Biased echoes: Large language models reinforce investment biases and increase portfolio risks of private investors
Journal
PLOS One
ISSN-Digital
1932-6203
Type
journal article
Date Issued
2025-06-27
Author(s)
Abstract
Large language models are increasingly used by private investors seeking financial advice. The current paper examines the potential of these models to perpetuate investment biases and affect the economic security of individuals at scale. We provide a systematic assessment of how large language models used for investment advice shape the portfolio risks of private investors. We offer a comprehensive model of large language model investment advice risk, examining five key dimensions of portfolio risks (geographical cluster risk, sector cluster risk, trend chasing risk, active investment allocation risk, and total expense risk). We demonstrate across four studies that large language models used for investment advice induce increased portfolio risks across all five risk dimensions, and that a range of debiasing interventions only partially mitigate these risks. Our findings show that large language models exhibit similar “cognitive” biases as human investors, reinforcing existing investment biases inherent in their training data. These findings have important implications for private investors, policymakers, artificial intelligence developers, financial institutions, and the responsible development of large language models in the financial sector.
Language
English
Keywords
Finance
Medical risk factors
Language
Artificial intelligence
Financial markets
Geography
Factorial design
Human learning
HSG Classification
contribution to scientific community
Refereed
Yes
Publisher
Public Library of Science (PLoS)
Volume
20
Number
6
Subject(s)
Division(s)
File(s)![Thumbnail Image]()
Name
journal.pone.0325459.pdf
Type
Main Article
Size
1.84 MB
Format
Adobe PDF
Checksum (MD5)
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