evReturn: Estimating Abnormal Return from Event Analysis

In erer: Empirical Research in Economics with R

Estimating Abnormal Return from Event Analysis

Description

Conduct an event analysis and estimate abnormal returns over time and across firms.

Usage

evReturn(y, firm, event.date, y.date = "date",
  index = "sp500", event.win = 3, est.win = 250, digits = 4, ...)

Arguments

y	a data frame object with one column for date, return series by firms, a return series for a stock market index, and a return series for a risk free asset.
firm	a character vector of firm names; this is the name of the return series in y.
event.date	event dates for each firm as specified in firm; this should be a numerical vector and can match the values in y$y.date; if event dates are the same for all the firms, this can be specificed as a single number.
y.date	a character value for the column name of date in y.
index	a character value for the column name of index in y.
event.win	the one-side width of event window in days; the default value of 3 corresponds to a 7-day window (i.e., 3 + 1 + 3).
est.win	the width of estimation window in days.
digits	number of digits used to format outputs.
...	additional arguments to be passed.

Details

This is the core function for event analysis. It estimates a market model by firm and then calculate abnormal returns by firm and over time. The time series of stock returns have irregular time frequency because of varying trading days. Thus, the time dimension is explicitly specified as a y.date column in the data of y.

Value

Return a list object of class "evReturn" with the following components:

y	a data frame of raw return data.
y.date	a character value for the column name of date in y..
firm	a character vector of firm names.
N	the number of firms.
index	a character value for the column name of index in y.
event.date	event dates for each firm as specified in firm.
event.win	the one-side width of event window in days.
event.width	total number of days in an event window.
est.win	the width of estimation window in days..
daEst	data used to estimate the market model for the last firm as specified in firm.
daEve	data over the event window for the last firm.
ra	fitted market model for the last firm.
digits	number of digits used to format outputs.
reg	regression coefficients by firm.
abr	abnormal returns by day over the event window and by firm.
abc	average abnormal returns across firms.
call	a record of the system call; this allows update.default to be used.

Methods

Two methods are defined as follows:

print:

print three selected outputs.

plot:

plot average cumulative abnormal returns from event analysis versus days in event window.

Author(s)

Changyou Sun ([e-mail])

References

Mei, B., and C. Sun. 2008. Event analysis of the impact of mergers and acquisitions on the financial performance of the U.S. forest products industry. Forest Policy and Economics 10(5):286-294.

Sun, C., and X. Liao. 2011. Effects of litigation under the Endangered Species Act on forest firm values. Journal of Forest Economics 17(4):388-398.

See Also

evRisk

Examples

data(daEsa)

# event analysis for one firm and one event window
hh <- evReturn(y = daEsa, firm = "wpp", 
   y.date = "date", index = "sp500", est.win = 250, digits = 3,
   event.date = 19990505, event.win = 5)
hh; plot(hh)

# event analysis for many firms and one event window
hh2 <- update(hh, firm = c("tin", "wy", "pcl", "pch")); hh2

# event analysis for many firms and many event windows: need a for loop

erer documentation built on Sept. 26, 2024, 5:06 p.m.