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Finance and Financial Management Commons

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Selected Works

Laurie Prather

Trade-direction algorithms

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Articles 1 - 2 of 2

Full-Text Articles in Finance and Financial Management

Updating Traditional Trade Direction Algorithms With Liquidity Motivation, William Bertin, David Michayluk, Laurie Prather May 2010

Updating Traditional Trade Direction Algorithms With Liquidity Motivation, William Bertin, David Michayluk, Laurie Prather

Laurie Prather

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.


Updating Traditional Trade Direction Algorithms With Liquidity Motivation, William J. Bertin, David Michayluk, Laurie Prather Nov 2009

Updating Traditional Trade Direction Algorithms With Liquidity Motivation, William J. Bertin, David Michayluk, Laurie Prather

Laurie Prather

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.