Calibrating Behavioral Parameters with Large Language Models

TL;DR

This paper introduces a framework using large language models as tools to measure and calibrate key behavioral parameters in asset pricing, revealing biases and aligning with empirical market phenomena.

Contribution

It presents a novel calibration method for behavioral biases using LLMs, enabling more accurate modeling of investor behavior in financial markets.

Findings

LLMs exhibit systematic rationality biases compared to humans.

Calibration significantly increases the magnitude of behavioral parameters.

Calibrated parameters produce asset price patterns consistent with empirical data.

Abstract

Behavioral parameters such as loss aversion, herding, and extrapolation are central to asset pricing models but remain difficult to measure reliably. We develop a framework that treats large language models (LLMs) as calibrated measurement instruments for behavioral parameters. Using four models and 24{,}000 agent--scenario pairs, we document systematic rationality bias in baseline LLM behavior, including attenuated loss aversion, weak herding, and near-zero disposition effects relative to human benchmarks. Profile-based calibration induces large, stable, and theoretically coherent shifts in several parameters, with calibrated loss aversion, herding, extrapolation, and anchoring reaching or exceeding benchmark magnitudes. To assess external validity, we embed calibrated parameters in an agent-based asset pricing model, where calibrated extrapolation generates short-horizon momentum and long-horizon reversal patterns consistent with empirical evidence. Our results establish measurement ranges, calibration functions, and explicit boundaries for eight canonical behavioral biases.