A machine learning approach to classification for traders in financial markets. Issue 1 (28th June 2022)

• Record Type: Journal Article
• Authors: Wright, Isaac D.; Reimherr, Matthew; Liechty, John
• Is Part Of: Stat. Volume 11:Issue 1(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-06-28
• Subjects: clustering -- limit order books -- trader classification -- Wasserstein distances; Statistics -- Periodicals; 519.2
• DOI: 10.1002/sta4.465
• Languages: English
• ISSNs: 2049-1573

Abstract

Abstract : We introduce new machine learning methods for clustering traders who are actively trading in a modern electronic exchange which uses a matching engine to track aggregate and individual‐level limit order books. Each trader's individual limit order book is centered (with the current best bid and ask prices acting as a central reference), and the patterns in the individual limit order books are identified using a Wasserstein distance. The method is illustrated using simulated limit order book data and limit order book data from a stock exchange in Canada.