Calibrating Behavioral Parameters with Large Language Models
This provides a novel measurement framework for behavioral economics, addressing a key bottleneck in asset pricing models, though it is incremental in applying existing LLM technology to a specific domain.
The researchers tackled the problem of reliably measuring behavioral parameters like loss aversion and herding in asset pricing by using large language models (LLMs) as calibrated instruments, finding that calibration shifted parameters to match or exceed human benchmarks and generated realistic market patterns in simulations.
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.