用ChatGPT和DeepSeek看各国经济数据做外汇，年化Sharpe超0.7，还验证了泰勒规则。有意思的论文。
该论文重访1983年Meese-Rogoff提出的汇率脱节之谜，用ChatGPT和DeepSeek分析主要货币对的经济数据发布，构造AI基本面强度。该指标在截面预测上显著有效；做多强基本面货币、做空弱基本面货币的策略年化Sharpe比率超过0.7。控制传统汇率因子后超额收益仍显著，作者通过多重检验排除记忆带来的前视偏差。论文还发现央行常用的泰勒规则是汇率与基本面关联的关键机制。
AI and Exchange Rate Predictability
I revisit the exchange rate disconnect puzzle, first documented by Meese and Rogoff (1983), using generative artificial intelligence (AI) to forecast currency returns based on economic fundamentals. Using ChatGPT and DeepSeek, I analyze a comprehensive dataset of economic data releases for major currency pairs and measure the fundamental strength of each currency. These AI-powered fundamentals exhibit significant cross-sectional predictive power. A simple trading strategy that goes long currencies with strong fundamentals and short currencies with weak fundamentals generates a Sharpe ratio exceeding 0.7 per annum. The excess returns of this strategy remain significant after controlling for traditional currency factors. To mitigate concerns of look-ahead bias, I run multiple exercises to ensure that predictability stems from AI reasoning rather than memorization. Finally, I explore the potential sources of predictability and find evidence that the Taylor rule framework, generally used by central banks to set interest rates, is a key mechanism connecting exchange rates to economic fundamentals.