An event study t-test.

A classical t-test that examines each date in the event window.

t_test(list_of_returns, event_start, event_end)

Arguments

list_of_returns	a list of objects of S3 class returns, each element of which is treated as a security.
event_start	an object of Date class giving the first date of the event period.
event_end	an object of Date class giving the last date of the event period.

Value

A data frame of the following columns:

• date: a calendar date

• weekday: a day of the week

• percentage: a share of non-missing observations for a given day

• mean: an average abnormal return

• t_test_stat: a t-test statistic

• t_test_signif: a significance of the statistic

Details

Performs a t-test for the event study. The procedure of this test is described in Boehmer et al. 1991, sometimes is called a cross-sectional test. Assumes independence of securities, however is stable to event-induced variance. This test examines the equality of the cross-sectional expected value to zero. The standard deviation, which is used in this test, is simply a cross-sectional standard deviation for a given day in the event window. It calculates statistics even if event window and estimation period are overlapped (intersect). The critical values are Student's t-distributed (no approximation in limit). The significance levels of \(\alpha\) are 0.1, 0.05, and 0.01 (marked respectively by *, **, and ***).

Warning

This test strongly requires cross-sectional independence and sensitive to the size of the sample.

References

Boehmer E., Musumeci J., Poulsen A.B. Event-study methodology under conditions of event-induced variance. Journal of Financial Economics, 30(2):253-272, 1991.

See also

parametric_tests, brown_warner_1980, brown_warner_1985, patell, boehmer, and lamb.

Examples

if (FALSE) {
library("magrittr")
rates_indx <- get_prices_from_tickers("^GSPC",
                                      start = as.Date("2019-04-01"),
                                      end = as.Date("2020-04-01"),
                                      quote = "Close",
                                      retclass = "zoo") %>%
    get_rates_from_prices(quote = "Close",
                          multi_day = TRUE,
                          compounding = "continuous")
tickers <- c("AMZN", "ZM", "UBER", "NFLX", "SHOP", "FB", "UPWK")
get_prices_from_tickers(tickers,
                        start = as.Date("2019-04-01"),
                        end = as.Date("2020-04-01"),
                        quote = "Close",
                        retclass = "zoo") %>%
    get_rates_from_prices(quote = "Close",
                          multi_day = TRUE,
                          compounding = "continuous") %>%
    apply_market_model(regressor = rates_indx,
                       same_regressor_for_all = TRUE,
                       market_model = "sim",
                       estimation_method = "ols",
                       estimation_start = as.Date("2019-04-01"),
                       estimation_end = as.Date("2020-03-13")) %>%
    t_test(event_start = as.Date("2020-03-16"),
           event_end = as.Date("2020-03-20"))
}
## The result of the code above is equivalent to:
data(securities_returns)
t_test(list_of_returns = securities_returns,
       event_start = as.Date("2020-03-16"),
       event_end = as.Date("2020-03-20"))
#>         date   weekday percentage        mean t_test_stat t_test_signif
#> 1 2020-03-16    Monday        100  0.02329740   1.3941497              
#> 2 2020-03-17   Tuesday        100 -0.03157867  -1.2769075              
#> 3 2020-03-18 Wednesday        100 -0.01617850  -0.4023135              
#> 4 2020-03-19  Thursday        100  0.07672027   1.7958275              
#> 5 2020-03-20    Friday        100  0.05840545   5.1233281           ***