机器学习预测加元兑美元汇率，随机游走仍是强基准。
Machine Learning and the Random Walk Puzzle: Forecasting the CAD/USD Exchange Rate with Expanding Window Evaluation and SHAP Interpretability
- 用滚动窗口评估五种机器学习模型的月度汇率预测能力。
- 线性回归显著优于随机游走，其他模型仅微弱提升。
- SHAP分析显示短期滞后和近期均值是主要预测因素。
本研究检验机器学习（ML）模型能否在预测月度美元兑加元汇率时超越简单的随机游走基准。基于加拿大央行2017年1月至2026年5月的日数据，重采样为113个每月观测值，评估了五种模型：线性回归、随机森林、梯度提升、XGBoost和AdaBoost。所有模型均采用扩展窗口框架进行评估以保证严格的样本外完整性，并通过Diebold-Mariano（DM）检验比较预测准确率差异。结构断裂检测识别出四个显著断点，分别对应2018年美中贸易争端升级、2020年新冠疫情经济复苏、2022年加拿大央行加息周期顶峰及2024年降息周期启动。对表现最佳模型应用SHAP（Shapley Additive Explanations）解释其驱动因素。结果显示，随机游走模型仍为强劲基准；线性回归在统计上显著优于随机游走，DM统计量为3.0585，p值为0.0071；其余集成学习模型仅表现出微弱改进。随机森林在扩展窗口框架下达到最低平均绝对百分比误差（MAPE）1.17%，仅次于随机游走。SHAP分析确认短期滞后（如滞后1与滞后2）及近期滚动均值主导预测，符合汇率近似随机游走特性。
原文摘要 · Abstract (English)
This study examines whether machine learning (ML) models can outperform the naive random walk benchmark in forecasting the monthly USD/CAD exchange rate. Using daily data from the Bank of Canada spanning January 2017 to May 2026, resampled into 113 monthly observations, five ML models are evaluated: linear regression, random forest, gradient boosting, XGBoost, and AdaBoost. These models are benchmarked against the naive random walk model and exponential smoothing with Holt-Winters seasonality (ETS). All models are evaluated using an expanding-window framework to maintain strict out-of-sample integrity, and forecast-accuracy differences are assessed using the Diebold-Mariano (DM) test. Structural break detection identifies four significant breakpoints in the series, corresponding to the escalation of the US-China trade war in 2018, the COVID-19 economic recovery in 2020, the peak of the Bank of Canada rate-hiking cycle in 2022, and the start of the Bank of Canada rate-cutting cycle in 2024. SHAP, or Shapley Additive Explanations, analysis is applied to interpret the drivers of the best-performing ML model. The results show that the naive random walk model remains a formidable benchmark. Linear regression is the only model that statistically outperforms the naive random walk model, with a DM statistic of 3.0585 and a p value of 0.0071, whereas the ML ensemble models show only marginal differences. Random Forest with an expanding-window framework achieves the lowest MAPE of 1.17 percent among all models except the random walk. SHAP analysis confirms that short-term lags, particularly lag1 and lag2, and recent rolling means dominate predictions, consistent with the near-random-walk behavior of exchange rates.
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