Improved Stock Price Prediction Model in the Nigeria Bank Sector Using Ensemble Machine Learning Models

Abstract

Stock market prediction remains a critical challenge in emerging economies, particularly within volatile

financial landscapes like Nigeria. Despite significant technological advancements, existing research

predominantly relies on single-model approaches that inadequately capture the complex, non-linear dynamics of

financial markets. This study addresses the methodological gap by developing an ensemble machine learning

model for predicting stock prices in the Nigerian banking sector. The research utilized historical stock price data

from Guaranty Trust Bank and First Bank (2018-2023), integrating advanced preprocessing techniques,

employing rigorous data transformation, feature standardization, and cross-validation strategies, the study

transforms raw financial data into a robust predictive framework. Empirical results reveal distinct performance

metrics across ensemble models: Among the models, Gradient Boosting achieved an MAE of 0.1547, MSE of

0.0918, and RMSE of 0.999, while the Stacking Regressor yielded an MAE of 0.1912, MSE of 0.1396, and

RMSE of 0.9989, highlighting their accuracy and reliability in volatile market conditions. The ensemble

methodology demonstrates superior performance in capturing intricate market dynamics, offering significant

improvements over traditional forecasting techniques by integrating macroeconomic indicators and advanced

machine learning algorithms. The findings underscore the potential of ensemble machine learning in decoding

complex financial patterns, providing valuable insights for investors, financial analysts, and policymakers.