A random forest based approach for predicting spreads in the primary catastrophe bond market
This work addresses the need for faster investment decisions in the catastrophe bond industry, but it is incremental as it applies an existing machine learning method to a specific financial domain.
The authors tackled the problem of predicting spreads in the primary catastrophe bond market using a random forest approach, achieving a predictive power that explains 93% of total variability, compared to 47% for linear regression.
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.