Stock Market Prediction Using Machine Learning Techniques: A Decade Survey on Methodologies, Recent Developments, and Future Directions

With the advent of technological marvels like global digitization, the prediction of the stock market has entered a technologically advanced era, revamping the old model of trading. With the ceaseless increase in market capitalization, stock trading has become a center of investment for many financi...

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Autores principales:	Nusrat Rouf, Majid Bashir Malik, Tasleem Arif, Sparsh Sharma, Saurabh Singh, Satyabrata Aich, Hee-Cheol Kim
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	generic review machine learning stock market prediction support vector machine Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/3925b8b8cad347ad8430557cac02ecdf
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spelling	oai:doaj.org-article:3925b8b8cad347ad8430557cac02ecdf2021-11-11T15:42:35ZStock Market Prediction Using Machine Learning Techniques: A Decade Survey on Methodologies, Recent Developments, and Future Directions10.3390/electronics102127172079-9292https://doaj.org/article/3925b8b8cad347ad8430557cac02ecdf2021-11-01T00:00:00Zhttps://www.mdpi.com/2079-9292/10/21/2717https://doaj.org/toc/2079-9292With the advent of technological marvels like global digitization, the prediction of the stock market has entered a technologically advanced era, revamping the old model of trading. With the ceaseless increase in market capitalization, stock trading has become a center of investment for many financial investors. Many analysts and researchers have developed tools and techniques that predict stock price movements and help investors in proper decision-making. Advanced trading models enable researchers to predict the market using non-traditional textual data from social platforms. The application of advanced machine learning approaches such as text data analytics and ensemble methods have greatly increased the prediction accuracies. Meanwhile, the analysis and prediction of stock markets continue to be one of the most challenging research areas due to dynamic, erratic, and chaotic data. This study explains the systematics of machine learning-based approaches for stock market prediction based on the deployment of a generic framework. Findings from the last decade (2011–2021) were critically analyzed, having been retrieved from online digital libraries and databases like ACM digital library and Scopus. Furthermore, an extensive comparative analysis was carried out to identify the direction of significance. The study would be helpful for emerging researchers to understand the basics and advancements of this emerging area, and thus carry-on further research in promising directions.Nusrat RoufMajid Bashir MalikTasleem ArifSparsh SharmaSaurabh SinghSatyabrata AichHee-Cheol KimMDPI AGarticlegeneric reviewmachine learningstock market predictionsupport vector machineElectronicsTK7800-8360ENElectronics, Vol 10, Iss 2717, p 2717 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	generic review machine learning stock market prediction support vector machine Electronics TK7800-8360
spellingShingle	generic review machine learning stock market prediction support vector machine Electronics TK7800-8360 Nusrat Rouf Majid Bashir Malik Tasleem Arif Sparsh Sharma Saurabh Singh Satyabrata Aich Hee-Cheol Kim Stock Market Prediction Using Machine Learning Techniques: A Decade Survey on Methodologies, Recent Developments, and Future Directions
description	With the advent of technological marvels like global digitization, the prediction of the stock market has entered a technologically advanced era, revamping the old model of trading. With the ceaseless increase in market capitalization, stock trading has become a center of investment for many financial investors. Many analysts and researchers have developed tools and techniques that predict stock price movements and help investors in proper decision-making. Advanced trading models enable researchers to predict the market using non-traditional textual data from social platforms. The application of advanced machine learning approaches such as text data analytics and ensemble methods have greatly increased the prediction accuracies. Meanwhile, the analysis and prediction of stock markets continue to be one of the most challenging research areas due to dynamic, erratic, and chaotic data. This study explains the systematics of machine learning-based approaches for stock market prediction based on the deployment of a generic framework. Findings from the last decade (2011–2021) were critically analyzed, having been retrieved from online digital libraries and databases like ACM digital library and Scopus. Furthermore, an extensive comparative analysis was carried out to identify the direction of significance. The study would be helpful for emerging researchers to understand the basics and advancements of this emerging area, and thus carry-on further research in promising directions.
format	article
author	Nusrat Rouf Majid Bashir Malik Tasleem Arif Sparsh Sharma Saurabh Singh Satyabrata Aich Hee-Cheol Kim
author_facet	Nusrat Rouf Majid Bashir Malik Tasleem Arif Sparsh Sharma Saurabh Singh Satyabrata Aich Hee-Cheol Kim
author_sort	Nusrat Rouf
title	Stock Market Prediction Using Machine Learning Techniques: A Decade Survey on Methodologies, Recent Developments, and Future Directions
title_short	Stock Market Prediction Using Machine Learning Techniques: A Decade Survey on Methodologies, Recent Developments, and Future Directions
title_full	Stock Market Prediction Using Machine Learning Techniques: A Decade Survey on Methodologies, Recent Developments, and Future Directions
title_fullStr	Stock Market Prediction Using Machine Learning Techniques: A Decade Survey on Methodologies, Recent Developments, and Future Directions
title_full_unstemmed	Stock Market Prediction Using Machine Learning Techniques: A Decade Survey on Methodologies, Recent Developments, and Future Directions
title_sort	stock market prediction using machine learning techniques: a decade survey on methodologies, recent developments, and future directions
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/3925b8b8cad347ad8430557cac02ecdf
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Stock Market Prediction Using Machine Learning Techniques: A Decade Survey on Methodologies, Recent Developments, and Future Directions

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