Financial Time Series Prediction Using Machine Learning

Year 2022, Volume: 24 Issue: 3, 1205 - 1230, 28.12.2022

Seyyide Doğan , Yasin Büyükkör

https://doi.org/10.26745/ahbvuibfd.1191080

Cited By: 3

Abstract

Making up an important working area of finance applications, financial time series forecasting, with the advancements in Machine Learning (ML) methods in recent years, has become a topic that finance and academic circles attach more importance to. The aim of this study is to present a comparative review of ML methods in financial time series future value. In the study, the last 5-year closing data of two developed and emerging stock market indices and two high-volume stocks of the Istanbul stock market were used. Support Vector Regression (SVR), which is often used in index forecasting and found successful, and Random Forest (RF) and eXtreme Gradient Boosting (XGB) methods which are rarely used ensemble machine learning methods in time series forecasting in literature, are preferred. As a result of the study, when MAE, MAPE and RMSE criterions are taken into consideration, SVR was confirmed to be the best forecasting method.

Keywords

financial time series, machine learning, support vector machine, random forest, xgboost

Makine Öğrenmesi ile Finansal Zaman Serisi Tahminleme

Abstract

Finans uygulamalarının önemli bir çalışma alanını oluşturan finansal zaman serisi tahminlemesi son yıllarda makine öğrenmesi (Machine Learning, ML) yöntemlerinin gelişimi ile finans ve akademi çevrelerinin daha fazla önem atfettiği bir konu olmuştur. Bu çalışmanın amacı, finansal zaman serisi gelecek değerinin tahmininde ML yöntemlerinin karşılaştırmalı olarak bir incelemesini sunmaktır. Çalışmada gelişmiş ve gelişmekte olan iki borsa endeksi ve İstanbul borsasının yüksek hacimli iki hisse senedinin son 5 yıllık kapanış verileri kullanılmıştır. Endeks tahmininde sıklıkla kullanılmış ve başarılı bulunan Destek Vektör Regresyonu (Suport Vector Regression, SVR) ve literatürde zaman serisi tahmininde izine az rastladığımız topluluk (ensemble) makine öğrenmesi yöntemleri olan Rassal Orman (Random Forest, RF) ve Extrem Gradyan Arttırma (eXtreme Gradient Boosting, XGB) yöntemleri tercih edilmiştir. Çalışmanın bulgularına göre, MAE, MAPE ve RMSE kriterleri göz önünde bulundurulduğunda en iyi tahmin yöntemi SVR olarak tespit edilmiştir.

Keywords

finansal zaman serisi, makine öğrenmesi, destek vektör makinesi, rassal orman, xgboost

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