Melola Discovery

← Melola Discovery

Open access Jurnal SINTA 1

Machine Learning vs. Human Investors: Analyzing Adaptive Herding Behavior in U.S. Stocks vs. Shariah-Compliant Stocks in Malaysia and Indonesia

Gadjah Mada International Journal of Business (GamaIJB) · Vol. 27 No. 3 (2025) · pp. 297–319

DOI
10.22146/gamaijb.110602
ISSN
2338-7238, 1411-1128
Published
2025-09-02
Publisher
Master in Management, Faculty of Economics and Business, Universitas Gadjah Mada
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

This study examined the effectiveness of machine learning models in capturing adaptive herding behavior in the U.S., Malaysia, and Indonesia. Utilizing data from January 2010 to December 2023, the study incorporates market sentiment (Thomson Reuters MarketPsych indices), news sentiment (Bloomberg sentiment analysis), and investor happiness measures (Hedonometer). The methodology employs both static and adaptive herding analyses using the CSAD approach, enhanced by real-time sentiment analysis and various machine learning models, including single- and multi-layer neural networks. The results indicate significant differences in herding behavior across the three markets, with machine learning models demonstrating superior performance in capturing herding behavior and faster normalization after major macroeconomic events than traditional methods. These findings highlight the potential of machine learning models to challenge the static assumptions of the efficient market hypothesis and provide insights for designing better trading algorithms by considering the impact of market sentiment, news sentiment, and investor happiness.

Keywords

Collaboration network of the authors

2 authors · 1 co-authorship link · 1 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Binus University
  • University of Malaya
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/gamaijb/oai, first harvested 28 Okt 2026, last seen 28 Okt 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Machine Learning vs. Human Investors: Analyzing Adaptive Herding Behavior in U.S. Stocks vs. Shariah-Compliant Stocks in Malaysia and Indonesia</dc:title>
	<dc:creator>Candra, Sevenpri</dc:creator>
	<dc:creator>Kok Loang, Ooi</dc:creator>
	<dc:subject xml:lang="en-US">Business; Financial; Technology</dc:subject>
	<dc:subject xml:lang="en-US">Machine Learning, Adaptive Herding, Market Sentiment, Financial Stability, Stock Returns</dc:subject>
	<dc:subject xml:lang="en-US">G02, G11, G14, G15</dc:subject>
	<dc:description xml:lang="en-US">This study examined the effectiveness of machine learning models in capturing adaptive herding behavior in the U.S., Malaysia, and Indonesia. Utilizing data from January 2010 to December 2023, the study incorporates market sentiment (Thomson Reuters MarketPsych indices), news sentiment (Bloomberg sentiment analysis), and investor happiness measures (Hedonometer). The methodology employs both static and adaptive herding analyses using the CSAD approach, enhanced by real-time sentiment analysis and various machine learning models, including single- and multi-layer neural networks. The results indicate significant differences in herding behavior across the three markets, with machine learning models demonstrating superior performance in capturing herding behavior and faster normalization after major macroeconomic events than traditional methods. These findings highlight the potential of machine learning models to challenge the static assumptions of the efficient market hypothesis and provide insights for designing better trading algorithms by considering the impact of market sentiment, news sentiment, and investor happiness.</dc:description>
	<dc:publisher xml:lang="en-US">Master in Management, Faculty of Economics and Business, Universitas Gadjah Mada</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2025-09-02</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/gamaijb/article/view/110602</dc:identifier>
	<dc:identifier>10.22146/gamaijb.110602</dc:identifier>
	<dc:source xml:lang="en-US">Gadjah Mada International Journal of Business; Vol 27, No 3 (2025): September-December; 297-319</dc:source>
	<dc:source>2338-7238</dc:source>
	<dc:source>1411-1128</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/gamaijb/article/view/110602/42378</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 Gadjah Mada International Journal of Business</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>