Nothing
## ----terrorism-example--------------------------------------------------------
# For each market with
# different time zones
library(eventstudies)
data(TerrorIndiceReturns)
data(TerrorAttack)
## ----terrorism-example2-------------------------------------------------------
TerrorIndiceCAR <- lapply(1: ncol(TerrorIndiceReturns), function(x){
# 10-day window around the event
event <- phys2eventtime(na.omit(TerrorIndiceReturns[ , x,
drop = FALSE]),
TerrorAttack,
10)
# Estimate ARs
esMean <- constantMeanReturn(event$z.e[which(attributes(event$z.e)$index
%in% -30:-11), ],
residual = FALSE)
ar <- event$z.e - esMean
ar <- window(ar, start = 0, end = 10)
# CAR
car <- remap.cumsum(ar, base = as.numeric(ar[1, 1]))
names(car) <- colnames(TerrorIndiceReturns[ , x,
drop = FALSE])
return(car)
})
names(TerrorIndiceCAR) <- colnames(TerrorIndiceReturns)
# Compile for all indices
TerrorIndiceCAR <- do.call(cbind, TerrorIndiceCAR)
# 11-day CAR
TerrorIndiceCAR[11, ]
# 6-day CAR
TerrorIndiceCAR[6, ]
## ----earnings-example---------------------------------------------------------
# For each market with
# different time zones
data(KGStockReturns)
data(KGMarketReturns)
data(KGSurpriseCategory)
## ----earnings-example2--------------------------------------------------------
es.categories <- function(stock, market, surprise, option){
# Categorising returns for
# each category
surprise <- surprise[which(surprise$Category %in% option), ]
stock <- stock[ , which(names(stock) %in%
as.character(surprise$Company))]
market <- market[ , which(names(market) %in%
as.character(surprise$Company))]
# ARs
ar <- lapply(1:NCOL(stock), function(x){
output <- excessReturn(stock[ , x],
market[ , x])
return(output)
})
names(ar) <- names(stock)
ar <- do.call("merge", ar)
# CARs
car <- lapply(1:NCOL(ar), function(x){
tmp <- remap.cumsum(z = ar[ , x],
base = as.numeric(ar[1, x]))
return(tmp)
})
names(car) <- names(ar)
car <- do.call("merge", car)
# CAARs
caar <- round(apply(car, 1, mean), 10)
return(list(ar, car, caar))
}
# Calling for each category
# Good
goodCompanies <- es.categories(KGStockReturns, KGMarketReturns,
KGSurpriseCategory, "good")
# Medium
mediumCompanies <- es.categories(KGStockReturns, KGMarketReturns,
KGSurpriseCategory, "medium")
# Bad
badCompanies <- es.categories(KGStockReturns, KGMarketReturns,
KGSurpriseCategory, "bad")
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