Machine Learning Treasury Yields

Zura Kakushadze and Willie Yu

Papers from arXiv.org

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.

Date: 2020-03
New Economics Papers: this item is included in nep-big, nep-cmp and nep-fmk
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Published in Bulletin of Applied Economics 7(1) (2020) 1-65

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