Application of Artificial Intelligence in Stock Market Forecasting: A Critique, Review, and Research Agenda

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 a...

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Autores principales:	Ritika Chopra, Gagan Deep Sharma
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	artificial intelligence neural networks training algorithm NVivo stock market forecast Risk in industry. Risk management HD61 Finance HG1-9999
Acceso en línea:	https://doaj.org/article/1d24f554e2584bd4a02e7a6d29f9dd19
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id	oai:doaj.org-article:1d24f554e2584bd4a02e7a6d29f9dd19
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spelling	oai:doaj.org-article:1d24f554e2584bd4a02e7a6d29f9dd192021-11-25T18:08:34ZApplication of Artificial Intelligence in Stock Market Forecasting: A Critique, Review, and Research Agenda10.3390/jrfm141105261911-80741911-8066https://doaj.org/article/1d24f554e2584bd4a02e7a6d29f9dd192021-11-01T00:00:00Zhttps://www.mdpi.com/1911-8074/14/11/526https://doaj.org/toc/1911-8066https://doaj.org/toc/1911-8074The 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.Ritika ChopraGagan Deep SharmaMDPI AGarticleartificial intelligenceneural networkstraining algorithmNVivostock market forecastRisk in industry. Risk managementHD61FinanceHG1-9999ENJournal of Risk and Financial Management, Vol 14, Iss 526, p 526 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial intelligence neural networks training algorithm NVivo stock market forecast Risk in industry. Risk management HD61 Finance HG1-9999
spellingShingle	artificial intelligence neural networks training algorithm NVivo stock market forecast Risk in industry. Risk management HD61 Finance HG1-9999 Ritika Chopra Gagan Deep Sharma Application of Artificial Intelligence in Stock Market Forecasting: A Critique, Review, and Research Agenda
description	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.
format	article
author	Ritika Chopra Gagan Deep Sharma
author_facet	Ritika Chopra Gagan Deep Sharma
author_sort	Ritika Chopra
title	Application of Artificial Intelligence in Stock Market Forecasting: A Critique, Review, and Research Agenda
title_short	Application of Artificial Intelligence in Stock Market Forecasting: A Critique, Review, and Research Agenda
title_full	Application of Artificial Intelligence in Stock Market Forecasting: A Critique, Review, and Research Agenda
title_fullStr	Application of Artificial Intelligence in Stock Market Forecasting: A Critique, Review, and Research Agenda
title_full_unstemmed	Application of Artificial Intelligence in Stock Market Forecasting: A Critique, Review, and Research Agenda
title_sort	application of artificial intelligence in stock market forecasting: a critique, review, and research agenda
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/1d24f554e2584bd4a02e7a6d29f9dd19
work_keys_str_mv	AT ritikachopra applicationofartificialintelligenceinstockmarketforecastingacritiquereviewandresearchagenda AT gagandeepsharma applicationofartificialintelligenceinstockmarketforecastingacritiquereviewandresearchagenda
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Application of Artificial Intelligence in Stock Market Forecasting: A Critique, Review, and Research Agenda

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