• STOCK MARKET PREDICTION USING ARTIFICIAL INTELLIGENCE: A SYSTEMATIC REVIEW OF SYSTEMATIC REVIEWS

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Metadata

Title

STOCK MARKET PREDICTION USING ARTIFICIAL INTELLIGENCE: A SYSTEMATIC REVIEW OF SYSTEMATIC REVIEWS

Author

LIN, COKA CHINYANG | MARQUES, JOAO ALEXANDRE LOBO

PUBLISH YEAR

2023

FACULTY / RESEARCH UNIT

FACULTY OF BUSINESS AND LAW

Abstract

THERE ARE MANY SYSTEMATIC REVIEWS ON PREDICTING STOCK. HOWEVER, EACH OF THEM REVEALS A DIFFERENT PORTION OF THE HYBRID AI ANALYSIS AND STOCK PREDICTION PUZZLE. THE PRINCIPAL OBJECTIVE OF THIS RESEARCH WAS TO SYSTEMATICALLY REVIEW AND CONCLUDE THE SYSTEMATIC REVIEWS ON AI AND STOCK TO PROVIDE PARTICULARLY USEFUL PREDICTIONS FOR MAKING FUTURE STRATEGIES FOR STOCK MARKETS. KEYWORDS THAT WOULD FALL UNDER THE BROAD HEADINGS OF AI AND STOCK PREDICTION WERE LOOKED UP IN TWO DATABASES, SCOPUS AND WEB OF SCIENCE. WE SCREENED 69 TITLES AND READ 43 SYSTEMATIC REVIEWS WHICH INCLUDE MORE THAN 379 STUDIES BEFORE RETAINING 10 OF THEM.

SUBJECTS

MACHINE LEARNING,DEEP LEARNING,LONG SHORT-TERM MEMORY (LSTM),NEURAL NETWORKS (NN),SUPPORT VECTOR MACHINES (SVM)

DOCUMENT TYPE

preprint

DOI

10.2139/ssrn.4341351

LANGUAGE

ENGLISH

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