A computing bias in estimating the probability of informed trading

Lin, Hong-Wen; Ke, Wen-Chyan

doi:10.1016/j.finmar.2011.03.001

Cited by 70 publications

(12 citation statements)

References 13 publications

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“…Both issues have been identified and analyzed in the literature, the former by William Lin and Ke (2011) and the latter by Yan and Zhang (2012). We therefore report convergence rates and check all model parameters for their distribution and occurrence of boundary solutions.…”

Section: Robustness Testsmentioning

confidence: 99%

A PIN per day shows what news convey: the intraday probability of informed trading

Pöppe

Aitken

Schiereck

et al. 2015

Rev Quant Finan Acc

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This paper develops a new intraday estimation procedure for the sequential microstructure trading model initially proposed by Easley et al. (Rev Financ Stud 10:805-835, 1997a). Using a full year of intraday trading data for the top 100 German stocks, we demonstrate how the new estimation procedure eliminates or significantly reduces the shortcomings of the original approach in recent, high-frequency trading environments. We slice a trading day in buckets of several minutes' length to obtain one estimate of the composite variable probability of informed trading (PIN) per day. This approach makes PIN applicable in short horizon event studies. Convergence rates are above 95 % even for the most liquid stocks and the model's underlying assumptions of independence for the arrival of traders and information events are fulfilled to a much higher degree than in the original approach. An empirical application in an event study type setting demonstrates how official announcements stipulated in German insider trading legislation significantly reduce information asymmetry upon public disclosure.

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Section: Robustness Testsmentioning

confidence: 99%

A PIN per day shows what news convey: the intraday probability of informed trading

Pöppe

Aitken

Schiereck

et al. 2015

Rev Quant Finan Acc

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show abstract

“…The likelihood factorization presented in Equation (1) (EKOP factorization) is prone to selection bias. Lin and Ke (2011) show that the feasible solution set for the non-linear optimization problem shrinks significantly as the daily number of buy and sell orders increases. The optimal value for the log-likelihood in Equation (2) becomes undefined, for large enough ðB t ; S t Þ whose factorials cannot be computed by mainstream computers (floating-point exception (FPE)).…”

Section: Floating-point Exception and Different Likelihood Factorizatmentioning

confidence: 99%

“…The first factorization (EHO factorization) is provided by Easley, Hvidkjaer, and O'Hara (2010). The other (LK factorization) is provided by Lin and Ke (2011). Both studies modify the factorization such that B t !…”

Section: Floating-point Exception and Different Likelihood Factorizatmentioning

confidence: 99%

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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“…Floating-Point Exception PIN estimates are prone to selection bias, especially for stocks for which the number of buy and sell orders are large 3 . Lin and Ke (2011) show that the increase in the number of buy and sell orders for a given stock, significantly shrinks the feasible solution set for the maximization of the log likelihood function in equation 2. To maximize the non-linear function (1), the optimization software introduces initial values for the parameters in Θ.…”

Section: Pin Modelmentioning

confidence: 99%

“…Easley et al (2010) indicate that for stocks with a large trading volume, it is not possible to estimate PIN due to floating-point-exception (FPE). Two different numerical factorizations are provided by Easley et al (2010) and Lin and Ke (2011) to overcome the bias created due to FPE.…”

Section: Introductionmentioning

confidence: 99%

InfoTrad: An R package for estimating the probability of informed trading

Çelik¹,

Tiniç²

2018

The R Journal

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The purpose of this paper is to introduce the R package InfoTrad for estimating the probability of informed trading (PIN) initially proposed by Easley et al. (1996). PIN is a popular information asymmetry measure that proxies the proportion of informed traders in the market. This study provides a short survey on alternative estimation techniques for the PIN. There are many problems documented in the existing literature in estimating PIN. InfoTrad package aims to address two problems. First, the sequential trading structure proposed by Easley et al. (1996) and later extended by Easley et al. (2002) is prone to sample selection bias for stocks with large trading volumes, due to floating point exception. This problem is solved by different factorizations provided by Easley et al. (2010) (EHO factorization) and Lin and Ke (2011) (LK factorization). Second, the estimates are prone to bias due to boundary solutions. A grid-search algorithm (YZ algorithm) is proposed by Yan and Zhang (2012) to overcome the bias introduced due to boundary estimates. In recent years, clustering algorithms have become popular due to their flexibility in quickly handling large data sets. Gan et al. (2015) propose an algorithm (GAN algorithm) to estimate PIN using hierarchical agglomerative clustering which is later extended by Ersan and Alici (2016) (EA algorithm). The package InfoTrad offers LK and EHO factorizations given an input matrix and initial parameter vector. In addition, these factorizations can be used to estimate PIN through YZ algorithm, GAN algorithm and EA algorithm.

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A computing bias in estimating the probability of informed trading

Cited by 70 publications

References 13 publications

A PIN per day shows what news convey: the intraday probability of informed trading

A PIN per day shows what news convey: the intraday probability of informed trading

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

InfoTrad: An R package for estimating the probability of informed trading

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