Machine Learning in the Corporate Bond Market and Beyond: A New Classifier

57 Pages Posted: 18 May 2021 Last revised: 9 Jun 2021

See all articles by Mark Fedenia

Mark Fedenia

University of Wisconsin - Madison - Department of Finance, Investment and Banking

Seunghan Nam

New York Institute of Technology

Tavy Ronen

Rutgers Business School -Newark and New Brunswick, Department of Finance & Economics

Date Written: June 8, 2021

Abstract

Trade signing algorithms that rely on quote data, tick data or both have been used extensively to assign a trade as either a buy or a sell. Leveraging the availability of a large panel of signed trade data in the corporate bond market, we explore machine learning methods to uncover a new trade signing model that improves upon standard trade classification methods in both the bond and equity markets. We show that both the trading and information environment at the time of the trade critically affect the accuracy of existing trade classification rules in general, and also illustrate the importance of optimizing the feature set in correctly classifying trade direction. Importantly, our approach and the Random Forest algorithm we propose can be used in markets both with and without pre-trade transparency.

Keywords: Machine Learning, Trade Direction Classifiers, Trade Signing, Corporate Bonds, Equity Market, Big Data

JEL Classification: G0

Suggested Citation

Fedenia, Mark A. and Nam, Seunghan and Ronen, Tavy, Machine Learning in the Corporate Bond Market and Beyond: A New Classifier (June 8, 2021). Available at SSRN: https://ssrn.com/abstract=3848068 or http://dx.doi.org/10.2139/ssrn.3848068

Mark A. Fedenia

University of Wisconsin - Madison - Department of Finance, Investment and Banking ( email )

975 University Avenue
Madison, WI 53706
United States
608-263-4502 (Phone)
608-265-3495 (Fax)

Seunghan Nam

New York Institute of Technology ( email )

NY

Tavy Ronen (Contact Author)

Rutgers Business School -Newark and New Brunswick, Department of Finance & Economics ( email )

1 Washington Park
Newark, NJ 07102
United States
973-353-5272 (Phone)

HOME PAGE: http://https://www.business.rutgers.edu/faculty/tavy-ronen

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