pin_est: Estimating PIN
In pinbasic: Fast and Stable Estimation of the Probability of Informed Trading (PIN)

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	pin_est(numbuys = NULL, numsells = NULL, nlminb_control = list(), confint = FALSE, ci_control = list(), posterior = TRUE)

`numbuys`	numeric: vector of daily buys
`numsells`	numeric: vector of daily sells
`nlminb_control`	list: Control list for `nlminb`
`confint`	logical: Compute confidence intervals for PIN? Defaults to `FALSE`
`ci_control`	list: see `pin_est_core`
`posterior`	logical: Should posterior probabilities for conditions of trading days be computed?

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.

A list with the following components:

Results: Matrix containing the parameter estimates as well as their estimated standard errors, t-values and p-values.
ll: Value of likelihood function returned by nlminb
pin: 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

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: 10.1111/1540-6261.00493

Easley, David et al. (1996)
Liquidity, Information, and Infrequently Traded Stocks
The Journal of Finance, Volume 51, Number 4, pp. 1405 - 1436
doi: 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
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, 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
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

# 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"])