AI and Exchange Rate Predictability

Amin Izadyar

Papers from arXiv.org

Abstract: 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.

Date: 2026-08
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2608.00761

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