pin_est_core: Optimization in PIN models
In pinbasic: Fast and Stable Estimation of the Probability of Informed Trading (PIN)

Description Usage Arguments Details Value References See Also Examples

Core function for optimization routines

pin_est_core(numbuys = NULL, numsells = NULL,
  factorization = "Lin_Ke", init_vals = NULL, lower = rep(0, 5),
  upper = c(1, 1, rep(Inf, 3)), num_best_res = 1,
  only_converged = TRUE, nlminb_control = list(), confint = FALSE,
  ci_control = list(), posterior = TRUE)

`numbuys`	numeric: vector of daily buys
`numsells`	numeric: vector of daily sells
`factorization`	character: factorization of likelihood function: either 'EHO' or 'Lin_Ke', defaults to: 'Lin_Ke'
`init_vals`	numeric: matrix of initial values: either generated by `initial_vals` or user-defined matrix with five columns for which colnames must consist of: `alpha`, `delta`, `epsilon_b`, `epsilon_s`, `mu`
`lower`	numeric: lower bounds for optimization, must have length of 5
`upper`	numeric: upper bounds for optimization, must have length of 5
`num_best_res`	Number of optimization runs for which results should be returned, either numeric or 'all', only relevant if `init_vals` = 'Grid', defaults to 1
`only_converged`	logical: Return only results for which the likelihood converged? Defaults to `TRUE`
`nlminb_control`	list: Control list for `nlminb`
`confint`	logical: Compute confidence intervals for PIN? Defaults to `FALSE`
`ci_control`	list: see Details
`posterior`	logical: Should posterior probabilities for conditions of trading days be computed?

Grants the most control over optimization procedure. User-friendly wrappers for estimation with trading data of arbitrary length and quarterly data are implemented with pin_est and qpin, respectively. nlminb function in the stats package is used for maximization. Vectors for numbuys and numsells need to have same length.

Confidence intervals for the probability of informed trading are calculated via Monte-Carlo-Simulation if confint = TRUE. Settings of the confidence interval simulation can be specified via a named list for ci_control. Valid list names are n, seed, level and ncores which pass number of simulation runs (defaults to 10000), seed for RNG (defaults to seed = NULL), confidence level (defaults to 0.95) and number of cpu cores utilized (defaults to 1).

If num_best_res = 1, a list with following elements is returned:

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

If num_best_res > 1, a named list of lists is returned. Each component of the outer list is again a list structured as shown above. Naming scheme for the outer list is 'Best1',..., 'Bestnum_best_res'.

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

Revolution Analytics and Steve Weston (2015)
doParallel: Foreach Parallel Adaptor for the 'parallel' Package
R package version 1.0.10

Revolution Analytics and Steve Weston (2015)
foreach: Provides Foreach Looping Construct for R
R package version 1.4.3 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 qpin pin_confint

# Loading simulated data for frequently traded stock

data("BSfrequent")

# Generate several matrices of initial values utilizing all methods implemented

inits_grid <- initial_vals(numbuys = BSfrequent[,"Buys"],
                           numsells = BSfrequent[,"Sells"],
                           method = "Grid")

inits_hac <- initial_vals(numbuys = BSfrequent[,"Buys"],
                          numsells = BSfrequent[,"Sells"],
                          method = "HAC")

inits_hac_ref <- initial_vals(numbuys = BSfrequent[,"Buys"],
                              numsells = BSfrequent[,"Sells"],
                              method = "HAC_Ref")

# Optimization with different matrices of initial values

pin_core_grid <- pin_est_core(numbuys = BSfrequent[,"Buys"],
                              numsells = BSfrequent[,"Sells"],
                              factorization = "Lin_Ke", init_vals = inits_grid,
                              lower = rep(0,5), upper = c(1,1, rep(Inf,3)),
                              num_best_res = 5)

pin_core_hac <- pin_est_core(numbuys = BSfrequent[,"Buys"],
                             numsells = BSfrequent[,"Sells"],
                             factorization = "Lin_Ke", init_vals = inits_hac,
                             lower = rep(0,5), upper = c(1,1, rep(Inf,3)))

pin_core_hac_ref <- pin_est_core(numbuys = BSfrequent[,"Buys"],
                                 numsells = BSfrequent[,"Sells"],
                                 factorization = "Lin_Ke", init_vals = inits_hac_ref,
                                 lower = rep(0,5), upper = c(1,1, rep(Inf,3)))

## Not run: 
pin_core_hac <- pin_est_core(numbuys = BSfrequent[,"Buys"],
                             numsells = BSfrequent[,"Sells"],
                             factorization = "Lin_Ke", init_vals = inits_hac,
                             lower = rep(0,5), upper = c(1,1, rep(Inf,3)),
                             confint = TRUE)

## End(Not run)