Abstract
Forecasting stock market based on the information available with high precision is not so consistent because of its unsteady nature. There are numerous approaches in the anticipation of stock markets. Machine learning systems are a standout among other methodologies in expectation. Numerous researchers have done wide research over the years using different machine learning algorithms. In this paper, the written work examines on different computational tools such as genetic algorithms (GAs), support vector machine (SVM), artificial neural networks (ANNs) are used for stock market forecasting.
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References
Kyoung-jae Kim IH (2000) Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index, pp 125–132, Elsevier
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de Oliveira FA, Nobre CN (2011) The use of artificial neural networks in the analysis and prediction of stock prices. IEEE
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Lalithendra Nadh, V., Syam Prasad, G. (2019). Stock Market Prediction Based on Machine Learning Approaches. In: Computational Intelligence and Big Data Analytics. SpringerBriefs in Applied Sciences and Technology(). Springer, Singapore. https://doi.org/10.1007/978-981-13-0544-3_7
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DOI: https://doi.org/10.1007/978-981-13-0544-3_7
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Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-0543-6
Online ISBN: 978-981-13-0544-3
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