Description Usage Arguments Details Value References See Also Examples
Estimates the probability of informed trading (PIN) for daily buys and sells trading data for arbitrary number of trading days.
1 2 
numbuys 
numeric: vector of daily buys 
numsells 
numeric: vector of daily sells 
confint 
logical: Compute confidence intervals for PIN?
Defaults to 
ci_control 
list: see 
Userfriendly 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
.
A list with the following components:
Matrix containing the parameter estimates as well as their estimated standard errors, tvalues and pvalues.
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
Easley, David et al. (2002)
Is Information Risk a Determinant of Asset Returns?
The Journal of Finance, Volume 57, Number 5, pp. 2185  2221
doi: 10.1111/15406261.00493
Easley, David et al. (1996)
Liquidity, Information, and Infrequently Traded Stocks
The Journal of Finance, Volume 51, Number 4, pp. 1405  1436
doi: 10.1111/j.15406261.1996.tb04074.x
Easley, David et al. (2010)
Factoring Information into Returns
Journal of Financial and Quantitative Analysis, Volume 45, Issue 2, pp. 293  309
doi: 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
doi: 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
doi: 10.1080/14697688.2015.1023336
Lin, HsiouWei William and Ke, WenChyan (2011)
A computing bias in estimating the probability of informed trading
Journal of Financial Markets, Volume 14, Issue 4, pp. 625  640
doi: 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
doi: 10.1016/j.jbankfin.2011.08.003
nlminb
,
initial_vals
pin_est_core
qpin
1 2 3 4 5 6 7 8  # Loading simulated data for frequently traded stock
data("BSfrequent")
# Optimization with HAC initial values and LinKe likelihood factorization
pin_freq < pin_est(numbuys = BSfrequent[,"Buys"],
numsells = BSfrequent[,"Sells"])

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