returns {monomvn} | R Documentation

## Financial Returns data from NYSE and AMEX

### Description

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

### Usage

    data(returns)
    data(returns.test)
    data(market)
    data(market.test)

### Format

The returns provided are collected in a `data.frame` with 1168 columns, and 360 rows in the case of `returns` and 12 rows for `returns.test`. The columns are uniquely coded to identify the stock traded on NYSE or AMEX. The market return is in two vectors `market` and `market.test` of length 360 and 12, respectively

### Details

The columns contain monthly returns of common domestic stocks traded on the NYSE and the AMEX from April 1968 until 1998. `returns` contains returns up until 1997, whereas `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

### Source

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 monomvn package as an example of a real world data set following a nearly monotone missingness pattern

### References

Louis K. Chan, Jason Karceski, and Josef Lakonishok (1999). On Portfolio Optimization: Forecasting Covariances and Choosing the Risk Model. The Review of Financial Studies. 12(5), 937-974

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

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

http://www.statslab.cam.ac.uk/~ bobby/monomvn.html

### See Also

`monomvn`, `bmonomvn`

### Examples

    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

[Package monomvn version 1.8 Index]