EXFormer: A Multi-Scale Trend-Aware Transformer with Dynamic Variable Selection for Foreign Exchange Returns Prediction
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摘要
We propose EXFormer, a Transformer-based model that integrates multi-scale trend-aware self-attention, multi-branch convolutions and a dynamic variable selector to forecast 1-day-ahead FX returns and provide pre-hoc interpretability; EXFormer outperforms random walk and multiple deep-learning/econometric baselines in MSFE and directional accuracy, and its signals produce economically meaningful backtest profits (e.g., cumulative returns ~18–26% and Sharpe >1.8 in frictionless tests; positive net returns remain after conservative transaction costs) while the dynamic selector reveals time-varying drivers such as commodity indices and long-term yields [page::0].
