# [Eventstudies-commits] r116 - pkg/vignettes

Wed Aug 7 13:05:59 CEST 2013

Author: renukasane
Date: 2013-08-07 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	2013-08-06 15:46:54 UTC (rev 115)
+++ pkg/vignettes/eventstudies.Rnw	2013-08-07 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.R-project.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 event-time, 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.R-project.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 day-0 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 data-set 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 time-series of stock returns. The second argument is
+\texttt{market.name}, a column name indicating market returns.
+
+The output from these two models is another time-series 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 @@
@

+\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