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Forecasting accuracy of machine learning and linear regression: evidence from the secondary CAT bond market

Author

Listed:
  • Tobias Götze

    (Braunschweig Institute of Technology)

  • Marc Gürtler

    (Braunschweig Institute of Technology)

  • Eileen Witowski

    (Braunschweig Institute of Technology)

Abstract

The main challenge in empirical asset pricing is forecasting the future value of assets traded in financial markets with a high level of accuracy. Because machine learning methods can model relationships between explanatory and dependent variables based on complex, non-linear, and/or non-parametric structures, it is not surprising that machine learning approaches have shown promising forecasting results and significantly outperform traditional regression methods. Corresponding results were achieved for CAT bond premia forecasts in the primary market. However, since secondary market data sets have a panel data structure, it is unclear whether the results of primary market studies can be applied to the secondary market. Against this background, this study aims to build the first out-of-sample forecasting model for CAT bond premia in the secondary market, comparing different modeling approaches. We apply random forest and neural networks as representatives of machine learning methods and linear regression based on a comprehensive data set of CAT bond issues and across various forecasting settings and show that random forest forecasts are significantly more precise. Because the lack of transparency of machine learning methods may limit their applicability, especially for institutional investors, we show ways to identify important variables in the context of random forest price forecasting.

Suggested Citation

  • Tobias Götze & Marc Gürtler & Eileen Witowski, 2023. "Forecasting accuracy of machine learning and linear regression: evidence from the secondary CAT bond market," Journal of Business Economics, Springer, vol. 93(9), pages 1629-1660, November.
  • Handle: RePEc:spr:jbecon:v:93:y:2023:i:9:d:10.1007_s11573-023-01138-8
    DOI: 10.1007/s11573-023-01138-8
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    References listed on IDEAS

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    1. Tobias Götze & Marc Gürtler & Eileen Witowski, 2020. "Improving CAT bond pricing models via machine learning," Journal of Asset Management, Palgrave Macmillan, vol. 21(5), pages 428-446, September.
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    Cited by:

    1. Wolfgang Breuer & Andreas Knetsch, 2023. "Recent trends in the digitalization of finance and accounting," Journal of Business Economics, Springer, vol. 93(9), pages 1451-1461, November.

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    More about this item

    Keywords

    Forecasting; Machine learning; Linear regression; CAT bond secondary market; Transparency;
    All these keywords.

    JEL classification:

    • 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
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies

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