Machine Learning and the Random Walk Puzzle: Forecasting the CAD/USD Exchange Rate with Expanding Window Evaluation and SHAP Interpretability
A new study published on arXiv explores the effectiveness of various machine learning models in forecasting the USD/CAD exchange rate against the random walk benchmark. Researchers found that while most machine learning models showed only marginal improvements, linear regression was the only model to statistically outperform the naive random walk. The study utilized daily data from the Bank of Canada, resampled into monthly observations, and employed an expanding-window framework for evaluation. SHAP analysis was used to interpret the best-performing model, revealing that short-term lags and recent rolling means were the dominant predictors, aligning with the near-random-walk nature of exchange rates. AI