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Corrigendum: Bond Risk Premiums with Machine Learning
[Bond risk premiums with machine learning]

Author

Listed:
  • Daniele Bianchi
  • Matthias Büchner
  • Tobias Hoogteijling
  • Andrea Tamoni

Abstract

In this note we revisit the empirical results in Bianchi, Büchner, and Tamoni (2020) after correcting for using information not available at the time the forecast was made. Although we note a decrease in out-of-sample , the revised analysis confirms that bond excess return predictability from neural networks remains statistically and economically significant.

Suggested Citation

  • Daniele Bianchi & Matthias Büchner & Tobias Hoogteijling & Andrea Tamoni, 2021. "Corrigendum: Bond Risk Premiums with Machine Learning [Bond risk premiums with machine learning]," The Review of Financial Studies, Society for Financial Studies, vol. 34(2), pages 1090-1103.
    • Handle: RePEc:oup:rfinst:v:34:y:2021:i:2:p:1090-1103.
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    File URL: http://hdl.handle.net/10.1093/rfs/hhaa098
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    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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