A Novel Exchange Rate Forecasting Paradigm Based on Multi-agent Collaboration and Multimodal Big Data-Driven Methods

The existing methodologies for forecasting exchange rates exhibit significant accuracy, systematicity, and efficiency deficiencies. Traditional exchange rate forecasting methods are limited by siloed data acquisition, cumbersome data processing, subjective feature engineering, and low efficiency model forecasting. To address these limitations, this paper proposes an exchange rate forecasting paradigm based on multimodal big data. The paradigm effectively addresses the challenges of non-standardized feature selection inherent in traditional forecasting methods by introducing flexible algorithmic mechanisms and dynamic feature selection strategies. On this basis, this paper proposes the FX-Agents framework, which enables efficient data acquisition and processing through agent collaboration driven by large language models (LLMs). Its flexible multi-agent module design ensures efficient and stable forecasting performance. Experimental results demonstrate that FX-Agents outperform traditional methods in forecasting accuracy and processing efficiency. The source code of our work is publicly available at https://github.com/Kon-Kwok/FX-Agent.