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Decision fusion for stock market prediction: A systematic review

Stock market prediction based on machine or deep learning is an essential topic in the financial community. Typically, models with different structures or initializations provide different forecasts of the same response variable. In such cases, better prediction is often achieved by combining foreca...

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Main Authors: Zhang, Cheng, Sjarif, Nilam N. A., Ibrahim, Roslina
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2022
Subjects:
T Technology (General)
Online Access:http://eprints.utm.my/104412/1/NilamNurAmir2022_DecisionFusionforStockMarketPrediction.pdf
http://eprints.utm.my/104412/
http://dx.doi.org/10.1109/ACCESS.2022.3195942
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spelling my.utm.1044122024-02-04T09:54:45Z http://eprints.utm.my/104412/ Decision fusion for stock market prediction: A systematic review Zhang, Cheng Sjarif, Nilam N. A. Ibrahim, Roslina T Technology (General) Stock market prediction based on machine or deep learning is an essential topic in the financial community. Typically, models with different structures or initializations provide different forecasts of the same response variable. In such cases, better prediction is often achieved by combining forecasts from multiple models rather than using a single model in isolation. This combination of forecasts from the base learners is known as decision fusion. Furthermore, although decision fusion is typical and essential for making the best possible use of multiple forecasts, few studies have systematically summarized the studies that apply this technique. Therefore, there is an urgent need for a literature review reflecting the application of decision fusion in this field. To this end, this study systematically reviewed research related to decision fusion for stock market prediction, focusing on the characteristics of base learners and decision fusion methods. Specifically, the research trend on this topic, which has shifted over the past two decades, is discussed. This review also presents future directions in applying decision fusion to stock market prediction, such as the fusion of forecasts with different data types, using new algorithms as base learners, and integrating sentiment analysis with decision fusion techniques. Institute of Electrical and Electronics Engineers Inc. 2022 Article PeerReviewed application/pdf en http://eprints.utm.my/104412/1/NilamNurAmir2022_DecisionFusionforStockMarketPrediction.pdf Zhang, Cheng and Sjarif, Nilam N. A. and Ibrahim, Roslina (2022) Decision fusion for stock market prediction: A systematic review. IEEE Access, 10 (NA). pp. 81364-81379. ISSN 2169-3536 http://dx.doi.org/10.1109/ACCESS.2022.3195942 DOI : 10.1109/ACCESS.2022.3195942
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Zhang, Cheng
Sjarif, Nilam N. A.
Ibrahim, Roslina
Decision fusion for stock market prediction: A systematic review
description Stock market prediction based on machine or deep learning is an essential topic in the financial community. Typically, models with different structures or initializations provide different forecasts of the same response variable. In such cases, better prediction is often achieved by combining forecasts from multiple models rather than using a single model in isolation. This combination of forecasts from the base learners is known as decision fusion. Furthermore, although decision fusion is typical and essential for making the best possible use of multiple forecasts, few studies have systematically summarized the studies that apply this technique. Therefore, there is an urgent need for a literature review reflecting the application of decision fusion in this field. To this end, this study systematically reviewed research related to decision fusion for stock market prediction, focusing on the characteristics of base learners and decision fusion methods. Specifically, the research trend on this topic, which has shifted over the past two decades, is discussed. This review also presents future directions in applying decision fusion to stock market prediction, such as the fusion of forecasts with different data types, using new algorithms as base learners, and integrating sentiment analysis with decision fusion techniques.
format Article
author Zhang, Cheng
Sjarif, Nilam N. A.
Ibrahim, Roslina
author_facet Zhang, Cheng
Sjarif, Nilam N. A.
Ibrahim, Roslina
author_sort Zhang, Cheng
title Decision fusion for stock market prediction: A systematic review
title_short Decision fusion for stock market prediction: A systematic review
title_full Decision fusion for stock market prediction: A systematic review
title_fullStr Decision fusion for stock market prediction: A systematic review
title_full_unstemmed Decision fusion for stock market prediction: A systematic review
title_sort decision fusion for stock market prediction: a systematic review
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2022
url http://eprints.utm.my/104412/1/NilamNurAmir2022_DecisionFusionforStockMarketPrediction.pdf
http://eprints.utm.my/104412/
http://dx.doi.org/10.1109/ACCESS.2022.3195942
_version_ 1792147734005809152
score 13.211869

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