11 Issue: 2, 958 - 976, 30.04.2023

Şevval Toprak , Gültekin Çağıl , Abdullah Hulusi Kökçam

https://doi.org/10.29130/dubited.1096767

Cited By: 1

Abstract

Bu çalışmada Borsa İstanbul’da (BİST) yer alan Petkim Petrokimya Holding A.Ş.’nin (PETKM) hisse senedi fiyatından, Dolar (USD/TRY) fiyatından ve BİST Kimyasal, Petrol & Plastik (XKMYA) indeks fiyatından yararlanılarak, PETKM hisse senedi fiyatının tahmin edildiği üç girdili ve bir çıktılı bir zaman serisi veri seti oluşturulmuştur. Zaman serisi modelleri için Random Forest Regression (RFR), Long-Short Term Memory (LSTM) ve Convolutional Neural Network (CNN) algoritmalarının ayrı ayrı çalışmalarda başarılı sonuçlar elde ettikleri görüldüğünden hisse senedi fiyatının tahmini için bu üç algoritma kullanılmıştır. Literatürde belirtilen kapsamda bu üç yöntemin karşılaştırıldığı bir çalışmaya rastlanmamıştır. Algoritmaların başarısı, genellikle bu tür çalışmalarda kullanılan MSE, RMSE ve MAE olmak üzere üç hata metrik değerleriyle ve R2 değeriyle karşılaştırılmıştır. Hesaplanan hata metriklerine göre LSTM ve RFR algoritmalarında MSE değeri 0.02’den küçük olup CNN’den daha başarılı sonuçlar vermesine rağmen R2 değerlerinin %95’te büyük olmasıyla her üç algoritmadan oluşturulan en başarılı modellerin bu veri setinin tahmininde kullanılabileceği görülmüştür.

Keywords

Hisse senedi fiyat tahmini, Makine öğrenmesi, Derin öğrenme, RFR, LSTM

Stock Closing Price Prediction with Machine Learning Algorithms: PETKM Stock Example In BIST

Abstract

This study predicts the stock price of Petkim Petrokimya Holding Corp. (PETKM), which is listed in Borsa Istanbul (BIST), using PETKM stock price, US dollar (USD/TRY) price and BIST Chemical, Petroleum & Plastic (XKMYA) index price. A time series data set with three inputs and one output is created using these data. Random Forest Regression (RFR), Long-Short Term Memory (LSTM), and Convolutional Neural Network (CNN) algorithms are used in the prediction model. The success of these methods is compared using performance metrics such as MSE, RMSE, MAE, and R2. According to the calculated error metrics, LSTM and RFR algorithms gave better results than CNN with an MSE value less than 0.02. However, the fact that the R2 values of the most successful models created with all three algorithms were greater than 95% revealed that all the algorithms mentioned could be used to estimate this data set.

Keywords

Stock price prediction, Machine learning, RFR, Deep learning, LSTM, CNN

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