Credit Agricole Analysis: Forecasting FX Market Trends

Monday, 1 April 2024, 20:07

Credit Agricole's analysis delves into the historical FX market trends during past Federal Reserve easing cycles and US recessions, providing a forecast template for the next 6-12 months. Anticipating a Fed easing cycle starting in summer 2024 and a mild US recession from Q4 2024, the analysis explores the implications for the USD, offering valuable insights for currency traders. The historical data suggests a mixed impact on the USD, emphasizing the complexity and resilience of the currency in certain scenarios.
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Credit Agricole Analysis: Forecasting FX Market Trends

Credit Agricole Analysis: Historical Insights for FX Market Forecasting

Credit Agricole examines 50 years of US economic history to forecast FX market trends for the next 6-12 months. The analysis focuses on the anticipated Federal Reserve easing cycle in summer 2024 and the potential mild US recession from Q4 2024.

Key Points:

  • Fed Easing and US Recession: The analysis predicts a starting Fed easing cycle in summer 2024 coinciding with a mild US recession from Q4 2024.
  • Historical FX Performance Template: By studying past Fed easing cycles and US recessions, Credit Agricole creates a template for forecasting 2024 FX market behavior.
  • Impact on the USD: The Fed's easing cycle is expected to impact the USD negatively, while a US recession might have a positive effect.
  • USD Rally in No Landing Scenarios: Instances where a Fed cycle didn't lead to a landing for the US economy saw a short-term USD rally.
  • Key Risk: A significant risk to consider is the potential momentum for the USD post-Fed easing cycle.

Credit Agricole's analysis offers valuable insights into forecasting FX market trends amid upcoming economic conditions. Investors and traders should prepare for intricate dynamics affecting the USD.


This article was prepared using information from open sources in accordance with the principles of Ethical Policy. The editorial team is not responsible for absolute accuracy, as it relies on data from the sources referenced.


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