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The Journal of Financial Data Science

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Classification Methods for Market Making in Auction Markets

Nikolaj Normann Holm, Mansoor Hussain and Murat Kulahci
The Journal of Financial Data Science Fall 2021, jfds.2021.1.076; DOI: https://doi.org/10.3905/jfds.2021.1.076
Nikolaj Normann Holm
is a PhD fellow in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark in Kgs. Lyngby, Denmark
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Mansoor Hussain
is a senior analyst in the Department of Product Development at Jyske Bank A/S in Kgs. Lyngby, Denmark
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Murat Kulahci
is an associate professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark in Kgs. Lyngby, Denmark. He is also a professor in the Department of Business Administration, Technology and Social Sciences at Luleå University of Technology in Luleå, Sweden
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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.

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The Journal of Financial Data Science: 4 (2)
The Journal of Financial Data Science
Vol. 4, Issue 2
Spring 2022
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Classification Methods for Market Making in Auction Markets
Nikolaj Normann Holm, Mansoor Hussain, Murat Kulahci
The Journal of Financial Data Science Sep 2021, jfds.2021.1.076; DOI: 10.3905/jfds.2021.1.076

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Classification Methods for Market Making in Auction Markets
Nikolaj Normann Holm, Mansoor Hussain, Murat Kulahci
The Journal of Financial Data Science Sep 2021, jfds.2021.1.076; DOI: 10.3905/jfds.2021.1.076
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  • Article
    • Abstract
    • PREDICTING AUCTION OUTCOME
    • THE PROFIT MAXIMIZING AGENT
    • DISCUSSION
    • CONCLUSION
    • ACKNOWLEDGMENTS
    • APPENDIX
    • ENDNOTES
    • REFERENCES
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