Package 'InfoTrad' reference manual

Title:	Calculates the Probability of Informed Trading (PIN)
Description:	Estimates the probability of informed trading (PIN) initially introduced by Easley et. al. (1996) <doi:10.1111/j.1540-6261.1996.tb04074.x> . Contribution of the package is that it uses likelihood factorizations of Easley et. al. (2010) <doi:10.1017/S0022109010000074> (EHO factorization) and Lin and Ke (2011) <doi:10.1016/j.finmar.2011.03.001> (LK factorization). Moreover, the package uses different estimation algorithms. Specifically, the grid-search algorithm proposed by Yan and Zhang (2012) <doi:10.1016/j.jbankfin.2011.08.003> , hierarchical agglomerative clustering approach proposed by Gan et. al. (2015) <doi:10.1080/14697688.2015.1023336> and later extended by Ersan and Alici (2016) <doi:10.1016/j.intfin.2016.04.001> .
Authors:	Duygu Celik and Murat Tinic
Maintainer:	Murat Tinic <[email protected]>
License:	GPL-3
Version:	1.2
Built:	2025-02-15 05:24:52 UTC
Source:	https://github.com/cran/InfoTrad

Calculates the Probability of Informed Trading (PIN)

Description

Estimates the probability of informed trading (PIN) initially introduced by Easley et. al. (1996) <doi:10.1111/j.1540-6261.1996.tb04074.x> . Contribution of the package is that it uses likelihood factorizations of Easley et. al. (2010) <doi:10.1017/S0022109010000074> (EHO factorization) and Lin and Ke (2011) <doi:10.1016/j.finmar.2011.03.001> (LK factorization). Moreover, the package uses different estimation algorithms. Specifically, the grid-search algorithm proposed by Yan and Zhang (2012) <doi:10.1016/j.jbankfin.2011.08.003> , hierarchical agglomerative clustering approach proposed by Gan et. al. (2015) <doi:10.1080/14697688.2015.1023336> and later extended by Ersan and Alici (2016) <doi:10.1016/j.intfin.2016.04.001> .

Author(s)

Duygu Celik and Murat Tinic

Maintainer: Murat Tinic <[email protected]>

References

D. Easley, N. M. Kiefer, M. O'Hara, and J.B. Paperman. Liquidity, information, and infrequently traded stocks. The Journal of Finance, 51(4):1405-1436, 1996

D. Easley, S. Hvidkjaer, and M. O'Hara. Is information risk a determinant of asset returns? The Journal of Finance, 57(5): 2185-2221, 2002.

D. Easley, S. Hvidkjaer, and M. O'Hara. Factoring information into returns. Journal of Financial and Quantitative Analysis, 45(2): 293-309, 2010.

Ersan, O., and Alici, A. An unbiased computation methodology for estimating the probability of informed trading (PIN). Journal of International Financial Markets, Institutions and Money, 43(1):74-94, 2016.

Q. Gan, W.C. Wei, and D. Johnstone. A faster estimation method for the probability of informed trading using hierarhical agglomerative clustering. Quantitative Finance, 15(11):1805-1821, 2015

H.W. W. Lin and W. C. Ke. A computing bias in estimating the probability of informed trading. Journal of Financial Markets, 14(4):625-640, 2011.

D. Mullner. fastcluster: Fast hierarchical, agglomerative clustering routines for r and python. Journal of Statistical Software, 53(9):1-18, 2013.

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

Ersan and Alici (2016) Cluster analysis with the altered steps.

Description

It estimates PIN using Ersan and Alici (2016) modified clustering algorithm.

Usage

EA(data, likelihood = c("LK", "EHO"))
## S3 method for class 'EA_class'
print(obj)EA(data, likelihood = c("LK", "EHO"))
## S3 method for class 'EA_class'
print(obj)

Arguments

`data`	Data frame with 2 variables
`likelihood`	Character strings for likelihood algorithm. Default is "LK".
`obj`	object variable

Details

Argument for data must be a data frame with 2 columns that only contain numbers. Not any other type. You do not have to give names to the columns. We will assign first one as "Buy" and second as "Sell", therefore you should put order numbers with respect to this order.

