Boehmer's parametric test (1991).

Source: R/parametric_tests.R

An event study parametric test described in Boehmer 1991.

boehmer(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

  • bh_stat: a Boehmer's test statistic

  • bh_signif: a significance of the statistic

Details

Performs a parametric test for event study, which is described in Boehmer 1991. Also called hybrid test or standardized cross-sectional test. This test performs t-test based on Patell's standardized residuals. By combining Patell's and t-tests, this test allows for event-induced variance changes, but still assumes cross-sectional independence. The test examines the hypothesis whether the theoretical cross-sectional expected value for a given day is equal to zero. It calculates statistics even if event window and estimation period are overlapped (intersect). The critical values has Student's t-distribution. The significance levels of \(\alpha\) are 0.1, 0.05, and 0.01 (marked respectively by *, **, and ***).

References

  • Patell J.M. Corporate forecasts of earnings per share and stock price behavior: empirical tests. Journal of Accounting Research, 14(2):246- 276, 1976.

  • 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, t_test, patell, 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")) %>%
    boehmer(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)
boehmer(list_of_returns = securities_returns,
        event_start =  as.Date("2020-03-16"),
        event_end = as.Date("2020-03-20"))

#>         date   weekday percentage        mean     bh_stat bh_signif
#> 1 2020-03-16    Monday        100  0.02329740  1.20471169          
#> 2 2020-03-17   Tuesday        100 -0.03157867 -1.17531339          
#> 3 2020-03-18 Wednesday        100 -0.01617850  0.07606284          
#> 4 2020-03-19  Thursday        100  0.07672027  2.16657728         *
#> 5 2020-03-20    Friday        100  0.05840545  8.65212561       ***