{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T12:24:53Z","timestamp":1761740693818,"version":"build-2065373602"},"reference-count":23,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,11,5]],"date-time":"2024-11-05T00:00:00Z","timestamp":1730764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>It is well-believed that most trading activities tend to herd. Herding is an important topic in finance. It implies a violation of efficient markets and hence, suggests possibly predictable trading profits. However, it is hard to test such a hypothesis using aggregated data (as in the literature). In this paper, we obtain a proprietary data set that contains detailed trading information, and as a result, for the first time it allows us to validate this hypothesis. The data set contains all trades transacted in 2019 by all the brokers\/dealers across all locations in Taiwan of all the equities (stocks, warrants, and ETFs). Given such data, in this paper, we use swarm intelligence to identify such herding behavior. In particular, we use two versions of swarm intelligence\u2014Boids and PSO (particle swarm optimization)\u2014to study the herding behavior. Our results indicate weak swarm among brokers\/dealers.<\/jats:p>","DOI":"10.3390\/info15110707","type":"journal-article","created":{"date-parts":[[2024,11,5]],"date-time":"2024-11-05T06:30:48Z","timestamp":1730788248000},"page":"707","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Is the Taiwan Stock Market (Swarm) Intelligent?"],"prefix":"10.3390","volume":"15","author":[{"given":"Ren-Raw","family":"Chen","sequence":"first","affiliation":[{"name":"Gabelli School of Business, Fordham University, 113 W 60th Street, New York, NY 10023, USA"},{"name":"College of Management, Chang Gung University, 259, Wenhua 1st Rd., Guishan Dist., Taoyuan City 33302, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"101740","DOI":"10.1016\/j.ribaf.2022.101740","article-title":"Analysis of Herding Behavior in Individual Investor Portfolios using Machine Learning Algorithms","volume":"62","author":"Mavruk","year":"2022","journal-title":"Res. 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Financ."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/11\/707\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:26:33Z","timestamp":1760113593000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/11\/707"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,5]]},"references-count":23,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,11]]}},"alternative-id":["info15110707"],"URL":"https:\/\/doi.org\/10.3390\/info15110707","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2024,11,5]]}}}