The event2car package allows users to generate Cumulative Abnormal Returns (CAR) of one or multiple events.
You can install the released version of event2car from GitHub with:
# install.pacakges("devtools") library(devtools) install_github("LisaLechner/event2car")
In classic event studies, a Cumulative Abnormal Return (CAR) measures the effect of events on one firm’s value, or as the case may be on several firms’ values. Several steps and decisions lead to such a CAR measure(s). The estudy2 package allows for the implementation of various parametric and nonparametric tests, but misses functionalities to calculate CAR measures of an event or multiple events. It is inefficient and time-consuming to derive the CAR measure from the estudy2 output and what is even more: it is hardly possible to implement various estimation strategies, which the literature suggests.
The event2car package provides functions to calculate CAR measures with varying estimation strategies and decisions, which are summarized in the following:
In general, two classical models exist: mean adjusted returns and market adjusted returns models. For an overview see Brown and Warner (1980) and Davies and Studnicka (2018).
If one chooses the most common model, namely the market adjusted model, two main estimation strategies exist:
Out-of-sample-prediction: Predict returns during an estimation window prior to the actual event and use fitted values to calculate the abnormal and then the cumulative abnormal returns at the event. For details about this approach see MacKinlay (1997).
In-sample-prediction: Predict returns during an estimation window that includes the event date, which is identified by a dummy variable in the estimation. Then, one uses the coefficient of the event date dummy to receive the abnormal, or as the case may be cumulative abnormal returns. For an implementation see Davies and Studnicka (2018).
For mean adjusted models, the mean return during the event period gets substracted from the mean return of the estimation period.
The duration of the estimation period varies and normally ranges from 250 days to 100 days, depending on the research problem at hand.
Since markets are unlikely to efficiently price in new information in a single day, the event period, which are days around the event day, must be defined. Mostly, one starts the event window at the day of the event and includes three to five trading days after the event day.
This is a basic example which shows you how to solve a common problem:
library(event2car) trumpelection <- "2016-11-08" tariffcutschina <- "2019-12-13" returns_firms <- tech_returns[,2:19] return_indx <- tech_returns[,1] # mean adjusted model event2car(returns=returns_firms,regressor=return_indx, event_date=c(trumpelection,tariffcutschina),method="mean_adj") # market adjusted model (out-of sample estimation) event2car(returns=returns_firms,regressor=return_indx, event_date=c(trumpelection,tariffcutschina),method="mrkt_adj_out") # market adjusted model (within sample estimation) event2car(returns=returns_firms,regressor=return_indx, event_date=c(trumpelection,tariffcutschina),method="mrkt_adj_within") # time-series car object effect_trump <- event2car_range(returns=returns_firms,regressor=return_indx, imputation_returns="mean", event_date=trumpelection,method="mean_adj") summary(effect_trump) plot(effect_trump)
Brown, Stephen J., and Jerold B. Warner. 1980. “Measuring security price performance.” Journal of Financial Economics 8 (3): 205–58. https://doi.org/10.1016/0304-405X(80)90002-1.
Davies, Ronald B., and Zuzanna Studnicka. 2018. “The heterogeneous impact of Brexit: Early indications from the FTSE.” European Economic Review 110 (November). North-Holland: 1–17. https://doi.org/10.1016/J.EUROECOREV.2018.08.003.
MacKinlay, A. Craig. 1997. “Event Studies in Economics and Finance.” Journal of Economic Literature 35 (1). American Economic Association: 13–39. https://doi.org/10.2307/2729691.
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