Artificial Neural Networks for Stock Market Prediction: A Comprehensive Review

Essam H. Houssein, Mahmoud Dirar, Kashif Hussain, Waleed M. Mohamed

Research output: Chapter in Book/Report/Published conference proceedingChapter


The forecasting of stock market is known to be a remarkable effort and a great deal of attention, as forecasting stock prices can effectively steer to desirable profits by making sound investment choices. It is a challenging job due to highly non-linear, blaring, and unpredictable data. Currently, a variety of useful methods have been developed to predict stock prices. This chapter provides a thorough analysis of 48 research papers proposing artificial neural networks-based stock price prediction methodologies. Here, the reported research is categorized on the basis of various prediction techniques. Moreover, the studies are evaluated based on databases used, performance assessment indicators, and prediction targets. The collective evidence suggests that stock market prediction involves numerous factors that need to be efficiently and precisely addressed.
Original languageEnglish
Title of host publicationMetaheuristics in Machine Learning: Theory and Applications
EditorsDiego Oliva, Essam H. Houssein, Salvador Hinojosa
Place of PublicationCham
PublisherSpringer International Publishing AG
Number of pages36
ISBN (Electronic)978-3-030-70542-8
ISBN (Print)978-3-030-70541-1
Publication statusPublished - 14 Jul 2021

Publication series

NameStudies in Computational Intelligence

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