Value

Returns a list of parameter estimates (output)

`alpha`	A Number
`delta`	A Number
`mu`	A Number
`eb`	A Number
`es`	A Number
`LikVal`	A Number
`PIN`	A Number

Warning

This function does not handle NA values. Therefore the datasets should not contain any missing value. This function do not conduct the iterative estimation procedure proposed in the same paper.

Author(s)

Duygu Celik and Murat Tinic

References

Ersan, Oguz, and Asli Alici . "An unbiased computation methodology for estimating the probability of informed trading (PIN)." Journal of International Financial Markets, Institutions and Money 43 (2016): 74-94.

Examples

 
  # Sample Data
  #   Buy Sell 
  #1  350  382  
  #2  250  500  
  #3  500  463  
  #4  552  550  
  #5  163  200  
  #6  345  323  
  #7  847  456  
  #8  923  342  
  #9  123  578  
  #10 349  455   
  
  Buy=c(350,250,500,552,163,345,847,923,123,349)
  Sell=c(382,500,463,550,200,323,456,342,578,455)
  data=cbind(Buy,Sell)
  
  # Parameter estimates using the LK factorization of Lin and Ke (2011) 
  # with the modified clustering algorithm of Ersan and Alici (2016).
  # Default factorization is set to be "LK"
  
  result=EA(data)
  print(result)
  
  # Alpha: 0.9511418 
  # Delta: 0.2694005 
  # Mu: 76.7224 
  # Epsilon_b: 493.7045 
  # Epsilon_s: 377.4877 
  # Likelihood Value: 43973.71 
  # PIN: 0.07728924 
  
  
  # Parameter estimates using the EHO factorization of Easley et. al. (2010) 
  # with the modified clustering algorithm of Ersan and Alici (2016).
  
  result=EA(data,likelihood="EHO")
  print(result)
  
  # Alpha: 0.9511418 
  # Delta: 0.2694005 
  # Mu: 76.7224 
  # Epsilon_b: 493.7045 
  # Epsilon_s: 377.4877 
  # Likelihood Value: 43973.71 
  # PIN: 0.07728924 
# Sample Data
  #   Buy Sell 
  #1  350  382  
  #2  250  500  
  #3  500  463  
  #4  552  550  
  #5  163  200  
  #6  345  323  
  #7  847  456  
  #8  923  342  
  #9  123  578  
  #10 349  455   
  
  Buy=c(350,250,500,552,163,345,847,923,123,349)
  Sell=c(382,500,463,550,200,323,456,342,578,455)
  data=cbind(Buy,Sell)
  
  # Parameter estimates using the LK factorization of Lin and Ke (2011) 
  # with the modified clustering algorithm of Ersan and Alici (2016).
  # Default factorization is set to be "LK"
  
  result=EA(data)
  print(result)
  
  # Alpha: 0.9511418 
  # Delta: 0.2694005 
  # Mu: 76.7224 
  # Epsilon_b: 493.7045 
  # Epsilon_s: 377.4877 
  # Likelihood Value: 43973.71 
  # PIN: 0.07728924 
  
  
  # Parameter estimates using the EHO factorization of Easley et. al. (2010) 
  # with the modified clustering algorithm of Ersan and Alici (2016).
  
  result=EA(data,likelihood="EHO")
  print(result)
  
  # Alpha: 0.9511418 
  # Delta: 0.2694005 
  # Mu: 76.7224 
  # Epsilon_b: 493.7045 
  # Epsilon_s: 377.4877 
  # Likelihood Value: 43973.71 
  # PIN: 0.07728924

Likelihood factorization of Easley et. al. (2010) - EHO Factorization

Description

The function calculates the likelihood factorization of Easley et. al. (2010) and computes paramaters for estimation of PIN value.

