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CTEs enable policymakers to correctly forecast 80% of crises with a 20% probability of incorrectly forecasting a crisis. These findings are based on a long\u2010run sample (1870\u20132011), and two broad post\u20101970 samples which together cover almost all known systemic banking crises. I show that the marked improvement in forecasting performance results from the combination of many classification trees into an ensemble, and the use of many predictors. Copyright \u00a9 2016 John Wiley &amp; Sons, Ltd.<\/jats:p>","DOI":"10.1002\/jae.2525","type":"journal-article","created":{"date-parts":[[2016,5,18]],"date-time":"2016-05-18T11:16:41Z","timestamp":1463570201000},"page":"359-378","source":"Crossref","is-referenced-by-count":36,"title":["Spotting the Danger Zone: Forecasting Financial Crises With Classification Tree Ensembles and Many Predictors"],"prefix":"10.1002","volume":"32","author":[{"given":"Felix","family":"Ward","sequence":"first","affiliation":[{"name":"Department of Macroeconomics and Econometrics University of Bonn  Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2016,5,18]]},"reference":[{"key":"e_1_2_8_2_1","unstructured":"AbbasSM BelhocineN El\u2010GanainyA HortonM.2013.A historical public debt database. 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