[Returnanalytics-commits] r3530 - pkg/PerformanceAnalytics/man

[noreply at r-forge.r-project.org]

Author: peter_carl
Date: 2014-09-12 00:16:10 +0200 (Fri, 12 Sep 2014)
New Revision: 3530

Modified:
   pkg/PerformanceAnalytics/man/PerformanceAnalytics-package.Rd
Log:
- minor changes to reflect changes in release

Modified: pkg/PerformanceAnalytics/man/PerformanceAnalytics-package.Rd
===================================================================
--- pkg/PerformanceAnalytics/man/PerformanceAnalytics-package.Rd	2014-09-11 14:33:02 UTC (rev 3529)
+++ pkg/PerformanceAnalytics/man/PerformanceAnalytics-package.Rd	2014-09-11 22:16:10 UTC (rev 3530)
@@ -39,7 +39,7 @@
 
 Performance measurement starts with returns.  Traders may object, complaining that \dQuote{You can't eat returns,} and will prefer to look for numbers with currency signs.  To some extent, they have a point - the normalization inherent in calculating returns can be deceiving.  Most of the recent work in performance analysis, however, is focused on returns rather than prices and sometimes called "returns-based analysis" or RBA.  This \dQuote{price per unit of investment} standardization is important for two reasons - first, it helps the decision maker to compare opportunities, and second, it has some useful statistical qualities.  As a result, the \kbd{PerformanceAnalytics} package focuses on returns.  See \code{\link{Return.calculate}} for converting net asset values or prices into returns, either discrete or continuous.  Many papers and theories refer to \dQuote{excess returns}: we implement a simple function for aligning time series and calculating excess returns in \code{\link{Return.excess}}.
 
-We provide two functions to calculate weighted returns for a portfolio of assets.  If you have a single weighting vector, or want the equal weighted portfolio, use \code{\link{Return.portfolio}}.  If you have a portfolio that is periodically rebalanced, and multiple time periods with different weights, use \code{\link{Return.rebalancing}}.  Both functions will subset the return series to only include returns for assets for which \code{\link{weights}} are provided.
+\code{\link{Return.portfolio}} can be used to calculate weighted returns for a portfolio of assets.  The function was recently changed to support several use-cases: a single weighting vector, an equal weighted portfolio, periodic rebalancing, or irregular rebalancing.  That replaces functionality that had been split between that function and \code{\link{Return.rebalancing}}.  The function will subset the return series to only include returns for assets for which \code{\link{weights}} are provided.
 
 Returns and risk may be annualized as a way to simplify comparison over longer time periods.  Although it requires a bit of estimating, such aggregation is popular because it offers a reference point for easy comparison.  Examples are in \code{\link{Return.annualized}}, \code{\link{sd.annualized}}, and \code{\link{SharpeRatio.annualized}}.
 
@@ -81,7 +81,7 @@
 \section{Style Analysis}{
 Style analysis is one way to help determine a fund's exposures to the changes in returns of major asset classes or other factors.  \kbd{PerformanceAnalytics} previously had a few functions that calculate style weights using an asset class style model as described in detail in Sharpe (1992).
 
-These functions have been moved to \kbd{R-Forge} in package \kbd{FactorAnalytics} as part of a new collaboration with Eric Zivot at the University of Washington. The functions combine to calculate effective style weights and display the results in a bar chart.  \code{chart.Style} calculates and displays style weights calculated over a single period. \code{chart.RollingStyle} calculates and displays those weights in rolling windows through time.  \code{style.fit} manages the calculation of the weights by method, and \code{style.QPfit} calculates the specific constraint case that requires quadratic programming.
+These functions have been moved to \kbd{R-Forge} in package \kbd{FactorAnalytics} as part of a collaboration with Eric Zivot at the University of Washington. The functions combine to calculate effective style weights and display the results in a bar chart.  \code{chart.Style} calculates and displays style weights calculated over a single period. \code{chart.RollingStyle} calculates and displays those weights in rolling windows through time.  \code{style.fit} manages the calculation of the weights by method, and \code{style.QPfit} calculates the specific constraint case that requires quadratic programming.  [note: these functions do not currently appear in the development codebase, but should reappear as a supported method at some point]
 
 There is a significant amount of academic literature on identifying and attributing sources of risk or returns.  Much of it falls into the field of \dQuote{factor analysis} where \dQuote{risk factors} are used to retrospectively explain sources of risk, and through regression and other analytical methods \emph{predict} future period returns and risk based on factor drivers.  These are well covered in chapters on factor analysis in \cite{Zivot and Wang(2006)} and also in the \R functions \code{\link{factanal}} for basic factor analysis and \code{\link{princomp}} for Principal Component Analysis.  The authors feel that financial engineers and analysts would benefit from some wrapping of this functionality focused on finance, but the capabilities already available from the base functions are quite powerful.  We are hopeful that our new collaboration with Prof. Zivot will provide additional functionality in the near future.
 }
@@ -264,10 +264,12 @@
 
 Jeff Ryan and Josh Ulrich are active participants in the R finance community and created \code{\link[xts]{xts}}, upon which much of PerformanceAnalytics depends.
 
-Prototypes of the drawdowns functionality were provided by Sankalp Upadhyay, and modified with permission. Stephan Albrecht provided detailed feedback on the Getmansky/Lo Smoothing Index.  Diethelm Wuertz provided prototypes of modified VaR and skewness and kurtosis functions (and is of course the maintainer of the RMetrics suite of pricing and optimization functions).  Any errors are, of course, our own.
+Prototypes of the drawdowns functionality were provided by Sankalp Upadhyay, and modified with permission. Stephan Albrecht provided detailed feedback on the Getmansky/Lo Smoothing Index.  Diethelm Wuertz provided prototypes of modified VaR and skewness and kurtosis functions (and is of course the maintainer of the RMetrics suite of pricing and optimization functions).  He also contributed prototypes for many other functions from Bacon's book that were incorporated into PerformanceAnalytics by Matthieu Lestel.  Any errors are, of course, our own.
 
 Thanks to Joe Wayne Byers and Dirk Eddelbuettel for comments on early versions of these functions, and to Khanh Nguyen and Ryan Sheftel for careful testing and detailed problem reports.
 
+Thanks also to our Google Summer of Code students through the years for their contributions.  Significant contributions from GSOC students to this package have come from Matthieu Lestel and Andrii Babii so far.  We expect to eventually incorporate contributions from Pulkit Mehrotra and Shubhankit Mohan, who worked with us during the summer of 2013.
+
 Thanks to the R-SIG-Finance community without whom this package would not be possible.  We are indebted to the R-SIG-Finance community for many helpful suggestions, bugfixes, and requests.
 }