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Machine learning and trade direction classification: insights from the corporate bond market

Author

Listed:
  • Mark Fedenia

    (University of Wisconsin-Madison)

  • Tavy Ronen

    (Rutgers Business School)

  • Seunghan Nam

    (Independent Researcher)

Abstract

Leveraging the availability of a large panel of signed trade data in the corporate bond market, we explore how machine learning methods can be used to improve upon standard trade direction classification methods in markets in general, both with and without pre-trade transparency. Using the signed data set allows us to show how 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. These insights extend to the equity market.

Suggested Citation

  • Mark Fedenia & Tavy Ronen & Seunghan Nam, 2024. "Machine learning and trade direction classification: insights from the corporate bond market," Review of Quantitative Finance and Accounting, Springer, vol. 63(1), pages 1-36, July.
    • Handle: RePEc:kap:rqfnac:v:63:y:2024:i:1:d:10.1007_s11156-024-01252-w
    DOI: 10.1007/s11156-024-01252-w
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Machine learning; Trade direction classifiers; Trade signing; Corporate bonds; Equity market; Big data;
    All these keywords.

    JEL classification:

    • G0 - Financial Economics - - General

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