InfoTrad-package: Calculates the Probability of Informed Trading (PIN)
In InfoTrad: Calculates the Probability of Informed Trading (PIN)
Description
Author(s)
References
Description
Estimates the probability of informed trading (PIN) initially introduced by Easley et. al. (1996) <doi:10.1111/j.1540-6261.1996.tb04074.x> . Contribution of the package is that it uses likelihood factorizations of Easley et. al. (2010) <doi:10.1017/S0022109010000074> (EHO factorization) and Lin and Ke (2011) <doi:10.1016/j.finmar.2011.03.001> (LK factorization). Moreover, the package uses different estimation algorithms. Specifically, the grid-search algorithm proposed by Yan and Zhang (2012) <doi:10.1016/j.jbankfin.2011.08.003> , hierarchical agglomerative clustering approach proposed by Gan et. al. (2015) <doi:10.1080/14697688.2015.1023336> and later extended by Ersan and Alici (2016) <doi:10.1016/j.intfin.2016.04.001> .
Author(s)
Duygu Celik and Murat Tinic
Maintainer: Murat Tinic <tinic@bilkent.edu.tr>
References
D. Easley, N. M. Kiefer, M. O'Hara, and J.B. Paperman. Liquidity, information, and infrequently traded stocks. The Journal of Finance, 51(4):1405-1436, 1996
D. Easley, S. Hvidkjaer, and M. O'Hara. Is information risk a determinant of asset returns? The Journal of Finance, 57(5): 2185-2221, 2002.
D. Easley, S. Hvidkjaer, and M. O'Hara. Factoring information into returns. Journal of Financial and Quantitative Analysis, 45(2): 293-309, 2010.
Ersan, O., and Alici, A. An unbiased computation methodology for estimating the probability of informed trading (PIN). Journal of International Financial Markets, Institutions and Money, 43(1):74-94, 2016.
Q. Gan, W.C. Wei, and D. Johnstone. A faster estimation method for the probability of informed trading using hierarhical agglomerative clustering. Quantitative Finance, 15(11):1805-1821, 2015
H.W. W. Lin and W. C. Ke. A computing bias in estimating the probability of informed trading. Journal of Financial Markets, 14(4):625-640, 2011.
D. Mullner. fastcluster: Fast hierarchical, agglomerative clustering routines for r and python. Journal of Statistical Software, 53(9):1-18, 2013.
Y. Yan and S. Zhang. An improved estimation method and empirical properties of the probability of informed trading. Journal of Banking & Finance, 36(2):454-467, 2012.
InfoTrad documentation built on May 1, 2019, 10:24 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Author(s) References
Description
Estimates the probability of informed trading (PIN) initially introduced by Easley et. al. (1996) <doi:10.1111/j.1540-6261.1996.tb04074.x> . Contribution of the package is that it uses likelihood factorizations of Easley et. al. (2010) <doi:10.1017/S0022109010000074> (EHO factorization) and Lin and Ke (2011) <doi:10.1016/j.finmar.2011.03.001> (LK factorization). Moreover, the package uses different estimation algorithms. Specifically, the grid-search algorithm proposed by Yan and Zhang (2012) <doi:10.1016/j.jbankfin.2011.08.003> , hierarchical agglomerative clustering approach proposed by Gan et. al. (2015) <doi:10.1080/14697688.2015.1023336> and later extended by Ersan and Alici (2016) <doi:10.1016/j.intfin.2016.04.001> .
Author(s)
Duygu Celik and Murat Tinic
Maintainer: Murat Tinic <tinic@bilkent.edu.tr>
References
D. Easley, N. M. Kiefer, M. O'Hara, and J.B. Paperman. Liquidity, information, and infrequently traded stocks. The Journal of Finance, 51(4):1405-1436, 1996
D. Easley, S. Hvidkjaer, and M. O'Hara. Is information risk a determinant of asset returns? The Journal of Finance, 57(5): 2185-2221, 2002.
D. Easley, S. Hvidkjaer, and M. O'Hara. Factoring information into returns. Journal of Financial and Quantitative Analysis, 45(2): 293-309, 2010.
Ersan, O., and Alici, A. An unbiased computation methodology for estimating the probability of informed trading (PIN). Journal of International Financial Markets, Institutions and Money, 43(1):74-94, 2016.
Q. Gan, W.C. Wei, and D. Johnstone. A faster estimation method for the probability of informed trading using hierarhical agglomerative clustering. Quantitative Finance, 15(11):1805-1821, 2015
H.W. W. Lin and W. C. Ke. A computing bias in estimating the probability of informed trading. Journal of Financial Markets, 14(4):625-640, 2011.
D. Mullner. fastcluster: Fast hierarchical, agglomerative clustering routines for r and python. Journal of Statistical Software, 53(9):1-18, 2013.
Y. Yan and S. Zhang. An improved estimation method and empirical properties of the probability of informed trading. Journal of Banking & Finance, 36(2):454-467, 2012.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.