Usage

EHO(data, fixed = c(FALSE, FALSE, FALSE, FALSE, FALSE))EHO(data, fixed = c(FALSE, FALSE, FALSE, FALSE, FALSE))

Arguments

`data`	Data frame with 2 variables
`fixed`	Initial values for parameters in the following order: alpha, delta, mu, epsilon_b, epsilon_s

Details

In order to use EHO's return in optimization functions, please omit second argument. With this way, EHO will return a function instead of a value. Moreover, argument for data must be a data frame with 2 columns that contain numbers. Not any other type.

Value

LK_out

Returns an optim() object including parameter estimates for the likelihood factorization of Easley et. al. (2010)

Warning

This function does not handle NA values. Therefore the datasets should not contain any missing values.

Author(s)

Duygu Celik and Murat Tinic

References

Easley, D., Hvidkjaer, S., & O'Hara, M. Factoring information into returns. Journal of Financial and Quantitative Analysis, 45(2):293-309,2010.

Examples

  # Sample Data
  #   Buy Sell 
  #1  350  382  
  #2  250  500  
  #3  500  463  
  #4  552  550  
  #5  163  200  
  #6  345  323  
  #7  847  456  
  #8  923  342  
  #9  123  578  
  #10 349  455 
  
  Buy<-c(350,250,500,552,163,345,847,923,123,349)
  Sell<-c(382,500,463,550,200,323,456,342,578,455)
  data=cbind(Buy,Sell)

  # Initial parameter values
  # par0 = (alpha, delta, mu, epsilon_b, epsilon_s)
  par0 = c(0.5,0.5,300,400,500)

  # Call EHO function
  EHO_out = EHO(data)
  model = optim(par0, EHO_out, gr = NULL, method = c("Nelder-Mead"), hessian = FALSE)

  ## Parameter Estimates
  model$par[1] # Estimate for alpha
  # [1] 0.9111102
  model$par[2] # Estimate for delta
  #[1] 0.0001231429
  model$par[3] # Estimate for mu
  # [1] 417.1497
  model$par[4] # Estimate for eb
  # [1] 336.075
  model$par[5] # Estimate for es
  # [1] 466.2539
  
  ## Estimate for PIN
  (model$par[1]*model$par[3])/((model$par[1]*model$par[3])+model$par[4]+model$par[5])
  # [1] 0.3214394
  ####

# Sample Data
  #   Buy Sell 
  #1  350  382  
  #2  250  500  
  #3  500  463  
  #4  552  550  
  #5  163  200  
  #6  345  323  
  #7  847  456  
  #8  923  342  
  #9  123  578  
  #10 349  455 
  
  Buy<-c(350,250,500,552,163,345,847,923,123,349)
  Sell<-c(382,500,463,550,200,323,456,342,578,455)
  data=cbind(Buy,Sell)

  # Initial parameter values
  # par0 = (alpha, delta, mu, epsilon_b, epsilon_s)
  par0 = c(0.5,0.5,300,400,500)

  # Call EHO function
  EHO_out = EHO(data)
  model = optim(par0, EHO_out, gr = NULL, method = c("Nelder-Mead"), hessian = FALSE)

  ## Parameter Estimates
  model$par[1] # Estimate for alpha
  # [1] 0.9111102
  model$par[2] # Estimate for delta
  #[1] 0.0001231429
  model$par[3] # Estimate for mu
  # [1] 417.1497
  model$par[4] # Estimate for eb
  # [1] 336.075
  model$par[5] # Estimate for es
  # [1] 466.2539
  
  ## Estimate for PIN
  (model$par[1]*model$par[3])/((model$par[1]*model$par[3])+model$par[4]+model$par[5])
  # [1] 0.3214394
  ####

GAN et al.(2015) Clustering based PIN Estimates

Description

It estimates PIN using hierarchical agglomertaive clustering.

Usage

GAN(data, likelihood = c("LK", "EHO"))
## S3 method for class 'GAN_class'
print(obj)GAN(data, likelihood = c("LK", "EHO"))
## S3 method for class 'GAN_class'
print(obj)

Arguments

`data`	Data frame with 2 variables
`likelihood`	Character strings for likelihood algorithm. Default is "LK".
`obj`	object variable

Details

Value

Returns a list of parameter estimates (output)

`alpha`	A Number
`delta`	A Number
`mu`	A Number
`eb`	A Number
`es`	A Number
`LikVal`	A Number
`PIN`	A Number

Warning

This function does not handle NA values. Therefore, the dataset should not contain any missing values.

