Synthetic Matching Method for Returns

Implements the revised Synthetic Matching Algorithm of Kreitmeir, Lane, and Raschky (2025) , building on the original approach of Acemoglu, Johnson, Kermani, Kwak, and Mitton (2016) , to estimate the cumulative treatment effect of an event on treated firms’ stock returns.

synthReturn

The synthReturn R package implements the revised Synthetic Matching Algorithm of Kreitmeir et al. (2025), building on the original approach of Acemoglu et al. (2016), to estimate the cumulative treatment effect of an event on treated firms’ stock returns. For details on the Synthetic Matching Algorithm and the available inference methods, see Section A.2 of the supplementary Online Appendix.

If you end up using this package, please cite the package and our paper:

Kreitmeir, D., and Düben, C. (2025). synthReturn. R Package Version 1.0.0.
Kreitmeir, D., Lane, N., and Raschky, P. A. (2025). The value of names - Civil society, information, and governing multinationals., conditionally accepted at Journal of the European Economic Association.

Installation

To install the most recent version of the synthReturn package from GitHub:

# install.packages("devtools")
devtools::install_github("davidkreitmeir/synthReturn")

Short examples

The following is an illustration of the method for a simulated dataset with two event-dates.

library(synthReturn)
# Load data in that comes in the synthReturn package
data(ret_two_evdates)

We run the synthetic matching algorithm with permutation inference.

set.seed(123) # set random seed

# Run synthReturn
res.placebo <- synthReturn(
  data = ret_two_evdates,
  unitname = "unit",
  treatname = "treat",
  dname = "date",
  rname = "ret",
  edname = "eventdate",
  estwind = c(-100,-1),
  eventwind = c(0,5),
  estobs_min = 1,
  eventobs_min = 1,
  inference = "permutation",
  correction = FALSE,
  ncontrol_min = 10,
  ndraws = 100,
  ncores = 1
)

# Print result table
print(res.placebo)

We run the synthetic matching algorithm with a nonparametric bootstrap procedure to obtain uncertainty estimates.

set.seed(123) # set random seed

# Run synthReturn
res.boot <- synthReturn(
  data = ret_two_evdates,
  unitname = "unit",
  treatname = "treat",
  dname = "date",
  rname = "ret",
  edname = "eventdate",
  estwind = c(-100,-1),
  eventwind = c(0,5),
  estobs_min = 1,
  eventobs_min = 1,
  inference = "bootstrap",
  correction = FALSE,
  ncontrol_min = 10,
  ndraws = 100,
  ncores = 1
)

# Print result table
print(res.boot)

We make use of the parallelization of synthRetrun by setting ncores = NULL. The default ncores = NULL uses all available cores. In addition, we provide the option static_scheduling to set the scheduling type, where TRUE (default) implies static scheduling, and FALSE dynamic scheduling. Note that the scheduling choice has no effect when ncores = 1 and in inference = "permutation" estimations on Windows machines.

set.seed(123) # set random seed

# Run synthReturn
res.parallel <- synthReturn(
  data = ret_two_evdates,
  unitname = "unit",
  treatname = "treat",
  dname = "date",
  rname = "ret",
  edname = "eventdate",
  estwind = c(-100,-1),
  eventwind = c(0,5),
  estobs_min = 1,
  eventobs_min = 1,
  inference = "permutation",
  correction = FALSE,
  ncontrol_min = 10,
  ndraws = 100,
  ncores = NULL,
  static_scheduling = TRUE
)

# Print result table
print(res.parallel)

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