Accurately forecasting daily exchange rate returns represents a longstanding challenge in international finance, as the exchange rate returns are driven by a multitude of correlated market factors and exhibit high-frequency fluctuations. This paper proposes EXFormer, a novel Transformer-based architecture specifically designed for forecasting the daily exchange rate returns. We introduce a multi-scale trend-aware self-attention mechanism that employs parallel convolutional branches with differing receptive fields to align observations on the basis of local slopes, preserving long-range dependencies while remaining sensitive to regime shifts. A dynamic variable selector assigns time-varying importance weights to 28 exogenous covariates related to exchange rate returns, providing pre-hoc interpretability. An embedded squeeze-and-excitation block recalibrates channel responses to emphasize informative features and depress noise in the forecasting. Using the daily data for EUR/USD, USD/JPY, and GBP/USD, we conduct out-of-sample evaluations across five different sliding windows. EXFormer consistently outperforms the random walk and other baselines, improving directional accuracy by a statistically significant margin of up to 8.5--22.8%. In nearly one year of trading backtests, the model converts these gains into cumulative returns of 18%, 25%, and 18% for the three pairs, with Sharpe ratios exceeding 1.8. When conservative transaction costs and slippage are accounted for, EXFormer retains cumulative returns of 7%, 19%, and 9%, while other baselines achieve negative. The robustness checks further confirm the model's superiority under high-volatility and bear-market regimes. EXFormer furnishes both economically valuable forecasts and transparent, time-varying insights into the drivers of exchange rate dynamics for international investors, corporations, and central bank practitioners.

翻译：准确预测每日汇率收益率是国际金融领域长期存在的挑战，因为汇率收益率受众多相关市场因素驱动且呈现高频波动特征。本文提出EXFormer，一种专为预测每日汇率收益率而设计的新型Transformer架构。我们引入了一种多尺度趋势感知自注意力机制，该机制采用具有不同感受野的并行卷积分支，基于局部斜率对齐观测值，在保持长程依赖关系的同时对机制转换保持敏感。动态变量选择器为28个与汇率收益率相关的外生协变量分配时变重要性权重，提供事前可解释性。内嵌的挤压-激励模块通过重新校准通道响应来强调信息特征并抑制预测中的噪声。使用欧元/美元、美元/日元和英镑/美元的日度数据，我们在五个不同的滑动窗口上进行了样本外评估。EXFormer始终优于随机游走模型及其他基线模型，将方向预测准确率以最高8.5%至22.8%的统计显著幅度提升。在近一年的交易回测中，该模型将上述优势转化为三组货币对累计18%、25%和18%的收益率，夏普比率均超过1.8。当计入保守的交易成本和滑点时，EXFormer仍保持7%、19%和9%的累计收益率，而其他基线模型则呈现负收益。稳健性检验进一步证实了该模型在高波动和熊市机制下的优越性。EXFormer不仅为国际投资者、企业和央行从业者提供了具有经济价值的预测，还以透明且时变的方式揭示了汇率动态的驱动因素。