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Yield curve trading strategies exploiting sentiment data

Author

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  • Audrino, Francesco
  • Serwart, Jan

Abstract

This paper builds upon previous research findings that show macro sentiment data-augmented models are better at predicting the yield curve. We extend the dynamic Nelson–Siegel model with macro sentiment data from either Twitter or RavenPack. Vector autogressive (VAR) models and Markov-switching VAR models are used to predict changes in the shape of the yield curve. We build bond butterfly trading strategies that exploit our yield curve shape change predictions. We find that the economic returns from our trading strategies based upon models exploiting macro sentiment data do not statistically significantly differ from those which do not rely on it.

Suggested Citation

  • Audrino, Francesco & Serwart, Jan, 2024. "Yield curve trading strategies exploiting sentiment data," The North American Journal of Economics and Finance, Elsevier, vol. 74(C).
    • Handle: RePEc:eee:ecofin:v:74:y:2024:i:c:s1062940824001517
    DOI: 10.1016/j.najef.2024.102226
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    More about this item

    Keywords

    Bond butterflies; Yield curve; Sentiment data;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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