Computes, for each day in the sample, the posterior probability that the day is a no-information day, good-information day and bad-information day, respectively (Easley and Ohara (1992) , Easley et al. (1996) , Ersan (2016) ).
Value
If the argument object is of type estimate.pin, returns a dataframe of
three variables post.N, post.G and post.B containing in each row the
posterior probability that a given day is a no-information day (N),
good-information day (G), or bad-information day (B) respectively.
If the argument object is of type estimate.mpin or estimate.mpin.ecm,
with J layers, returns a dataframe of 2*J+1 variables Post.N, and
Post.G[j] and Post.B[j] for each layer j containing in each row the
posterior probability that a given day is a no-information day,
good-information day in layer j or bad-information day in layer j,
for each layer j respectively.
If the argument object is of any other type, an error is returned.
References
Easley D, Kiefer NM, Ohara M, Paperman JB (1996).
“Liquidity, information, and infrequently traded stocks.”
Journal of Finance, 51(4), 1405--1436.
ISSN 00221082.
Easley D, Ohara M (1992).
“Time and the Process of Security Price Adjustment.”
The Journal of Finance, 47(2), 577--605.
ISSN 15406261.
Ersan O (2016).
“Multilayer Probability of Informed Trading.”
Available at SSRN 2874420.
Examples
# 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
# ------------------------------------------------------------------------ #
# Posterior probabilities for PIN estimates #
# ------------------------------------------------------------------------ #
# Estimate PIN using the Ersan and Alici (2016) algorithm and the
# factorization Lin and Ke(2011).
estimate <- pin_ea(xdata, "LK", verbose = FALSE)
# Display the estimated PIN value
estimate@pin
#> [1] 0.5661721
# Store the posterior probabilities in a dataframe variable and display its
# first 6 rows.
modelposteriors <- get_posteriors(estimate)
show(round(head(modelposteriors), 3))
#> post.N post.G post.B
#> 1 1 0 0
#> 2 0 1 0
#> 3 0 1 0
#> 4 0 1 0
#> 5 0 1 0
#> 6 0 1 0
# ------------------------------------------------------------------------ #
# Posterior probabilities for MPIN estimates #
# ------------------------------------------------------------------------ #
# Estimate MPIN via the ECM algorithm, assuming that the dataset has 2
# information layers
estimate <- mpin_ecm(xdata, layers = 2, verbose = FALSE)
# Display the estimated Multilayer PIN value
show(estimate@mpin)
#> [1] 0.576684
# Store the posterior probabilities in a dataframe variable and display its
# first six rows. The posterior probabilities are contained in a dataframe
# with 7 variables: one for no-information days, and two variables for each
# layer, one for good-information days and one for bad-information days.
modelposteriors <- get_posteriors(estimate)
show(round(head(modelposteriors), 3))
#> post.N post.G[1] post.G[2] Post.B[1] Post.B[2]
#> 1 1 0 0 0 0
#> 2 0 1 0 0 0
#> 3 0 1 0 0 0
#> 4 0 1 0 0 0
#> 5 0 1 0 0 0
#> 6 0 1 0 0 0