\HeaderA{returns}{Financial Returns data from NYSE and AMEX}{returns}
\aliasA{market}{returns}{market}
\methaliasA{market.test}{returns}{market.test}
\methaliasA{returns.test}{returns}{returns.test}
\keyword{datasets}{returns}
\begin{Description}\relax
Monthly returns of common domestic stocks traded on the NYSE and the
AMEX from April 1968 until 1998; also contains the return to the market
\end{Description}
\begin{Usage}
\begin{verbatim}
data(returns)
data(returns.test)
data(market)
data(market.test)
\end{verbatim}
\end{Usage}
\begin{Format}\relax
The returns provided are collected in a \code{data.frame} with
1168 columns, and 360 rows in the case of \code{returns} and 12
rows for \code{returns.test}. The columns are uniquely coded to
identify the stock traded on NYSE or AMEX.  The market return
is in two vectors \code{market} and \code{market.test}
of length 360 and 12, respectively
\end{Format}
\begin{Details}\relax
The columns contain monthly returns of common domestic stocks traded
on the NYSE and the AMEX from April 1968 until 1998. \code{returns}
contains returns up until 1997, whereas \code{returns.test} has the
returns for 1997.  Both data sets have been cleaned in the following
way.  All stocks have a share price greater than \$5 and a market
capitalization greater than 20\% based on the size distribution of
NYSE firms.  Stocks without completely observed return
series in 1997 were also discarded.

The market returns provided are essentially the monthly return on the
S\&P500 during the same period, which is highly correlated with the
raw monthly returns weighted by their market capitalization
\end{Details}
\begin{Source}\relax
This data is a subset of that originally used by Chan, Karceski,
and Lakonishok (1999), and subsequently by several others;
see the references below.  We use it as part of the \pkg{monomvn}
package as an example of a real world data set following a
nearly monotone missingness pattern
\end{Source}
\begin{References}\relax
Louis K. Chan, Jason Karceski, and Josef Lakonishok (1999).
\emph{On Portfolio Optimization: Forecasting Covariances and
Choosing the Risk Model}. The Review of Financial Studies.
\bold{12}(5), 937-974

Ravi Jagannathan and Tongshu Ma (2003).
\emph{Risk Reduction in Large Portfolios: Why Imposing the
Wrong Constraints Helps}. Journal of Finance, American Finance
Association. \bold{58}(4), 1641-1684

Robert B. Gramacy, Joo Hee Lee, and Ricardo Silva (2008).
\emph{On estimating covariances between many assets with histories 
of highly variable length}. \\ Preprint available on arXiv:0710.5837:
\url{http://arxiv.org/abs/0710.5837}

\url{http://www.statslab.cam.ac.uk/~ bobby/monomvn.html}
\end{References}
\begin{SeeAlso}\relax
\code{\LinkA{monomvn}{monomvn}}, \code{\LinkA{bmonomvn}{bmonomvn}}
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
data(returns)

## investigate the monotone missingness pattern
returns.na <- is.na(returns)
image(1:ncol(returns), 1:nrow(returns), t(returns.na))

## for a portfolio balancing exercise, see
## the example in the bmonomvn help file
\end{ExampleCode}
\end{Examples}

