generalized_sign_test: An event study binomial sign test.
In estudy2: An Implementation of Parametric and Nonparametric Event Study

Description Usage Arguments Details Value References See Also Examples

A binomial sign test which determines whether the frequency of positive abnormal returns in the event period is significantly different from the frequency in the estimation period.

1	generalized_sign_test(list_of_returns, event_start, event_end)

`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.

This test is application of the binomial test to the event study, which indicates whether the cross-sectional frequency of positive abnormal returns is significantly different from the expected. This test is stable to outliers, in other words allows for checking if the result is driven by few companies with extremely large abnormal performance. For this test the estimation period and the event period must not overlap, otherwise an error will be thrown. This test uses an estimate from the estimation period instead of using naive value of expected frequency 0.5. The test statistic is assumed to have a normal distribution. Typically the test is used together with parametric tests. The test is well-specified for the case, when cross-sectional abnormal returns are not symmetric. Also this procedure is less sensitive to extreme returns than the rank test. 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
gsign_stat: a generalized sign test statistic
gsign_signif: a significance of the statistic

McConnell J.J., Muscarella C.J. Capital expenditure plans and firm value Journal of Financial Economics, 14:399-422, 1985.
Cowan A.R. Nonparametric Event Study Tests. Review of Quantitative Finance and Accounting, 2:343-358, 1992.

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

## 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")) %>%
    generalized_sign_test(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)
generalized_sign_test(list_of_returns = securities_returns,
                      event_start =  as.Date("2020-03-16"),
                      event_end = as.Date("2020-03-20"))