Quarterly PIN estimates
Description
Estimation of model parameters and probability of informed trading for quarterly data.
Usage
1 2 
Arguments
numbuys 
numeric vector of daily buys 
numsells 
numeric vector of daily sells 
dates 
see Details 
lower 
numeric lower bounds for optimization, must have length of 5 
upper 
numeric upper bounds for optimization, must have length of 5 
confint 
logical Compute confidence intervals for PIN?
Defaults to 
ci_control 
list see 
Details
Wrapper around pin_est
function and therefore inherits its settings for optimization.
Data is split into quarters with the quarter
function from lubridate package.
According to the help page of this function dates
argument must be
a datetime object of class POSIXct, POSIXlt, Date, chron, yearmon, yearqtr, zoo, zooreg,
timeDate, xts, its, ti, jul, timeSeries, fts or anything else that can be converted with as.POSIXlt.
nlminb
function in the stats package is used for maximization.
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
Value
A list of lists. The length of the outer list equals the number of available quarters in the data. Naming scheme for the outer list is 'Year.QuarterNumber', where QuarterNumber equals an integer from 1 to 4. The inner list is structured as follows:
 Results
Matrix containing the parameter estimates as well as their estimated standard errors, tvalues and pvalues.
 ll
Value of likelihood function returned by
nlminb
 pin
Value(s) of the estimated probability of informed trading
 conv
Convergence code for nlminb optimization
 message
Convergence message returned by the nlminb optimizer
 iterations
Number of iterations until convergence of nlminb optimizer
 init_vals
Vector of initial values
 confint
If
confint = TRUE
; confidence interval for the probability of informed trading
References
Easley, David et al. (2002)
Is Information Risk a Determinant of Asset Returns?
The Journal of Finance, Volume 57, Number 5, pp. 2185  2221
\Sexpr[results=rd,stage=build]{tools:::Rd_expr_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
\Sexpr[results=rd,stage=build]{tools:::Rd_expr_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
\Sexpr[results=rd,stage=build]{tools:::Rd_expr_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
\Sexpr[results=rd,stage=build]{tools:::Rd_expr_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
\Sexpr[results=rd,stage=build]{tools:::Rd_expr_doi("10.1080/14697688.2015.1023336")}
Grolemund, Garett and Wickham, Hadley (2011)
Dates and Times Made Easy with lubridate
Journal of Statistical Software, Volume 40, Issue 3, pp. 1  25
\Sexpr[results=rd,stage=build]{tools:::Rd_expr_doi("10.18637/jss.v040.i03")}
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
\Sexpr[results=rd,stage=build]{tools:::Rd_expr_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
\Sexpr[results=rd,stage=build]{tools:::Rd_expr_doi("10.1016/j.jbankfin.2011.08.003")}
See Also
nlminb
,
initial_vals
pin_est
pin_est_core
quarter
year
Examples
1 2 3 4 5 6 7 8 9 10  # Loading one year of simulated daily buys and sells
data('BSfrequent2015')
# Quarterly estimates for model parameters and the probability of informed trading
# Rownames of 'BSfrequent2015' equal the business days in 2015.
qpin2015 < qpin(numbuys = BSfrequent2015[,"Buys"],
numsells = BSfrequent2015[,"Sells"],
dates = as.Date(rownames(BSfrequent2015), format = "%Y%m%d"))
