Predicting the Canadian Yield Curve Using Machine Learning Techniques

Ali Rayeni and Hosein Naderi ()
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Ali Rayeni: Department of Finance, Schulich School of Business, York University, Toronto, ON M3J 1P3, Canada
Hosein Naderi: Department of Finance, Schulich School of Business, York University, Toronto, ON M3J 1P3, Canada

IJFS, 2025, vol. 13, issue 3, 1-30

Abstract: This study applies machine learning methods to predict the Canadian yield curve using a comprehensive set of macroeconomic variables. Lagged values of the yield curve and a wide array of Canadian and international macroeconomic variables are utilized across various machine learning models. Hyperparameters are estimated to minimize mispricing across government bonds with different maturities. The Group Lasso algorithm outperforms the other models studied, followed by Lasso. In addition, the majority of the models outperform the Random Walk benchmark. The feature importance analysis reveals that oil prices, bond-related factors, labor market conditions, banks’ balance sheets, and manufacturing-related factors significantly drive yield curve predictions. This study is one of the few that uses such a broad array of macroeconomic variables to examine Canadian macro-level outcomes. It provides valuable insights for policymakers and market participants, with its feature importance analysis highlighting key drivers of the yield curve.

Keywords: yield curve; machine learning; financial forecasting; bond pricing (search for similar items in EconPapers)
JEL-codes: F2 F3 F41 F42 G1 G2 G3 (search for similar items in EconPapers)
Date: 2025
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