LLM Agents Do Not Replicate Human Market Traders: Evidence From Experimental Finance
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914087193018368 |
|---|---|
| author | Henning, Thomas Ojha, Siddhartha M. Spoon, Ross Han, Jiatong Camerer, Colin F. |
| author_facet | Henning, Thomas Ojha, Siddhartha M. Spoon, Ross Han, Jiatong Camerer, Colin F. |
| contents | This paper explores how Large Language Models (LLMs) behave in a classic experimental finance paradigm widely known for eliciting bubbles and crashes in human participants. We adapt an established trading design, where traders buy and sell a risky asset with a known fundamental value, and introduce several LLM-based agents, both in single-model markets (all traders are instances of the same LLM) and in mixed-model "battle royale" settings (multiple LLMs competing in the same market). Our findings reveal that LLMs generally exhibit a "textbook-rational" approach, pricing the asset near its fundamental value, and show only a muted tendency toward bubble formation. Further analyses indicate that LLM-based agents display less trading strategy variance in contrast to humans. Taken together, these results highlight the risk of relying on LLM-only data to replicate human-driven market phenomena, as key behavioral features, such as large emergent bubbles, were not robustly reproduced. While LLMs clearly possess the capacity for strategic decision-making, their relative consistency and rationality suggest that they do not accurately mimic human market dynamics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_15800 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LLM Agents Do Not Replicate Human Market Traders: Evidence From Experimental Finance Henning, Thomas Ojha, Siddhartha M. Spoon, Ross Han, Jiatong Camerer, Colin F. Trading and Market Microstructure This paper explores how Large Language Models (LLMs) behave in a classic experimental finance paradigm widely known for eliciting bubbles and crashes in human participants. We adapt an established trading design, where traders buy and sell a risky asset with a known fundamental value, and introduce several LLM-based agents, both in single-model markets (all traders are instances of the same LLM) and in mixed-model "battle royale" settings (multiple LLMs competing in the same market). Our findings reveal that LLMs generally exhibit a "textbook-rational" approach, pricing the asset near its fundamental value, and show only a muted tendency toward bubble formation. Further analyses indicate that LLM-based agents display less trading strategy variance in contrast to humans. Taken together, these results highlight the risk of relying on LLM-only data to replicate human-driven market phenomena, as key behavioral features, such as large emergent bubbles, were not robustly reproduced. While LLMs clearly possess the capacity for strategic decision-making, their relative consistency and rationality suggest that they do not accurately mimic human market dynamics. |
| title | LLM Agents Do Not Replicate Human Market Traders: Evidence From Experimental Finance |
| topic | Trading and Market Microstructure |
| url | https://arxiv.org/abs/2502.15800 |