Stock price prediction using combined GARCH-AI models

Abstract

The non-linear and non-stationary nature of financial time series data poses significant challenges for standalone statistical and neural network methods. While predictive modeling in finance often focuses on volatility, there is a notable lack of research on predicting actual stock prices, particularly in the African market. This study addresses this gap by utilizing Airtel stock data from Yahoo Finance, spanning June 28, 2019, to May 8, 2024. The research employs the GARCH model to extract statistical properties, which are then combined with historical prices and fed into LSTM, GRU, and Transformer models leading to GARCH-LSTM, GARCH-GRU, GARCH-Transfomer hybrid models. These hybrid models are benchmarked against standalone LSTM, GRU and Transfomer models using RMSE, MAE, MAPE, and R-squared metrics. Results indicate that hybrid models, especially GARCH-LSTM, significantly outperform standalone models. This integration of GARCH with advanced AI models offers a more robust framework for stock price prediction, enhancing accuracy and reliability in forecasting future prices.