Trade classification accuracy for the BIST

Aktas, Osman Ulas; Kryzanowski, Lawrence

doi:10.1016/j.intfin.2014.08.003

Cited by 18 publications

(9 citation statements)

References 38 publications

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“…We apply Lee and Ready’s (1991) algorithm to classify trades as buys or sells and then calculate the total number of buys and sells per trading day for each stock. A large literature demonstrates the high success rates of the Lee–Ready algorithm in comparison with the tick, quote, at-the-quote and Ellis et al’s (2000) algorithms (Lee and Radhakrishna, 2000; Chakrabarty et al , 2007; Asquith et al , 2010; Aktas and Kryzanowski, 2014). As we are interested in the probability of informed trading around both the AD and ED of each acquisition announcement, we examine the following five windows for each acquirer: 59 days pre-announcement or [AD − 60: AD − 2], three days centered on the announcement date [AD − 1: AD + 1], the period between AD and ED or [AD + 2: ED − 2] and 59 days after the ED or [ED + 2: ED + 60].…”

Section: Sample and Datamentioning

confidence: 99%

Informed trading around biotech M&As

Kryzanowski

Tran²

2018

SEF

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Purpose This paper aims to test the extent to which downward bias due to a floating-point exception in probability of informed trading (PIN) estimates obtained using the Easley, Hvidkjaer and O’Hara (EHO; 2002) method is remedied using the Yan and Zhang (YZ; 2012) method. The paper also aims to test the sample-size sensitivity of EHO PIN and identify PIN determinants for acquirers and targets in the biotech sector. Design/methodology/approach EHO and YZ PIN performances are compared for US biotech acquirers and targets around their mergers and acquisition (M&A) announcements. The sampling method of Kryzanowski and Lazrak (2007) is used to assess sample-size sensitivity of announcement window EHO PIN estimates. Cross-sectional regressions are estimated to identify PIN determinants. Findings EHO and YZ PIN are not significantly different. EHO PIN exhibits significant sample-size sensitivity. Information leakage prior to M&A announcements is strongly affected by some firm characteristics. Significant determinants of PIN behavior around M&A announcements include insider and institutional holdings and research and development (R&D) expense. Research limitations/implications Findings imply that PIN partially reflects the activities of insiders and other informed investors about takeover intentions. Subsequent research can examine PIN behavior around pre-announcement rumors for M&As in the same or other industries and for potential targets that are peers of the M&A targets. Originality/value This paper contributes to the ongoing debate in the empirical finance literature on whether PIN measures informed trading by examining its behavior and the importance of some methodological issues associated with its use in examining market behavior around M&A announcements.

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Section: Sample and Datamentioning

confidence: 99%

Informed trading around biotech M&As

Kryzanowski

Tran²

2018

SEF

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“…The literature on the accuracy of algorithms that classify trades as buyer or seller initiated is inconclusive. However, all classification algorithms are shown to create some bias in BIST (Aktas and Kryzanowski 2014). Our results presented in the next section, are therefore robust to any biases that might arise due to improper selection of trade classification algorithms.…”

Section: Sample Formationmentioning

confidence: 57%

Informed trading, order flow shocks and the cross section of expected returns in Borsa Istanbul

Tiniç

Altay-Salih

2019

Applied Economics

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This paper examines the relationship between information asymmetry and stock returns in Borsa Istanbul. For all stocks that are traded in Borsa Istanbul between March 2005 and April 2017, we estimate the probability of informed trading (PIN) to proxy for information asymmetry. Firm-level cross-sectional regressions indicate a statistically insignificant relationship between PIN estimates and future returns. Moreover, univariate and multivariate portfolio analyses assert that investors that hold stocks that have high information asymmetry do not obtain significant future returns. Consequently, our results suggest that information asymmetry proxied by PIN is a firm-specific risk and can be eliminated with portfolio diversification. Findings are robust to different factorizations in estimating PIN and free of any bias due to trade classification algorithms, boundary solutions, floating-point exceptions and symmetric order flow shocks. KEYWORDS Information asymmetry; market microstructure; borsa istanbul; asset pricing JEL CLASSIFICATION G11; G12

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“…The empirical analysis in this study shows that the overall classification accuracy is 76.87% and the daily classification accuracy ranges from 68.98% to 83.76% in the Bitcoin market. According to previous research, the classification success rate of the tick rule ranges from 72.2% (Theissen, 2001) to 92.15% (Aktas & Kryzanowski, 2014) on the U.S. and non-U.S. stock markets. Of the research cited in this work, the study by Carrion and Kolay (2020) presents the similar fast trading environment by using high-frequency NASDAQ data stamped to seconds.…”

Section: Discussionmentioning

confidence: 99%

“…An examination of classification algorithms on data from the Taiwan Stock Exchange by Lu and Wei (2009) revealed an overall success rate of 74.18% for the tick rule. 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%.…”

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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Trade classification accuracy for the BIST

Cited by 18 publications

References 38 publications

Informed trading around biotech M&As

Informed trading around biotech M&As

Informed trading, order flow shocks and the cross section of expected returns in Borsa Istanbul

The Accuracy of the Tick Rule in the Bitcoin Market

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