Trade classification algorithms for electronic communications network trades

Chakrabarty, Bidisha; Li, Bingguang; Nguyen, Vanthuan; Ness, Robert A. Van

doi:10.1016/j.jbankfin.2007.03.003

Cited by 85 publications

(59 citation statements)

References 11 publications

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“…It is clear that the quote rule has a higher success rate unless mid quote trades are counted as errors, in which case four papers find the tick rule to be superior. In fact, the papers by EMO, Theissen, and Chakrabarty find similar performances across all the algorithms; for instance Chakrabarty et al (2007) these signing methods, the tick rule is particularly appealing because it avoids the size, cost, and matching problems inherent in modern quote tick data.…”

Section: Relevant Literaturementioning

confidence: 96%

“…Chakrabarty et al (2007) find superior performance for a modified EMO rule for ECNs (INET and ARCAex) on NASDAQ stocks while Lu and Wei (2009) recommend a modified LR rule for the electronic order-matching Taiwan Stock Exchange. Both studies involve the tick rule for a portion of their data.…”

Section: Relevant Literaturementioning

confidence: 99%

“…For example, Chakrabarty et al (2007) show the biases in estimating effective spreads and the price impact of trades for both LR and EMO. Petersen et al (2003) finds effective spreads overstate execution costs by 17% and recommend using EMO with contemporaneous quotes over LR.…”

Section: Relevant Literaturementioning

confidence: 99%

See 2 more Smart Citations

Tick test accuracy in foreign exchange ECN markets

Omrane

Welch

2016

Research in International Business and Finance

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Recent changes in quoting activity have increased the allure of the Tick test because the quote rule and its variants require matching asynchronous trade and quote records. We find Tick test accuracy of forex USD/Euro trades in 2005/6 is 66%. The accuracy falls to 60% for zero tick trades (half the sample), with a disturbing asymmetry between buyer (61%) and seller (70%) initiated trades. We also observe extreme asymmetry for quote changes, where buyer accuracy is 96% (29%) for up (down) quote changes, respectively.JEL classification: G10, F31.

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Section: Relevant Literaturementioning

confidence: 96%

Section: Relevant Literaturementioning

confidence: 99%

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Tick test accuracy in foreign exchange ECN markets

Omrane

Welch

2016

Research in International Business and Finance

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“…Let a and b be the Lagrangian multipliers for the equality constraints in (5). The augmented Lagrangian function can be expressed as follows:…”

Section: Consensus Psvmmentioning

confidence: 99%

“…The classification problems have practical applications in many areas of life, such as pattern recognition, regression forecasting, data processing, protein classification problem, meteorology, etc., [5,6,8,26]. There are many methods for solving the classification problems, such as decision trees, neural networks, clustering algorithm, expectation-maximization (EM), support vector machine (SVM), etc., [3,12].…”

Section: Introductionmentioning

confidence: 99%

Consensus Proximal Support Vector Machine for Classification Problems with Sparse Solutions

Bai

Shen

2014

J. Oper. Res. Soc. China

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Classification problem is the central problem in machine learning. Support vector machines (SVMs) are supervised learning models with associated learning algorithms and are used for classification in machine learning. In this paper, we establish two consensus proximal support vector machines (PSVMs) models, based on methods for binary classification. The first one is to separate the objective functions into individual convex functions by using the number of the sample points of the training set. The constraints contain two types of the equations with global variables and local variables corresponding to the consensus points and sample points, respectively. To get more sparse solutions, the second one is l 1 -l 2 consensus PSVMs in which the objective function contains an ' 1 -norm term and an ' 2 -norm term which is responsible for the good classification performance while ' 1 -norm term plays an important role in finding the sparse solutions. Two consensus PSVMs are solved by the alternating direction method of multipliers. Furthermore, they are implemented by the real-world data taken from the University of California, Irvine Machine Learning Repository (UCI Repository) and are compared with the existed models such as ' 1 -PSVM, ' p -PSVM, GEPSVM, PSVM, and SVM-light. Numerical results show that our models outperform others with the classification accuracy and the sparse solutions.

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Flow toxicity of high‐frequency trading and its impact on price volatility: Evidence from the KOSPI 200 futures market

Kang

Kwon

Kim

2019

Journal of Futures Markets

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We examine the relations among high frequency trading, flow toxicity, and short-term volatility during both normal and stressful times. Using transaction data from the KOSPI 200 futures market, we find that the volume-synchronized probability of informed trading (VPIN) well measures flow toxicity in that it strongly predicts short-term volatility. We further show that high frequency trading is negatively related to VPIN and short-term volatility in normal times, but turns to be positively related in stressful times. Finally, we advocate to use bulk volume classification (BVC) by presenting evidence that the initiator identified by BVC trades at more favorable prices than the true trade initiator.

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Trade classification algorithms for electronic communications network trades

Cited by 85 publications

References 11 publications

Tick test accuracy in foreign exchange ECN markets

Tick test accuracy in foreign exchange ECN markets

Consensus Proximal Support Vector Machine for Classification Problems with Sparse Solutions

Flow toxicity of high‐frequency trading and its impact on price volatility: Evidence from the KOSPI 200 futures market

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