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Evaluating Trade Side Classification Algorithms Using Intraday Data from the Warsaw Stock Exchange

Olbrys, Joanna; Mursztyn, Michał

Abstract:
According to the literature, to measure both market liquidity and dimensions of market liquidity based on intraday data, it is essential to recognize the side initiating a transaction. Although the Warsaw Stock Exchange (WSE) is an order-driven market with an electronic order book, information of the order book database is not publicly available. Trade side classification algorithms enable us to assign the side that initiates a transaction and to distinguish between the so-called buyer- and seller-initiated trades. The aim of this paper is to evaluate several trade side classification procedures using high frequency intraday data for the WSE. The whole sample covers the period from January 3, 2005 to December 30, 2016, and it includes the Global Financial Crisis. Selected trade side classification algorithms are implemented, tested and compared with each other. Moreover, the robustness analysis of empirical results is provided. The empirical experiments show that the Lee and Ready (1991) algorithm performs better than other procedures on the WSE.

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Verlagsausgabe §
DOI: 10.5445/KSP/1000085951/20
Veröffentlicht am 11.12.2019
Coverbild
Zugehörige Institution(en) am KIT Institut für Informationswirtschaft und Marketing (IISM)
Publikationstyp Zeitschriftenaufsatz
Jahr 2018
Sprache Englisch
Identifikator ISSN: 2363-9881
KITopen-ID: 1000100804
Erschienen in Archives of Data Science, Series A (Online First)
Band 4
Heft 1
Seiten A20, 19 S. online
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