ChatGPT如何解读历史股票收益？LLM股票收益预测中的外推与校准偏差
What Does ChatGPT Make of Historical Stock Returns? Extrapolation and Miscalibration in LLM Stock Return Forecasts
AI总结：
本文考察LLM对历史股票收益的预测，发现其存在过度外推和乐观偏差，但置信区间校准优于人类调查，且风险衡量能力更强。
摘要：
我们研究大型语言模型（LLM）如何解读历史股票收益，并将其预测与一个用于股票排名的众包平台的估计进行比较。尽管股票收益呈现短期反转，但LLM预测存在过度外推，与人类相似，对近期表现赋予过高权重。相对于历史和未来已实现收益，LLM预测显得乐观。当被要求给出80%置信区间预测时，LLM回答的校准优于调查证据，但对异常值持悲观态度，导致预测分布偏斜。研究结果表明，LLM在预测预期收益时会表现出常见的行为偏差，但在衡量风险方面比人类更擅长。
英文摘要：
We examine how large language models (LLMs) interpret historical stock returns and compare their forecasts with estimates from a crowd-sourced platform for ranking stocks. While stock returns exhibit short-term reversals, LLM forecasts over-extrapolate, placing excessive weight on recent performance similar to humans. LLM forecasts appear optimistic relative to historical and future realized returns. When prompted for 80% confidence interval predictions, LLM responses are better calibrated than survey evidence but are pessimistic about outliers, leading to skewed forecast distributions. The findings suggest LLMs manifest common behavioral biases when forecasting expected returns but are better at gauging risks than humans.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
