Comparative Analysis of Machine Learning Algorithms in Stock Price Prediction

Year 2024, Volume: 5 Issue: 2, 36 - 46

Umut Dökmen

https://doi.org/10.54047/bibted.1406867

Abstract

Stock is part of a company's principal. A person who buys stock of a company shares the profit or loss of this company. Large volume transactions are made on stock exchanges where stocks are traded. Stock prices are difficult to predict because they are affected by many variables, but when they can be predicted, great benefits are provided. Prediction of stock prices is possible with today's computers using machine learning algorithms. Machine learning provides more successful results than fundamental and technical analysis in stock price prediction. In our study, daily closing price predictions were made by collecting approximately 5-years data of the top 5 stocks with the highest market value traded in BIST 100 between 2016 and 2020. Multiple linear regression, Bayesian regression, random forest, decision trees, support vector machines, artificial neural networks algorithms were applied to include maximum 22 features and the results were compared. The most successful result was obtained in the artificial neural networks algorithm. To achieve the highest success, data pre-processing, normalization, cross-validation, parameter optimization and feature selection were applied. It has been observed that using these methods together increases the success.

Keywords

Machine Learning, Algorithms, Stock, Regression, Supervised Learning

Hisse Senedi Fiyat Tahmininde Makine Öğrenimi Algoritmalarının Karşılaştırmalı Analizi

Abstract

Hisse senedi bir şirketin anaparasının bir parçasıdır. Bir şirketin hisselerini satın alan kişi, bu şirketin kar veya zararına ortak olur. Hisse senetlerinin işlem gördüğü borsalarda büyük hacimli işlemler yapılmaktadır. Hisse senedi fiyatları birçok değişkenden etkilendiğinden tahmin edilmesi zordur ancak tahmin edilebildiğinde büyük faydalar sağlanır. Hisse senedi fiyatlarının tahmini, makine öğrenmesi algoritmalarını kullanan günümüz bilgisayarları ile mümkün olmaktadır. Makine öğrenimi, hisse senedi fiyat tahmininde temel ve teknik analize göre daha başarılı sonuçlar sağlamaktadır. Çalışmamızda 2016-2020 yılları arasında BIST 100'de işlem gören en yüksek piyasa değerine sahip 5 hisse senedinin yaklaşık 5 yıllık verileri toplanarak günlük kapanış fiyatı tahminleri yapılmıştır. Çoklu doğrusal regresyon, bayesian regresyon, rastgele orman, karar ağaçları, destek vektör makineleri, yapay sinir ağları maksimum 22 özelliği dahil edecek şekilde uygulanmış ve sonuçlar karşılaştırılmıştır. En başarılı sonuç yapay sinir ağları algoritmasında elde edilmiştir. En yüksek başarıyı elde etmek için veri ön işleme, normalleştirme, çapraz doğrulama, parametre optimizasyonu ve özellik seçimi uygulanmıştır. Bu yöntemlerin bir arada kullanılmasının başarıyı arttırdığı gözlemlenmiştir.

Keywords

Makine Öğrenmesi, Algoritmalar, Hisse Senedi, Regresyon, Gözetimli Öğrenme

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