Please use this identifier to cite or link to this item:
https://digital.lib.ueh.edu.vn/handle/UEH/70321
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Huan Huu Nguyen | - |
dc.contributor.other | Vu Minh Ngo | - |
dc.contributor.other | Thao Thi Phuong Le | - |
dc.contributor.other | Phuc Van Nguyen | - |
dc.date.accessioned | 2023-11-29T08:45:08Z | - |
dc.date.available | 2023-11-29T08:45:08Z | - |
dc.date.issued | 2023 | - |
dc.identifier.issn | 2405-8440 | - |
dc.identifier.uri | https://www.sciencedirect.com/science/article/pii/S2405844023024805 | - |
dc.identifier.uri | https://digital.lib.ueh.edu.vn/handle/UEH/70321 | - |
dc.description.abstract | This study uses experiments and surveys from 146 participants who participated in equity trading to explore the predictive power of the Big-five personality traits, social behaviours, along with self-attribution and demographic characteristics on trading performance. Interestingly, we found that investors who are more open and neurotic gain higher returns compared to the market benchmark. We also found that other social traits are associated with the effectiveness of stock trading, such as awareness of social and ethical virtues (fairness and politeness). Moreover, instead of using separate characteristics, this study employs machine learning to cluster these personal features to understand the interconnection between socioeconomic determinants and financial decisions. This study contributes new evidence to the existing literature that personalities could explain trading performance. | en |
dc.format | Portable Document Format (PDF) | - |
dc.language.iso | eng | - |
dc.publisher | Elsevier B.V. | - |
dc.relation.ispartof | Heliyon | - |
dc.relation.ispartofseries | Vol. 9, Issue 4 | - |
dc.rights | The Author(s) | - |
dc.subject | Investors behaviors | en |
dc.subject | Financial investment | en |
dc.subject | Big-five personality traits | en |
dc.subject | Market winners | en |
dc.title | Do investors’ personalities predict market winners? Experimental setting and machine learning analysis | en |
dc.type | Journal Article | en |
ueh.JournalRanking | Scopus | - |
item.fulltext | Only abstracts | - |
item.languageiso639-1 | en | - |
item.openairetype | Journal Article | - |
item.grantfulltext | none | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
Appears in Collections: | INTERNATIONAL PUBLICATIONS |
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