Application of Poisson Mixtures in the Estimation of Probability of Informed Trading

  • Emily Lin
  • Cheng-Few Lee
Reference work entry


This research first discusses the evolution of probability for informed trading in finance literature. Motivated by asymmetric effects, e.g., return and trading volume in up and down markets, this study modifies a mixture of the Poisson distribution model by different arrival rates of informe d buys and sells to measure the probability of informed trading proposed by Easley et al. (Journal of Finance 51:1405–1436, 1996).

By applying the expectation–maximization (EM) algorithm to estimate the parameters of the model, we derive a set of equations for maximum likelihood estimation, and these equations are encoded in a SAS Macro utilizing SAS/IML for implementation of the methodology.


Probability of informed trading (PINExpectation–maximization (EM) algorithm A mixture of Poisson distribution Asset-pricing returns Order imbalance Information asymmetry Bid–ask spreads Market microstructure Trade direction Errors in variables GARCH 



Special thanks to professors Lii-Yuh Leu, Ren-Her Wang, and Charles Chang for their helpful comments and consultation. All errors are our own.


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© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.St. John’s UniversityNew Taipei CityTaiwan
  2. 2.Department of Finance and EconomicsRutgers Business School, Rutgers, The State University of New JerseyPiscatawayUSA
  3. 3.Graduate Institute of FinanceNational Chiao Tung UniversityHsinchuTaiwan

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