Updating traditional trade direction algorithms with liquidity motivation

William J. Bertin, Bond University
David Michayluk
Laurie Prather, Bond University

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© Copyright William Bertin, David Michayluk & Laurie Prather, 2004

Abstract

Trade-direction algorithms play an important role in traditional studies of market microstructure and in understanding the market for immediacy. This paper examines the underlying definition of trade origination and proposes a new liquidity motivation (LM) method to classify individual trades using orders. This LM model represents a unique alternative to the traditional algorithms used in most microstructure research. Using the NYSE TORQ database, LM trade classifications are compared with traditional methods for classifying trade direction. We document systematic biases resulting from the conventional algorithms and provide an alternative liquidity-based classification method that captures the actual behavior of market participants.