Author(s)

Duygu Celik and Murat Tinic

References

D. Mullner. fastcluster: Fast hierarchical, agglomerative clustering routines for r and python. Journal of Statistical Software, 53(9):1-18, 2013.

Gan, Q., Wei, W. C., & Johnstone, D. A faster estimation method for the probability of informed trading using hierarchical agglomerative clustering. Quantitative Finance, 15(11), 1805-1821, 2015.

Examples

  # Sample Data
  #   Buy Sell 
  #1  350  382  
  #2  250  500  
  #3  500  463  
  #4  552  550  
  #5  163  200  
  #6  345  323  
  #7  847  456  
  #8  923  342  
  #9  123  578  
  #10 349  455   
  
  Buy<-c(350,250,500,552,163,345,847,923,123,349)
  Sell<-c(382,500,463,550,200,323,456,342,578,455)
  data<-cbind(Buy,Sell)

  # Parameter estimates using the LK factorization of Lin and Ke (2011) 
  # with the algorithm of Gan et. al. (2015).
  # Default factorization is set to be "LK"
  
  result=GAN(data)
  print(result)
  
  # Alpha: 0.3999998 
  # Delta: 0 
  # Mu: 442.1667 
  # Epsilon_b: 263.3333 
  # Epsilon_s: 424.9 
  # Likelihood Value: 44371.84 
  # PIN: 0.2044464 
  
  # Parameter estimates using the EHO factorization of Easley et. al. (2010) 
  # with the algorithm of Gan et. al. (2015)
  
  result=GAN(data, likelihood="EHO")
  print(result)
  
  # Alpha: 0.3230001 
  # Delta: 0.4780001 
  # Mu: 481.3526 
  # Epsilon_b: 356.6359 
  # Epsilon_s: 313.136 
  # Likelihood Value: Inf 
  # PIN: 0.1884001 


# Sample Data
  #   Buy Sell 
  #1  350  382  
  #2  250  500  
  #3  500  463  
  #4  552  550  
  #5  163  200  
  #6  345  323  
  #7  847  456  
  #8  923  342  
  #9  123  578  
  #10 349  455   
  
  Buy<-c(350,250,500,552,163,345,847,923,123,349)
  Sell<-c(382,500,463,550,200,323,456,342,578,455)
  data<-cbind(Buy,Sell)

  # Parameter estimates using the LK factorization of Lin and Ke (2011) 
  # with the algorithm of Gan et. al. (2015).
  # Default factorization is set to be "LK"
  
  result=GAN(data)
  print(result)
  
  # Alpha: 0.3999998 
  # Delta: 0 
  # Mu: 442.1667 
  # Epsilon_b: 263.3333 
  # Epsilon_s: 424.9 
  # Likelihood Value: 44371.84 
  # PIN: 0.2044464 
  
  # Parameter estimates using the EHO factorization of Easley et. al. (2010) 
  # with the algorithm of Gan et. al. (2015)
  
  result=GAN(data, likelihood="EHO")
  print(result)
  
  # Alpha: 0.3230001 
  # Delta: 0.4780001 
  # Mu: 481.3526 
  # Epsilon_b: 356.6359 
  # Epsilon_s: 313.136 
  # Likelihood Value: Inf 
  # PIN: 0.1884001

Likelihood factorization of Lin and Ke (2011) - LK Factorization

Description

The function calculates the likelihood factorization of Lin and Ke (2011) and computes paramaters for estimation of PIN value.

