Description Usage Arguments Details Value References See Also Examples
View source: R/parametric_tests.R
An event study parametric test described in Boehmer 1991.
1 | boehmer(list_of_returns, event_start, event_end)
|
list_of_returns |
a list of objects of S3 class |
event_start |
an object of |
event_end |
an object of |
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 α are 0.1, 0.05, and 0.01 (marked respectively by *, **, and ***).
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
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.
parametric_tests
, brown_warner_1980
,
brown_warner_1985
, t_test
, patell
,
and lamb
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | ## Not run:
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"))
## End(Not run)
## 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"))
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