Authors : Tayo P. Ogundunmade; Olayinka B. Ayeni

Volume/Issue : Volume 10 - 2025, Issue 10 - October

Google Scholar : https://tinyurl.com/rsatjdwh

Scribd : https://tinyurl.com/mszy32rk

DOI : https://doi.org/10.38124/ijisrt/25oct1245

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Note : Google Scholar may take 30 to 40 days to display the article.

Abstract : The stock market is inherently volatile and influenced by a multitude of dynamic, interrelated factors ranging from macroeconomic indicators and geopolitical events to investor sentiment and technological innovation. For investors and financial analysts, accurately predicting stock prices remains a persistent challenge, particularly in the technology sector, where companies like Apple, Microsoft, Google, and Netflix experience rapid growth, frequent innovation cycles, and high market sensitivity. This project applies machine learning to predict future stock prices for five major tech firms, which include Apple, Tesla, Amazon, Google, and Microsoft. Past stock price datasets available on Kaggle were used, and by leveraging historical adjusted closing prices alongside engineered inputs such as daily returns, moving averages, rolling volatility, and lagged values. We train and compare sequential models, including TCNs, Informer architectures, and LSTMs. The analysis revealed several important insights about the current state of machine learning-based stock prediction. Model performance limitations were identified, with modest accuracies ranging from 49% to 63% for predicting whether stock prices would increase by more than 1.5% within five days. Stock-specific predictability and market insights were also highlighted, with Microsoft emerging as the most predictable (63% accuracy) due to its relatively stable price movements, while Tesla proved most challenging to forecast (49-50% accuracy). Our findings demonstrate that thoughtful feature construction combined with advanced sequence modeling can uncover market dynamics and boost prediction accuracy, showcasing the tangible benefits of AI-driven analytics for informed financial decision-making.

Keywords : Stock Price, Technology Companies, Machine Learning, Regression Models, Prediction.

References :

1. Ogundunmade TP, Adepoju AA, Allam A. Stock Price Forecasting: Machine Learning Models with K-fold and Repeated Cross Validation Approaches. Mod Econ Manag, 2022; 1: 1. DOI: 10.53964/mem.2022001.
2. Kaur, P., Marwaha, K., & Kumar, K. (2024). A novel approach to predict the stock price using LSTM and linear regression. IJSRSET, 24(11), 1–11. Retrieved from https://res.ijsrset.com/page.php?param= IJSRSET2411111
3. Liang, J., Chen, X., & Wang, Y. (2023). Comparison of price prediction based on LSTM, GRU, Random Forest, LSSVM and linear regression. BCPBM, 38(3713). https://doi.org/10.54691/bcpbm.v38i.3713
4. Hu, Z., Zhao, Y., & Khushi, M. (2021). A survey of forex and stock price prediction using deep l4arning. Applied System Innovation, 4(1), 9.
5. Yin, C., Zhang, L., & Liu, H. (2023). Stock price prediction of GM Company: Comparison based on KNN, linear regression and LSTM. In Advances in Economics, Management and Political Sciences (pp. 82–89). EWADirect. https://www.ewadirect.com/ proceedings/aemps/article/view/8253
6. Zheng, Z. (2023). A review of stock price prediction based on LSTM and TCN methods. Advances in Economics, Management and Political Sciences, 46, 48–54. https://doi.org/10.54254/2754-1169/46/20230316
7. Crow, J. (2023). Stock Market Dataset [Data set]. Kaggle. https://www.kaggle.com/datasets/ jacksoncrow/stock-market-dataset
8. Li, X., Zheng, Z., & Li, Y. (2020). Deep reinforcement learning for optimal investment portfolio. Complexity, 2020.
9. Bukhari, A. H., Raja, M. A. Z., Sulaiman, M., Islam, S., Shoaib, M., & Kumam, P. (2020). Fractional neuro-sequential ARFIMA-LSTM for financial market forecasting. Ieee Access, 8, 71326-71338.
10. Khan, W., Ghazanfar, M. A., Azam, M. A., Karami, A., Alyoubi, K. H., & Alfakeeh, A. S. (2022). Stock market prediction using machine learning classifiers and social media, news. Journal of Ambient Intelligence and Humanized Computing, 1-24.
11. Kumbure, M. M., Lohrmann, C., Luukka, P., & Porras, J. (2022). Machine learning techniques and data for stock market forecasting: A literature review. Expert Systems with Applications, 197, 116659.
12. Kurani, A., Doshi, P., Vakharia, A., & Shah, M. (2023). A comprehensive comparative study of artificial neural network (ANN) and support vector machines (SVM) on stock forecasting. Annals of Data Science, 10(1), 183-208.
13. Sharma, D. K., Hota, H. S., Brown, K., & Handa, R. (2022). Integration of genetic algorithm with artificial neural network for stock market forecasting. International Journal of System Assurance Engineering and Management, 13(Suppl 2), 828-841.
14. Thakkar, A., & Chaudhari, K. (2020). Predicting stock trend using an integrated term frequency–inverse document frequency-based feature weight matrix with neural networks. Applied Soft Computing, 96, 106684.
15. Yadav, A., Jha, C. K., & Sharan, A. (2020). Optimizing LSTM for time series prediction in Indian stock market. Procedia Computer Science, 167, 2091-2100.
16. Zakhidov, G. (2024). Economic indicators: tools for analyzing market trends and predicting future performance. International Multidisciplinary Journal of Universal Scientific Prospectives, 2(3), 23-29.
17. Ogundunmade, T.P., Adepoju, A.A. (2023). Predicting the Nature of Terrorist Attacks in Nigeria Using Bayesian Neural Network Model. In: Awe, O.O., Vance, E.A. (eds) Sustainable Statistical and Data Science Methods and Practices. STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health. Springer, Cham. https://doi.org/10.1007/978-3-031-41352-0_14
18. Ogundunmade TP, Abidoye M, Olunfunbi OM. Modelling Residential Housing Rent Price Using Machine Learning Models. Modern Economy and Management. 2023; 2: 14. doi: 10.53964/mem.2023014.
19. Ogundunmade TP, Daniel AO, M. Awwal A. Modelling Infant Mortality Rate using Time Series Models. International Journal of Data Science. 2023; 4(2): 107-115. doi: 10.18517/ijods.4.2.107-115.2023.
20. Ogundunmade TP, Ganiyu KA, Yahaya OT. Assessment of profitability and inventory management in the Nigerian power generation asset companies. Financial Statistical Journal. 2025; 8(1): 11398. https://doi.org/10.24294/fsj11398
21. Afolabi O. Adedamola; Tayo P. Ogundunmade (2025). Predictive Modelling of Crime Data using Machine Learning Models: A Case Study of Oyo State, Nigeria. International Journal of Innovative Science and Research Technology, 10(4), 1669-1677. https://doi.org/10.38124/ijisrt/25apr851.