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DEVELOPING A MACHINE LEARNING FORECASTING FRAMEWORK FOR EXCHANGE RATES IN THE CONTEXT OF CROSS-BORDER BUSINESS
Author(s)
Chen, Wei
Date Issued
January 1, 2025
Type
Article
Abstract
Traditionally, this matter was regarded as a largely technical undertaking; however, recent market disruptions have demonstrated that the issue extends far beyond purely technical considerations. This study addresses the subject from two interconnected perspectives. The first concern centers upon the capacity of different Machine Learning (ML) models to maintain performance during periods in which financial markets deviate from conventional behavioural patterns. The second concern focuses on the way professionals who depend upon such predictive outputs interpret, evaluate, and integrate these forecasts into routine operational decision-making. The empirical findings revealed that several ML approaches, particularly Long Short-Term Memory (LSTM) architectures and selected ensemble-based methods, adapted more consistently to abrupt market fluctuations than the econometric benchmark models employed within the study. Such resilience became especially apparent throughout the COVID-19 crisis, when exchange-rate dynamics departed substantially from the assumptions underpinning traditional forecasting frameworks. The interview findings produced a somewhat different perspective. Although most practitioners recognised the practical potential associated with ML-driven forecasting systems, their evaluations remained notably cautious. Collectively, these findings suggest that high predictive performance alone is insufficient to secure widespread organisational acceptance of ML applications within cross-border commercial activities. For these systems to become genuinely effective, they must integrate smoothly into existing organisational procedures and risk-management cultures while remaining comprehensible to end users. Consequently, effective forecasting frameworks should not only possess strong technical capability but must also remain interpretable, adaptable, and sufficiently resilient to withstand major structural transformations within financial markets.
Citation
International Journal of Ebusiness and Egovernment Studies, 17(3), 123-148, 2025