Classification Methods for Market Making in Auction Markets

Abstract

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.

TOPICS: Big data/machine learning, exchanges/markets/clearinghouses, performance measurement, behavioral finance in markets

Key Findings

• ▪ We propose a machine learning–based approach for determining auction outcomes by exploring the use of classification algorithms for outcome predictions and show that gradient-boosted decision trees obtain the best performance on an industrial data set.

• ▪ We uncover bid–ask normalized spread levels and competition level as the most important features and evaluate their influence on predictions through Shapley value estimation.

• ▪ We demonstrate the usefulness of our approach by creating a profit-maximizing agent using a classifier for win probability predictions. Our agent’s behavior is aggressive compared to human dealers.