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

Tobias Götze (), Marc Gürtler () and Eileen Witowski ()
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Tobias Götze: Braunschweig Institute of Technology
Marc Gürtler: Braunschweig Institute of Technology
Eileen Witowski: Braunschweig Institute of Technology

Journal of Business Economics, 2023, vol. 93, issue 9, No 6, 1629-1660

Abstract: 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.

Keywords: Forecasting; Machine learning; Linear regression; CAT bond secondary market; Transparency (search for similar items in EconPapers)
JEL-codes: C45 C53 C58 G12 G17 G22 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11573-023-01138-8

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