Tick test accuracy in foreign exchange ECN markets

Omrane, Walid Ben; Welch, Robert L.

doi:10.1016/j.ribaf.2015.10.001

Cited by 5 publications

(2 citation statements)

References 55 publications

(75 reference statements)

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“…Aktas and Kryzanowski (2014) examined the trade classification accuracy of different classification algorithms using the data of component firms of the BIST-30 index and found that the classification success rate of the tick rule ranged from 84.86% to 92.15% in different subsamples. In addition, Omrane and Welch (2016) found that the classification success rate of the tick rule for 1.2 million trades on the foreign exchange electronic communication network market was about 68%. To the best of the authors' knowledge, none of the previous research has focused on the accuracy of trade classification algorithms for the cryptocurrency markets.…”

Section: Literature Reviewmentioning

confidence: 99%

The Accuracy of the Tick Rule in the Bitcoin Market

Zhai

2021

SAGE Open

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The tick rule is one of the most popular trade classification algorithms used when an order initiator in market data is not signed. Using 11.9 million trades of Bitcoin/USD on Bitstamp, this article tests the accuracy of the tick rule in the Bitcoin market. Evidence indicates that the overall success rate of the tick rule is 76.87%. It is also shown that the tick rule is inclined to fail in discerning trade intentions when there is a long period of time between trades. Furthermore, order imbalances computed using the tick rule lack sufficient accuracy in the Bitcoin market.

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Section: Literature Reviewmentioning

confidence: 99%

The Accuracy of the Tick Rule in the Bitcoin Market

Zhai

2021

SAGE Open

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“…More recently, Chakrabarty et al (2012) assess the performance of the Lee-Ready procedure by comparing its results to a sign provided by the exchange (i.e. not using quotes data) and find on 2005 data a misclassification by the Lee-Ready procedure of the order of magnitude of 20 to 30% (similar to Omrane & Welch (2016) on 2005 data as well). Please note these few references have been selected just as an illustration with various data (from 1996 to 2005), references in each of them should be checked for further details.…”

Section: Number Of Reported Transactionsmentioning

confidence: 99%

Reconstruction of Order Flows using Aggregated Data

Toke

2016

Mark. Microstructure Liq.

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In this work we investigate tick-by-tick data provided by the TRTH database for several stocks on three different exchanges (Paris -Euronext, London and Frankfurt -Deutsche Börse) and on a 5-year span. We use a simple algorithm that helps the synchronization of the trades and quotes data sources, providing enhancements to the basic procedure that, depending on the time period and the exchange, are shown to be significant. We show that the analysis of the performance of this algorithm turns out to be a a forensic tool assessing the quality of the aggregated database: we are able to track through the data some significant technical changes that occurred on the studied exchanges. We also illustrate the fact that the choices made when reconstructing order flows have consequences on the quantitative models that are calibrated afterwards on such data. Our study also provides elements on the trade signature, and we are able to give a more refined look at the standard Lee-Ready procedure, giving new elements on the way optimal lags should be chosen when using this method. The findings are in line with both financial reasoning and the analysis of an illustrative Poisson model of the order flow.

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