Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/258629 
Year of Publication: 
2021
Citation: 
[Journal:] Journal of Risk and Financial Management [ISSN:] 1911-8074 [Volume:] 14 [Issue:] 11 [Article No.:] 526 [Publisher:] MDPI [Place:] Basel [Year:] 2021 [Pages:] 1-34
Publisher: 
MDPI, Basel
Abstract: 
The stock market is characterized by extreme fluctuations, non-linearity, and shifts in internal and external environmental variables. Artificial intelligence (AI) techniques can detect such non-linearity, resulting in much-improved forecast results. This paper reviews 148 studies utilizing neural and hybrid-neuro techniques to predict stock markets, categorized based on 43 auto-coded themes obtained using NVivo 12 software. We group the surveyed articles based on two major categories, namely, study characteristics and model characteristics, where 'study characteristics' are further categorized as the stock market covered, input data, and nature of the study; and 'model characteristics' are classified as data pre-processing, artificial intelligence technique, training algorithm, and performance measure. Our findings highlight that AI techniques can be used successfully to study and analyze stock market activity. We conclude by establishing a research agenda for potential financial market analysts, artificial intelligence, and soft computing scholarship.
Subjects: 
artificial intelligence
neural networks
training algorithm
NVivo
stock market forecast
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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