Machine learning and trade direction classification: insights from the corporate bond market
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 da...
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| Veröffentlicht in: | Review of quantitative finance and accounting 2024-07, Vol.63 (1), p.1-36 |
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| container_title | Review of quantitative finance and accounting |
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| creator | Fedenia, Mark Ronen, Tavy Nam, Seunghan |
| description | 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. |
| doi_str_mv | 10.1007/s11156-024-01252-w |
| format | Article |
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| issn | 0924-865X 1573-7179 |
| language | eng |
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| subjects | Accounting/Auditing Bond markets Classification Corporate bonds Corporate Finance Econometrics Economics and Finance Finance Futures market Institutional investments Liquidity Machine learning Operations Research/Decision Theory Original Research Transparency |
| title | Machine learning and trade direction classification: insights from the corporate bond market |
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