Usage

LK(data, fixed = c(FALSE, FALSE, FALSE, FALSE, FALSE))LK(data, fixed = c(FALSE, FALSE, FALSE, FALSE, FALSE))

Arguments

`data`	Data frame with 2 variables
`fixed`	Initial values for parameters in the following order: alpha, delta, mu, epsilon_b, epsilon_s

Details

In order to use LK's return in optimization functions, please omit second argument. With this way, LK will return a function instead of a value. Moreover, argument for data must be a data frame with 2 columns that contain numbers. Not any other type.

Value

LK_out

Returns an optim() object including parameter estimates for the likelihood factorization of Lin and Ke (2011)

Warning

This function does not handle NA values. Therefore the datasets should not contain any missing value

Author(s)

Duygu Celik and Murat Tinic

References

Lin, H.W.W. and Ke, W.C. A computing bias in estimating the probability of informed trading. Journal of Financial Markets, 14(4), pp.625-640, 2011.

Examples

  # Sample Data
  #   Buy Sell 
  #1  350  382  
  #2  250  500  
  #3  500  463  
  #4  552  550  
  #5  163  200  
  #6  345  323  
  #7  847  456  
  #8  923  342  
  #9  123  578  
  #10 349  455 
  
  Buy<-c(350,250,500,552,163,345,847,923,123,349)
  Sell<-c(382,500,463,550,200,323,456,342,578,455)
  data=cbind(Buy,Sell)
  
  # Initial parameter values
  # par0 = (alpha, delta, mu, epsilon_b, epsilon_s)
  par0 = c(0.5,0.5,300,400,500)
  
  # Call LK function
  LK_out = LK(data)
  model = optim(par0, LK_out, gr = NULL, method = c("Nelder-Mead"), hessian = FALSE)
  
  ## Parameter Estimates
  model$par[1] # Estimate for alpha
  # [1] 0.480277
  model$par[2] # Estimate for delta
  # [1] 0.830850
  model$par[3] # Estimate for mu
  # [1] 315.259805
  model$par[4] # Estimate for eb
  # [1] 296.862318
  model$par[5] # Estimate for es
  # [1] 434.3046
  
  ## Estimate for PIN 
  (model$par[1]*model$par[3])/((model$par[1]*model$par[3])+model$par[4]+model$par[5])
  # [1] 0.178391
  ####
# Sample Data
  #   Buy Sell 
  #1  350  382  
  #2  250  500  
  #3  500  463  
  #4  552  550  
  #5  163  200  
  #6  345  323  
  #7  847  456  
  #8  923  342  
  #9  123  578  
  #10 349  455 
  
  Buy<-c(350,250,500,552,163,345,847,923,123,349)
  Sell<-c(382,500,463,550,200,323,456,342,578,455)
  data=cbind(Buy,Sell)
  
  # Initial parameter values
  # par0 = (alpha, delta, mu, epsilon_b, epsilon_s)
  par0 = c(0.5,0.5,300,400,500)
  
  # Call LK function
  LK_out = LK(data)
  model = optim(par0, LK_out, gr = NULL, method = c("Nelder-Mead"), hessian = FALSE)
  
  ## Parameter Estimates
  model$par[1] # Estimate for alpha
  # [1] 0.480277
  model$par[2] # Estimate for delta
  # [1] 0.830850
  model$par[3] # Estimate for mu
  # [1] 315.259805
  model$par[4] # Estimate for eb
  # [1] 296.862318
  model$par[5] # Estimate for es
  # [1] 434.3046
  
  ## Estimate for PIN 
  (model$par[1]*model$par[3])/((model$par[1]*model$par[3])+model$par[4]+model$par[5])
  # [1] 0.178391
  ####

Yan and Zhang (2012) Grid-Search based PIN Estimates

Description

It estimates PIN using Yan and Zhang (2012) algorithm.

