Jeff Bohn, Swiss Re
ABSTRACT: As risk, trading, strategy, and decision-support systems have become more deeply integrated into financial services firms’ workflows, predicting a collection of economic and market indicators becomes even more critical to support these systems than in the past. At the same time, the underlying processes that drive economies and markets have become increasingly dynamic given they are more likely to be subject to rapid successions of regime changes. Conventional curve-fitting frameworks that assume linear/log-linear, stable relationships continue to exhibit degraded predictive performance. Fortunately, we are finding innovations in machine learning algorithmic frameworks that lead to a collection of promising techniques defined as “boosted trees.” These boosting methods better capture underlying non-linear data relationships in ways that can materially improve predictive performance for economic and market indicators. I will describe some of the newer boosting methods and report compelling results for predicting inflation and interest rates with a subset of these methods.