Machine Learning Treasury Yields

Bulletin of Applied Economics 7(1) (2020) 1-65

68 Pages Posted: 10 Jan 2020 Last revised: 5 Feb 2020

See all articles by Zura Kakushadze

Zura Kakushadze

Quantigic Solutions LLC; Free University of Tbilisi

Willie Yu

Duke-NUS Medical School - Centre for Computational Biology

Date Written: January 6, 2020


We give explicit algorithms and source code for extracting factors underlying Treasury yields using (unsupervised) machine learning (ML) techniques, such as nonnegative matrix factorization (NMF) and (statistically deterministic) clustering. NMF is a popular ML algorithm (used in computer vision, bioinformatics/computational biology, document classification, etc.), but is often misconstrued and misused. We discuss how to properly apply NMF to Treasury yields. We analyze the factors based on NMF and clustering and their interpretation. We discuss their implications for forecasting Treasury yields in the context of out-of-sample ML stability issues.

Keywords: non-negative matrix factorization, NMF, clustering, k-means, Treasury, yield, machine learning, maturity, time series, weight, factor, exposure, source code, principal component, correlation, forecasting, interest rate, stability, level, slope, curvature, fixed income, term structure, yield curve

JEL Classification: G00, G10, G11, G12, G23

Suggested Citation

Kakushadze, Zura and Yu, Willie, Machine Learning Treasury Yields (January 6, 2020). Bulletin of Applied Economics 7(1) (2020) 1-65, Available at SSRN: or

Zura Kakushadze (Contact Author)

Quantigic Solutions LLC ( email )

680 E Main St #543
Stamford, CT 06901
United States
6462210440 (Phone)
6467923264 (Fax)


Free University of Tbilisi ( email )

Business School and School of Physics
240, David Agmashenebeli Alley
Tbilisi, 0159

Willie Yu

Duke-NUS Medical School - Centre for Computational Biology ( email )

8 College Road
Singapore, 169857

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