A survey on Stock Market Prediction Techniques

Computer Engineering

Prediction is usually considered a difficult undertaking, but when it comes to stock market prediction, it gets even more difficult because it is dependent on fundamental, societal, economic, political, and many other aspects, making it unpredictable, dynamic, and nonlinear in nature. Many traders, investors, and businesses are drawn to the stock market because a solid prediction model can enhance profits while lowering the chance of loss. It has also succeeded in attracting the attention of the scientific community and researchers, encouraging them to consider it as a potential research domain. For stock market trend prediction, a few of them have successfully designed a methodology/model based on several approaches such as statistical, mathematical, Genetic Algorithm based approach, AI based approach, and so on. Artificial intelligence-based approaches such as Machine learning and Deep learning are the most commonly utilized methods in articles, research papers, and other scientific databases. As a result, the focus of this study review is Artificial Intelligence-based techniques. The majority of the research articles and papers for this review were sourced from scientific databases such as Science Direct, IEEE Access, and Scopus-indexed journals, with a few pieces from other reputable publications also being evaluated.

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