Estimates the probability of informed trading (PIN) for daily buys and sells trading data for arbitrary number of trading days.
pin_est(numbuys = NULL, numsells = NULL, nlminb_control = list(),
confint = FALSE, ci_control = list(), posterior = TRUE)numeric: vector of daily buys
numeric: vector of daily sells
list: Control list for nlminb
logical: Compute confidence intervals for PIN?
Defaults to FALSE
list: see pin_est_core
logical: Should posterior probabilities for conditions of trading days be computed?
A list with the following components:
Matrix containing the parameter estimates as well as their estimated standard errors, t-values and p-values.
Value of likelihood function returned by nlminb
Estimated probability of informed trading
Convergence code for nlminb optimization
Convergence message returned by the nlminb optimizer
Number of iterations until convergence of nlminb optimizer
Vector of initial values
If confint = TRUE; confidence interval for the probability of informed trading
User-friendly wrapper around workhorse function pin_est_core.
nlminb function in the stats package is used for maximization.
In the literature, at least data for 60 trading days is recommended to ensure convergence of optimization.
No information about the trading days' dates is needed.
Vectors for numbuys and numsells need to have same length.
Calculation of confidence interval for the probability of informed trading is disabled by default.
For more details see pin_est_core or pin_confint.
Easley, David et al. (2002) Is Information Risk a Determinant of Asset Returns? The Journal of Finance, Volume 57, Number 5, pp. 2185 - 2221 10.1111/1540-6261.00493
Easley, David et al. (1996) Liquidity, Information, and Infrequently Traded Stocks The Journal of Finance, Volume 51, Number 4, pp. 1405 - 1436 10.1111/j.1540-6261.1996.tb04074.x
Easley, David et al. (2010) Factoring Information into Returns Journal of Financial and Quantitative Analysis, Volume 45, Issue 2, pp. 293 - 309 10.1017/S0022109010000074
Ersan, Oguz and Alici, Asli (2016) An unbiased computation methodology for estimating the probability of informed trading (PIN) Journal of International Financial Markets, Institutions and Money, Volume 43, pp. 74 - 94 10.1016/j.intfin.2016.04.001
Gan, Quan et al. (2015) A faster estimation method for the probability of informed trading using hierarchical agglomerative clustering Quantitative Finance, Volume 15, Issue 11, pp. 1805 - 1821 10.1080/14697688.2015.1023336
Lin, Hsiou-Wei William and Ke, Wen-Chyan (2011) A computing bias in estimating the probability of informed trading Journal of Financial Markets, Volume 14, Issue 4, pp. 625 - 640 10.1016/j.finmar.2011.03.001
Yan, Yuxing and Zhang, Shaojun (2012) An improved estimation method and empirical properties of the probability of informed trading Journal of Banking & Finance, Volume 36, Issue 2, pp. 454 - 467 10.1016/j.jbankfin.2011.08.003
# NOT RUN {
# Loading simulated data for frequently traded stock
data("BSfrequent")
# Optimization with HAC initial values and Lin-Ke likelihood factorization
pin_freq <- pin_est(numbuys = BSfrequent[,"Buys"],
numsells = BSfrequent[,"Sells"])
# }
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