Skip to contents

Based on an extension of the algorithm in Cheng and Lai (2021) , generates sets of initial parameters to be used in the maximum likelihood estimation of AdjPIN model.


initials_adjpin_cl(data, restricted = list(), verbose = TRUE)



A dataframe with 2 variables: the first corresponds to buyer-initiated trades (buys), and the second corresponds to seller-initiated trades (sells).


A binary list that allows estimating restricted AdjPIN models by specifying which model parameters are assumed to be equal. It contains one or multiple of the following four elements {theta, mu, eps, d}. For instance, If theta is set to TRUE, then the probability of liquidity shock in no-information days, and in information days is assumed to be the same (\(\theta\)=\(\theta'\)). If any of the remaining rate elements {mu, eps, d} is set to TRUE, (say mu=TRUE), then the rate is assumed to be the same on the buy side, and on the sell side (\(\mu\)b=\(\mu\)s). If more than one element is set to TRUE, then the restrictions are combined. For instance, if the argument restricted is set to list(theta=TRUE, eps=TRUE, d=TRUE), then the restricted AdjPIN model is estimated, where \(\theta\)=\(\theta'\), \(\epsilon\)b=\(\epsilon\)s, and \(\Delta\)b=\(\Delta\)s. If the value of the argument restricted is the empty list, then all parameters of the model are assumed to be independent, and the unrestricted model is estimated. The default value is the empty list list().


a binary variable that determines whether information messages about the initial parameter sets, including the number of the initial parameter sets generated. No message is shown when verbose is set to FALSE. The default value is TRUE.


Returns a dataframe of numerical vectors of ten elements {\(\alpha\), \(\delta\), \(\theta\), \(\theta'\), \(\epsilon\)b, \(\epsilon\)s, \(\mu\)b, \(\mu\)s, \(\Delta\)b, \(\Delta\)s}.


The argument 'data' should be a numeric dataframe, and contain at least two variables. Only the first two variables will be considered: The first variable is assumed to correspond to the total number of buyer-initiated trades, while the second variable is assumed to correspond to the total number of seller-initiated trades. Each row or observation correspond to a trading day. NA values will be ignored.

The function implements an extension of the algorithm of Cheng and Lai (2021) . In their paper, the authors assume that the probability of liquidity shock is the same in no-information, and information days, i.e., \(\theta\)=\(\theta'\), and use a procedure similar to that of Yan and Zhang (2012) to generate 64 initial parameter sets. The function implements an extension of their algorithm, by relaxing the assumption of equality of liquidity shock probabilities, and generates thereby 256 initial parameter sets for the unrestricted AdjPIN model.


Cheng T, Lai H (2021). “Improvements in estimating the probability of informed trading models.” Quantitative Finance, 21(5), 771-796.

Yan Y, Zhang S (2012). “An improved estimation method and empirical properties of the probability of informed trading.” Journal of Banking and Finance, 36(2), 454--467. ISSN 03784266.


# There is a preloaded quarterly dataset called 'dailytrades' with 60
# observations. Each observation corresponds to a day and contains the
# total number of buyer-initiated trades ('B') and seller-initiated
# trades ('S') on that day. To know more, type ?dailytrades

xdata <- dailytrades

# The function adjpin(xdata, initialsets="CL") allows the user to directly
# estimate the AdjPIN model using the full set of initial parameter sets
# generated using the algorithm Cheng and Lai (2021)
# \donttest{
estimate.1 <- adjpin(xdata,  initialsets="CL", verbose = FALSE)
# }

# Obtaining the set of initial parameter sets using initials_adjpin_cl
# allows us to estimate the PIN model using a subset of these initial sets.

# Use initials_adjpin_cl() to generate 256 initial parameter sets using the
# algorithm of Cheng and Lai (2021).

initials_cl <- initials_adjpin_cl(xdata, verbose = FALSE)

# Use 20 randonly chosen initial sets from the dataframe 'initials_cl' in
# order to estimate the AdjPIN model using the function adjpin() with custom
# initial parameter sets

numberofsets <- nrow(initials_cl)
selectedsets <- initials_cl[sample(numberofsets, 20),]

estimate.2 <- adjpin(xdata, initialsets = selectedsets, verbose = FALSE)

# Compare the parameters and the pin values of both specifications
# \donttest{
comparison <- rbind(
c(estimate.1@parameters, adjpin = estimate.1@adjpin, psos = estimate.1@psos),
c(estimate.2@parameters, estimate.2@adjpin, estimate.2@psos))

rownames(comparison) <- c("all", "50")

#>       alpha     delta      theta    thetap   eps.b   eps.s    mu.b    mu.s
#> all 0.73334 0.1363624 0.06251172 0.6363624 337.167 335.981 599.125 871.186
#> 50  0.58334 0.1714266 0.40000480 0.7999966 337.190 336.065 913.118 871.102
#>         d.b   d.s    adjpin      psos
#> all 912.750 0.384 0.2950966 0.2791487
#> 50  598.818 0.172 0.3342391 0.2399395
# }