Use of Machine Learning in Stock Market Prediction
DOI:
https://doi.org/10.47672/ejt.634Keywords:
Machine learning, genetic algorithm, stock market prediction, stock market valuations, artificial intelligence, neural networksAbstract
Objective: The objective of this study was to examine and determine future directions in regard to future machine learning techniques based on the review of the current literature.
Methodology: A systematic review has been used to review the current trends from the peer-reviewed journal articles in the past twenty years. For this study, four categories have been categorized, the use of neural networks, support vector machines, the use of a genetic algorithm, and the combination of hybrid techniques. Studies in each of these categorize have been evaluated.
Finding: Firstly, there is a strong link between machine learning methods and the prediction problems they are associated with. The second conclusion that we can conclude from this review is that past studies need to improve its generalizability results. Most of the studies that have been reviewed in this analysis has only used the machine learning systems through the use of one market or during only a one time period without taking into consideration whether the system would be adaptable in other situations and conditions. Limitations, future trends, as well as policy implications have been defined.
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