Machine Learning Treasury Yields

Zura Kakushadze and Willie Yu

Bulletin of Applied Economics, 2020, vol. 7, issue 1, 1-65

Abstract: 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; out-of-sample; in-sample; weight; factor; exposure; source code; principal component; correlation; forecasting; interest rate; stability; level; slope; steepness; curvature; fixed income; term structure; yield curve. (search for similar items in EconPapers)
JEL-codes: G00 G10 G11 G12 G23 (search for similar items in EconPapers)
Date: 2020
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