[Eventstudiescommits] r116  pkg/vignettes
noreply at rforge.rproject.org
noreply at rforge.rproject.org
Wed Aug 7 13:05:59 CEST 2013
Author: renukasane
Date: 20130807 13:05:59 +0200 (Wed, 07 Aug 2013)
New Revision: 116
Modified:
pkg/vignettes/eventstudies.Rnw
Log:
Small modification to the vignette. Work in progress still.
Modified: pkg/vignettes/eventstudies.Rnw
===================================================================
 pkg/vignettes/eventstudies.Rnw 20130806 15:46:54 UTC (rev 115)
+++ pkg/vignettes/eventstudies.Rnw 20130807 11:05:59 UTC (rev 116)
@@ 31,8 +31,9 @@
of specific events on the value of the firm. The typical procedure for
conducting an event study involves \citep{MacKinlay}
\begin{itemize}
 \item Defining the event of interest and the event window which is
 larger than the specific period of interest. % Generally the event
+\item Defining the event of interest and the event window. The event
+ window is larger than the specific period of
+ interest. % Generally the event
% period itself is not included in the estimation period to prevent
% the event from influencing the normal performance model parameter
% estimates.
@@ 44,13 +45,17 @@
on stock prices of information that is specific to the firm under
question (e.g. stock split annoucement) and information that is
likely to affect stock prices marketwide (e.g. interest rates)
+ \item Analysis of firm returns around the event date
\end{itemize}
The \textbf{eventstudies} package makes possible BLAH. All functions
in this package are implemented in the R system for statistical
computing. The package, and R are available at no cost under the terms
of the general public license (GPL) from the comprehensive R archive
network (CRAN, \texttt{http://CRAN.Rproject.org}).
+The \textbf{eventstudies} package brings together the various aspects
+of an event study analysis in one library. It provides for functions
+to calculate returns, transform data into eventtime, and conduct
+inference on the unit of interest. All functions in this package are
+implemented in the R system for statistical computing. The package,
+and R are available at no cost under the terms of the general public
+license (GPL) from the comprehensive R archive network (CRAN,
+\texttt{http://CRAN.Rproject.org}).
This paper is organised as follows. A skeletal event study model is
presented in Section \ref{s::model}. Section \ref{s:approach}
@@ 61,19 +66,11 @@
inference in section \ref{ss:inference}. Section \ref{s:conclusion}
conclues the paper.
+\section{Skeletal event study model} \label{s:model}
+In this section, we present a model to evaluate the impact of stock
+splits on returns.
% In this package, there are three major functions
% \textit{phys2eventtime}, \textit{remap.cumsum} and
% \textit{inference.Ecar}. \textit{phys2eventtime} changes the
% physical dates to event time frame on which event study analysis can
% be done with ease. \textit{remap.cumsum}
% can be used to convert returns to cumulative sum or product in the
% event time frame. \textit{inference.Ecar} generates bootstrap
% inference for the event time response of the variable.

\section{Skeletal event study model} \label{s:model}

Let day0 identify the stock split date under scrutiny and let days
t = ... 3,2,1 represent trading days leading up to the event. If
the return on the firm with the stock split $R_o$ is statistically
@@ 111,33 +108,41 @@
statistical significance is that abnormal returns are normally
distributed.

\section{Software approach} \label{s:approach}
The package offers the following functionalities:

 Models for calculating returns. These include:
 \begin{itemize}
 \item Excess returns model
 \item Market residual model
 \item Augmented market model (AMM)
 \end{itemize}

\begin{itemize}
+\begin{enumerate}
+ \item Models for calculating returns. These include:
+ \begin{itemize}
+ \item \texttt{excessReturn}: estimation of excess return i.e. $R_j 
+ R_m$ where $R_j$ is the return of firm $j$ and $R_m$ is the market
+ return.
+ \item \texttt{marketResidual}: estimation of market residual after
+ extracting market returns from firm returns.
+ \item \texttt{AMM}: estimation of market residual after extracting
+ market returns and currency returns from firm returns.
+ \end{itemize}
\item Coverting the dataset to an event frame. This requires:
\begin{itemize}
\item A time series object of stock price returns
\item Event dates object with two columns, \textit{unit} and
\textit{when}, the date of occurrence of the event.
\end{itemize}

\item Procedures for inference. These include:
\begin{itemize}
\item Bootstrapping
\item Wilcoxon signed rank test
\end{itemize}
\end{itemize}
+\end{enumerate}
+The first argument of the first two models is the \texttt{data.oject}
+which is a timeseries of stock returns. The second argument is
+\texttt{market.name}, a column name indicating market returns.
+
+The output from these two models is another timeseries object which
+is used for converting to event time.
+
+
\section{Example: Performing Eventstudy analysis}
\label{s:example}
@@ 147,6 +152,8 @@
Exchange (BSE), from 2001 to 2013. We have stock split dates for each
firm from 2000 onwards.
+\subsection{Basic data files}
+
We first create a \textit{zoo} object for stock price returns for the
thirty firms. For event dates, a data frame with two columns
\textit{unit} and \textit{when} is formed. \textit{unit} has name of
@@ 163,6 +170,8 @@
head(SplitDates)
@
+\subsection{Calculating returns}
+
\subsection{Using the market model}
<<>>=
data(StockPriceReturns)
@@ 170,6 +179,7 @@
er.result < excessReturn(market.name="nifty",
data.object=StockPriceReturns)
+
@
<<>>=
@@ 196,6 +206,8 @@
rM1=y3c3$NIFTY_INDEX, others=y3c3$INRUSD,
switch.to.innov=TRUE, rM1purge=TRUE, nlags=1)
+
+
## With AMM different structural periods
result2 < AMM(amm.type="all",rj=Company_A,
nlags=NA,
@@ 206,6 +218,9 @@
switch.to.innov=TRUE, rM1purge=TRUE, nlags=1)
@
+
+
+
\subsection{Converting physical dates to event frame}
The first step towards event study analysis is to convert the physical
dates to event time frame. The event date and the returns on that
More information about the Eventstudiescommits
mailing list