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.