Returns the result of given event study nonparametric tests.

Performs main nonparametric tests for each date in the event window and returns a data frame of their statistics and significance.

nonparametric_tests(list_of_returns, event_start, event_end, all = TRUE, tests)

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.
all	a logical vector of length one indicating whether all tests should be performed. The default value is TRUE.
tests	a list of tests' functions among sign_test, generalized_sign_test, corrado_sign_test, rank_test, modified_rank_test, and wilcoxon_test.

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

• Various tests' statistics and significance

Details

nonparametric_tests performs given tests among sign_test, generalized_sign_test, corrado_sign_test, rank_test, modified_rank_test, wilcoxon_test, and merge result to a single data frame. If all = TRUE (the default value), the function ignores the value of tests.

References

• Corrado C.J., Zivney T.L. The Specification and Power of the Sign Test in Event Study Hypothesis Tests Using Daily Stock Returns. Journal of Financial and Quantitative Analysis, 27(3):465-478, 1992.

• McConnell J.J., Muscarella C.J. Capital expenditure plans and firm value Journal of Financial Economics, 14:399-422, 1985.

• 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.

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

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

• 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.

• Kolari J.W., Pynnonen S. Event Study Testing with Cross-sectional Correlation of Abnormal Returns. The Review of Financial Studies, 23(11):3996-4025, 2010.

• Wilcoxon F. Individual Comparisons by Ranking Methods. Biometrics Bulletin 1(6):80-83, 1945.

• Lehmann E.L, Nonparametrics: Statistical Methods Based on Ranks. San Francisco: Holden-Day, 1975.

• Hollander M., Wolfe D.A. Nonparametric Statistical Methods. New York: John Wiley & Sons, 1973.

See also

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

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")
nparam <- 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")) %>%
    nonparametric_tests(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)
nparam <- nonparametric_tests(list_of_returns = securities_returns,
                              event_start = as.Date("2020-03-16"),
                              event_end = as.Date("2020-03-20"))