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Volume 2 , Issue 1 , PP: 53-63, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Doaa Sami Khafaga 1 * , Sunil Kumar 2
Doi: https://doi.org/10.54216/MOR.020105
Future stock price prediction is one of the most important and complex tasks in the lecture on finance, mainly due to the characteristics of the financial world. Machine learning techniques have greatly improved this area: problems with frequent data and nonlinear processes, which cannot be solved using conventional models, have been solved. In this paper, the author looks at how the methodology of data preprocessing and two modeling techniques, namely, the high-frequency data model and the sentiment analysis model, have helped improve the efficiency of stock price forecasts. Among the proposed techniques, Temporal Convolutional Networks (TCN), Attention Mechanisms, and Transformer-based architectures are mentioned due to their capability to distill complex market dynamics. However, issues like data quality and fluctuations in the market remain sticky even as we see the speed of innovation picking up, and thus, the importance of model robustness and interpretability. Drawing on recent advances and mapping out the directions for future studies, this paper reveals how machine learning may revolutionize stock market prediction and investment decision-making in a continuously transforming financial environment.
High-frequency data , Hybrid models , Machine learning , Stock price prediction , Sentiment analysis , Financial forecasting
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