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Machine Learning Treasury Yields

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  • Zura Kakushadze
  • Willie Yu

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.

Suggested Citation

  • Zura Kakushadze & Willie Yu, 2020. "Machine Learning Treasury Yields," Bulletin of Applied Economics, Risk Market Journals, vol. 7(1), pages 1-65.
    • Handle: RePEc:rmk:rmkbae:v:7:y:2020:i:1:p:1-65
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    References listed on IDEAS

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    More about this item

    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.;
    All these keywords.

    JEL classification:

    • G00 - Financial Economics - - General - - - General
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors

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