# A random forest based approach for predicting spreads in the primary catastrophe bond market

arXiv: https://arxiv.org/abs/2001.10393 · HTML: https://mlsanity.com/paper/2001.10393/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2020.

## Built on by (most cited first, 3 of 3)

- [Churn modeling of life insurance policies via statistical and machine learning methods -- Analysis of important features](https://mlsanity.com/paper/2202.09182/) (2022)
- [Unveiling Nonlinear Dynamics in Catastrophe Bond Pricing: A Machine Learning Perspective](https://mlsanity.com/paper/2405.00697/) (2024)
- [Machine learning models for predicting catastrophe bond coupons using climate data](https://mlsanity.com/paper/2512.22660/) (2025)

## Abstract (arXiv)

We introduce a random forest approach to enable spreads' prediction in the primary catastrophe bond market. We investigate whether all information provided to investors in the offering circular prior to a new issuance is equally important in predicting its spread. The whole population of non-life catastrophe bonds issued from December 2009 to May 2018 is used. The random forest shows an impressive predictive power on unseen primary catastrophe bond data explaining 93% of the total variability. For comparison, linear regression, our benchmark model, has inferior predictive performance explaining only 47% of the total variability. All details provided in the offering circular are predictive of spread but in a varying degree. The stability of the results is studied. The usage of random forest can speed up investment decisions in the catastrophe bond industry.