Machine Spirits: Speculation and Adaptation of LLM Agents in Asset Markets

TL;DR

This paper explores the diverse economic behaviors of LLMs in simulated financial markets, revealing their potential to cause instability and adapt strategies that impact market dynamics.

Contribution

It provides the first systematic investigation of LLM agent behaviors in financial markets, highlighting their capacity for speculation, adaptation, and market destabilization.

Findings

LLMs exhibit behaviors from stable to speculative bubbles.

Mixed markets with heterogeneous LLMs show high variability in outcomes.

Advanced LLMs adapt strategies that can increase market volatility.

Abstract

As Large Language Models (LLMs) become increasingly integrated into financial systems, understanding their behavioural properties is crucial. Do LLMs conform to the rational expectations paradigm, do they exhibit human-like "animal spirits", or do they instead manifest distinct "machine spirits"? We investigate these questions with a simulated financial market, exploring the behaviour of 15 LLMs spanning a range of sizes, capabilities, and providers. Our results show that LLMs exhibit a spectrum of economic behaviours, from stable coordination on the fundamental value to human-like speculative bubbles. These behaviours are generally inconsistent with the rational expectations hypothesis. We also consider an ecology of heterogeneous agents, a more realistic setting compared to markets with identical LLM agents. These mixed markets can produce outcomes which vary substantially across repeated simulations. Even the most advanced models fail to consistently stabilise the market, with price bubbles sometimes forming despite only a minority of agents naturally forming bubbles. Instead, advanced models in mixed markets adapt their forecasting strategies to the behaviour of other agents. This adaptation can allow them to successfully exploit less sophisticated counterparts and achieve higher profits, but can also contribute to increased market volatility. These findings suggest that the introduction of AI agents into financial markets fundamentally reshapes their ecology. In particular, heterogeneous populations of LLMs can generate endogenous instability, while individual-level adaptation may amplify, rather than mitigate, market volatility.