A clever insight from ChatGPT into the agent-based behavioral stock market model in “Heavy-tailed distributions of volume and price-change resulting from strategy coordination and decision noise”
Abstract
This paper presents insights from a large language model (ChatGPT-4o) into my earlier work, “Heavy-tailed distributions of volume and price-change resulting from strategy coordination and decision noise,” which models the stock market using a Gibbsian mixture model, and is applied to the GameStop short squeeze. Motivated by a challenge posed by Vernon Smith, this model captures endogenous coordination among agents and its effects on market tails, volatility, and volume. Surprisingly, ChatGPT highlighted the macro-scale U-shaped behavior of coordination dynamics — a feature I had underemphasized — offering a fresh interpretation of agent-based market regimes beyond traditional models like Black–Scholes. This paper is dedicated to Vernon Smith, whose challenge and intellectual generosity inspired this line of research.
References
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