Integrating AI-driven Macroeconomic Forecasting with Exchange Rate Hedging: The Case of Japanese Yen

Mammadov, Avaz and Mammadli, Kanan and Szóka, Károly and Tóth, Balázs István (2026) Integrating AI-driven Macroeconomic Forecasting with Exchange Rate Hedging: The Case of Japanese Yen. In: FEJLŐDÉSI PÁLYÁK ÉS ÚJ TÖRÉSVONALAK A FENNTARTHATÓSÁGI ÁTMENET IDŐSZAKÁBAN : Nemzetközi tudományos konferencia a Magyar Tudomány Ünnepe alkalmából. Soproni Egyetem Kiadó, Sopron, pp. 421-434. ISBN 9789633345795

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Abstract

The Foreign Exchange market is the largest and most liquid market, and it is considered a highly volatile market, which poses a significant risk for investors, governments, and global companies. The use of macroeconomic indicators such as inflation, economic growth, and interest rates on exchange rate predictions is valuable, as stated in traditional economic models, Interest rate parity, and purchasing power parity. However, machine learning models demonstrated higher accuracy in predicting macroeconomic indicators by capturing non-linearity and rapid market shocks, which traditional models might miss. The primary goal of this study is to evaluate how effective AI and ML models are in predicting exchange rates through macroeconomic indicators, which have a significant impact on exchange rate movements. In this paper, the application of artificial intelligence has been used, specifically a long short-term memory neural network model, which makes predictions more accurate. For analysis, we will use USD/JPY due to the volatility of the yen in recent years. The empirical findings, derived from monthly data spanning 1996 to 2024, indicate that AI-enhanced models substantially exceed traditional econometric methods in predicting fluctuations in the USD/JPY exchange rate. The study also uses simulated hedging strategies, which lead to less exposure to changes in the exchange rate.

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