Usage

YZ(data, likelihood = c("LK", "EHO"))
## S3 method for class 'YZ_class'
print(obj)YZ(data, likelihood = c("LK", "EHO"))
## S3 method for class 'YZ_class'
print(obj)

Arguments

`data`	Data frame with 2 variables
`likelihood`	Character strings for likelihood algorithm. Default is "LK".
`obj`	object variable

Details

Value

Returns a list of parameter estimates (output)

`alpha`	A Number
`delta`	A Number
`mu`	A Number
`eb`	A Number
`es`	A Number
`LikVal`	A Number
`PIN`	A Number

Warning

This function does not handle NA values. Therefore the datasets should not contain any missing value

Author(s)

Duygu Celik and Murat Tinic

References

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

Examples

 
  # Sample Data
  #   Buy Sell 
  #1  350  382  
  #2  250  500  
  #3  500  463  
  #4  552  550  
  #5  163  200  
  #6  345  323  
  #7  847  456  
  #8  923  342  
  #9  123  578  
  #10 349  455   
  
  Buy<-c(350,250,500,552,163,345,847,923,123,349)
  Sell<-c(382,500,463,550,200,323,456,342,578,455)
  data<-cbind(Buy,Sell)
  
  # Parameter estimates using the LK factorization of Lin and Ke (2011) 
  # with the algorithm of Yan and Zhang (2012).
  # Default factorization is set to be "LK"
  
  result=YZ(data)
  print(result)
  
  # Alpha: 0.3999999 
  # Delta: 0 
  # Mu: 442.1667 
  # Epsilon_b: 263.3333 
  # Epsilon_s: 424.9 
  # Likelihood Value: 44371.84 
  # PIN: 0.2004457 
  
  # Parameter estimates using the EHO factorization of Easley et. al. (2010) 
  # with the algorithm of Yan and Zhang (2012).
  
  result=YZ(data,likelihood="EHO")
  print(result)
  
  # Alpha: 0.9000001 
  # Delta: 0.9000001 
  # Mu: 489.1111 
  # Epsilon_b: 396.1803 
  # Epsilon_s: 28.72002 
  # Likelihood Value: Inf 
  # PIN: 0.3321033 

# Sample Data
  #   Buy Sell 
  #1  350  382  
  #2  250  500  
  #3  500  463  
  #4  552  550  
  #5  163  200  
  #6  345  323  
  #7  847  456  
  #8  923  342  
  #9  123  578  
  #10 349  455   
  
  Buy<-c(350,250,500,552,163,345,847,923,123,349)
  Sell<-c(382,500,463,550,200,323,456,342,578,455)
  data<-cbind(Buy,Sell)
  
  # Parameter estimates using the LK factorization of Lin and Ke (2011) 
  # with the algorithm of Yan and Zhang (2012).
  # Default factorization is set to be "LK"
  
  result=YZ(data)
  print(result)
  
  # Alpha: 0.3999999 
  # Delta: 0 
  # Mu: 442.1667 
  # Epsilon_b: 263.3333 
  # Epsilon_s: 424.9 
  # Likelihood Value: 44371.84 
  # PIN: 0.2004457 
  
  # Parameter estimates using the EHO factorization of Easley et. al. (2010) 
  # with the algorithm of Yan and Zhang (2012).
  
  result=YZ(data,likelihood="EHO")
  print(result)
  
  # Alpha: 0.9000001 
  # Delta: 0.9000001 
  # Mu: 489.1111 
  # Epsilon_b: 396.1803 
  # Epsilon_s: 28.72002 
  # Likelihood Value: Inf 
  # PIN: 0.3321033

Package 'InfoTrad'

Help Index

Calculates the Probability of Informed Trading (PIN)

Description

Author(s)

References

Ersan and Alici (2016) Cluster analysis with the altered steps.

Description

Usage

Arguments

Details

Value

Warning

Author(s)

References

Examples

Likelihood factorization of Easley et. al. (2010) - EHO Factorization

Description

Usage

Arguments

Details

Value

Warning

Author(s)

References

Examples

GAN et al.(2015) Clustering based PIN Estimates

Description

Usage

Arguments

Details

Value

Warning

Author(s)

References

Examples

Likelihood factorization of Lin and Ke (2011) - LK Factorization

Description

Usage

Arguments

Details

Value

Warning

Author(s)

References

Examples

Yan and Zhang (2012) Grid-Search based PIN Estimates

Description

Usage

Arguments

Details

Value

Warning

Author(s)

References

Examples