Hisse Senedi Fiyat Tahmininde Stacked Generalization Modelinin Kullanımı: BIST100 Endeksi Üzerine Karşılaştırmalı Bir Analiz

Öz

Finansal piyasalara yatırım yapmak hem bireysel hem de kurumsal yatırımcılar için önemli bir strateji ve karar alma sürecini gerektirmektedir. Finansal piyasaların oynaklığı, karmaşık ve sürekli gelişen faktörlerden etkilenerek yatırımcıları, analistleri ve finansal uzmanları gelecekteki fiyat dalgalanmalarını kesin olarak tahmin etmek için giderek daha karmaşık ve veri merkezli metodolojiler kullanmaya teşvik etmektedir. Hisse senedi fiyat tahmini için derin öğrenme modelleri, kapsamlı veri kümelerini birleştirerek karmaşık bağlantıları kavrama yeteneği sayesinde giderek daha çok tercih edilmektedir. Bu makalenin amacı, Türkiye'nin önemli finansal göstergelerinden biri olan BIST100 endeksine ait 04.01.2010-29.11.2023 tarihleri arasındaki 3494 günlük veri setini kullanarak en iyi tahmin yeteneğine sahip modeli belirlemektir. Bu kapsamda SVR, KNN, RF, XGBoost ve Stacked Generalization modeli kullanılmıştır. Modellerin tahmin becerileri RMSE, MSE, MAE ve R2 performans göstergeleri kullanılarak değerlendirilmiştir. Modellerin tahmin performanslarından elde edilen sonuçlara göre, Stacked generalization modeli analiz edilen veri kümesi için tahminler yapmada daha yüksek performans göstermiştir. Bu bulgular gelecekte benzer analizler için model seçerken dikkate alınması gereken değerli bilgiler sunmaktadır.

Anahtar Kelimeler

• BIST100
• Stacked Generalization
• Hisse Senedi Tahmini
• Karar Destek
• Zaman Serisi

Using Stacked Generalization Model in Stock Price Forecasting: A Comparative Analysis on BIST100 Index

Abstract

Investing in financial markets requires an adequately planned approach and decision-making process for both individual and institutional investors. The volatility of financial markets is influenced by intricate and constantly evolving factors, prompting investors, analysts, and financial experts to employ progressively sophisticated and data-centric methodologies to precisely forecast future price swings. Deep learning models for stock price prediction demonstrate the ability to comprehend intricate connections by amalgamating extensive datasets. The objective of this essay is to employ various machine learning models using daily data from the BIST100 index, a prominent financial indicator in Turkey. The models under question encompass Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Random Forest (RF), XGBoost and Stacked Generalization. The models' prediction skills were evaluated using RMSE, MSE, MAE, and R2 performance indicators. Based on the observed results, the Stacked Generalization model demonstrated greater performance in making predictions for the analyzed dataset. These findings offer valuable insights that should be considered when selecting models for similar analyses in the future.

Keywords

• BIST100
• Stacked Generalization
• Stock Prediction
• Decision Support
• Time Series

Kaynakça

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