Market Trading Behavior Simulation Driven by Large Language Models

doi:10.11871/jfdc.issn.2096-742X.2026.01.001

Abstract

Abstract:

[Objective] In markets, participants' decisions are reflected through market trading. Understanding market trading is crucial for grasping the operational mechanisms of markets. This paper aims to assess whether Large Language Models (LLMs) can replicate bounded rationality in human market trading decisions and to explore the potential and limitations of LLMs in market trading simulation. [Methods] Behavioral economics theories, such as framing effect, endowment effect, and mental accounting, highlight how cognitive biases, emotional factors, and external information influence human market trading decisions. These theories form the basis for understanding bounded rationality in human decision-making. Building on these classic theories, this paper designs and implements four types of experiments: a questionnaire simulation, a fair price simulation, a voucher exchange simulation, and a product trading simulation. This study evaluates the human-like consistency of LLMs by analyzing the alignment between LLMs' responses and human decisions in the questionnaire experiments and their trading decisions in the exchange experiments. [Results] The experimental results show that LLMs align closely with humans in the questionnaire experiments. Factors such as persona, phrasing of questions, and the sequence of event descriptions significantly influence LLMs' decisions. However, LLMs differ from humans in pricing decisions and value assessments and fail to reproduce key behavioral patterns implied by mental accounting and the endowment effect. [Conclusions] In conclusion, while LLMs can simulate human market trading decisions in specific contexts, they still face limitations in fully replicating bounded rationality in human decision-making.

Key words: market trading, Large Language Models (LLMs), behavioral economics, bounded rationality, simulation capability

WANG Cheng,ZENG Shirong,WANG Chuwen,JIANG Changjun. Market Trading Behavior Simulation Driven by Large Language Models[J]. Frontiers of Data and Computing, 2026, 8(1): 2-13, https://cstr.cn/32002.14.jfdc.CN10-1649/TP.2026.01.001.

Figures/Tables 7

Fig.1

Fig.2

Table 1

Matching rates of LLMs in the framing effect questionnaire experiment, with the best result under overall matching rate bolded"

LLMs设置		$M$ /%	$M c 1$ /%	$M c 2$ /%	$M c 3$ /%	$M c 4$ /%
有人设	glm-4-9b	72.73	100	66.67	100	50
	internlm3-8b	54.55	50	66.67	50	50
	Llama-3-8b	72.73	100	66.67	50	75
	Qwen2.5-7B	63.64	50	66.67	50	75
	Qwen2.5-14B	54.55	50	66.67	100	25
无人设	glm-4-9b	54.55	100	33.33	100	25
	internlm3-8b	72.73	50	100	100	50
	Llama-3-8b	72.73	50	100	100	50
	Qwen2.5-7B	54.55	50	33.33	50	75
	Qwen2.5-14B	63.64	50	66.67	100	50

Table 1

Table 2

Table 3

Table 4

Fig.3

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LLMs	交易轮次	实际交易量	预期交易量	实际市场价格/$	预期市场价格/$
glm-4-9b	1	11	11	4.25^-	3.75⁺
	2	13	13	4.75^-	4.75^-
	3	7	6	4.75^-	4.75
internlm3-8b	1	12	11	4.75	4.75⁺
	2	13	13	4.75⁺	4.75⁺
	3	10	10	4.75⁺	4.75⁺
Llama-3-8b	1	10	9	4.25⁺	4.25
	2	10	11	5.75⁺	4.75
	3	11	12	3.75⁺	3.75
Qwen2.5-7B	1	11	11	4.75	4.75^-
	2	13	14	4.75⁺	4.75⁺
	3	9	9	4.75⁺	5.25
Qwen2.5-14B	1	10	9	4.75	4.75⁺
	2	10	10	4.25^-	4.25^-
	3	11	11	3.75⁺	3.75⁺