AI 中文总结
本文利用ChatGPT、DeepSeek等生成式AI分析经济数据，解决汇率脱钩难题，构建的交易策略夏普比率超0.7，超额回报显著，且证实泰勒规则是汇率与基本面关联的关键机制。
AI 中文摘要
本文重新审视了由Meese和Rogoff（1983）首次提出的汇率脱钩难题，利用生成式人工智能（AI）基于经济基本面预测货币回报率。通过ChatGPT和DeepSeek，本文分析了主要货币对的经济数据发布的综合数据集，并衡量每种货币的基本面强度。这些AI驱动的基本面表现出显著的横截面预测能力。一个简单的交易策略——做多基本面强劲的货币、做空基本面疲软的货币——每年产生的夏普比率超过0.7。在控制传统货币因子后，该策略的超额回报仍然显著。为缓解前瞻偏差的担忧，本文开展了多项验证，确保可预测性源于AI的推理而非记忆。最后，本文探究了可预测性的潜在来源，发现泰勒规则框架（央行通常用于设定利率的框架）是连接汇率与经济基本面的关键机制。
英文摘要
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.
