Stock price prediction using combined GARCH-AI models
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, particul...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Scientific African |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2468227624003168 |
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| author | John Kamwele Mutinda Amos Kipkorir Langat |
| author_facet | John Kamwele Mutinda Amos Kipkorir Langat |
| author_sort | John Kamwele Mutinda |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-29d0b47701e54e32b5cefa8cab30cbd7 |
| institution | OA Journals |
| issn | 2468-2276 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Scientific African |
| spelling | doaj-art-29d0b47701e54e32b5cefa8cab30cbd72025-08-20T01:57:04ZengElsevierScientific African2468-22762024-12-0126e0237410.1016/j.sciaf.2024.e02374Stock price prediction using combined GARCH-AI modelsJohn Kamwele Mutinda0Amos Kipkorir Langat1African Master’s In Machine Intelligence (AMMI), Senegal; Pan African University Institute for Basic Sciences, Technology and Innovation-JKUAT, Department of Mathematics, Nairobi, KenyaCorresponding author.; African Master’s In Machine Intelligence (AMMI), Senegal; Pan African University Institute for Basic Sciences, Technology and Innovation-JKUAT, Department of Mathematics, Nairobi, KenyaThe 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.http://www.sciencedirect.com/science/article/pii/S2468227624003168LSTMGRUGARCHTransformers |
| spellingShingle | John Kamwele Mutinda Amos Kipkorir Langat Stock price prediction using combined GARCH-AI models Scientific African LSTM GRU GARCH Transformers |
| title | Stock price prediction using combined GARCH-AI models |
| title_full | Stock price prediction using combined GARCH-AI models |
| title_fullStr | Stock price prediction using combined GARCH-AI models |
| title_full_unstemmed | Stock price prediction using combined GARCH-AI models |
| title_short | Stock price prediction using combined GARCH-AI models |
| title_sort | stock price prediction using combined garch ai models |
| topic | LSTM GRU GARCH Transformers |
| url | http://www.sciencedirect.com/science/article/pii/S2468227624003168 |
| work_keys_str_mv | AT johnkamwelemutinda stockpricepredictionusingcombinedgarchaimodels AT amoskipkorirlangat stockpricepredictionusingcombinedgarchaimodels |