estudy2 (version 0.10.0)

rank_test: An event study rank test.

Description

An original rank test applied to an event study, which is based on Wilcoxon (1945) rank test.

Usage

rank_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

• rank_stat: a rank test statistic

• rank_signif: a significance of the statistic

Details

This procedure uses ranks of abnormal returns to examine significance of each day in the event window. In order to get ranks of corresponding abnormal returns, the procedure uses regular R function rank with parameter ties.method = "average" and na.last = "keep". For this test the estimation period and the event period must not overlap, otherwise an error will be thrown. The test statistic is assumed to have a normal distribution (as an approximation). The test is well-specified for the case, when cross-sectional abnormal returns are not symmetric. The test is stable to variance increase during event window. This test is more sensitive to extreme values than sign test. For data with missing data see the modified_rank_test. The significance levels of \(\alpha\) are 0.1, 0.05, and 0.01 (marked respectively by *, **, and ***).

References

• Corrado C.J. A Nonparametric Test for Abnormal Security-Price Performance in Event Studies. Journal of Financial Economics 23:385-395, 1989.

• Cowan A.R. Nonparametric Event Study Tests. Review of Quantitative Finance and Accounting, 2:343-358, 1992.

• Campbell C.J., Wasley C.E. Measuring Security Price Performance Using Daily NASDAQ Returns. Journal of Financial Economics 33:73-92, 1993.

• Savickas R. Event-Induced Volatility and Tests for Abnormal Performance. The Journal of Financial Research, 26(2):156-178, 2003.

See Also

nonparametric_tests,sign_test, generalized_sign_test, corrado_sign_test, modified_rank_test, and wilcoxon_test.

Examples

# 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")) %>%
    rank_test(event_start = as.Date("2020-03-16"),
              event_end = as.Date("2020-03-20"))
# }
# NOT RUN {
## The result of the code above is equivalent to:
data(securities_returns)
rank_test(list_of_returns = securities_returns,
          event_start =  as.Date("2020-03-16"),
          event_end = as.Date("2020-03-20"))

# }