  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.
