Can machines learn to reliably predict auction outcomes in financial markets? The authors study this question using classification methods from machine learning and auction data from the request-for-quote protocol used in many multi-dealer-to-client markets. Their answer is affirmative. The highest performance is achieved using gradient-boosted decision trees coupled with preprocessing tools to handle class imbalance. Competition level, client identity, and bid–ask quotes are shown to be the most important features. To illustrate the usefulness of these findings, the authors create a profit-maximizing agent to suggest price quotes. Results show more aggressive behavior compared to human dealers.