% $Id: PerformanceAnalyticsChartsPresentation-Meielisalp-2007.Rnw 3541 2014-09-15 09:39:58Z braverock $

\documentclass{beamer}

% may need full path to find these .sty files outside of R environment
\usepackage{Sweave}
% \usepackage{/usr/lib/R/share/texmf/Sweave}

\SweaveOpts{engine=R,eps=FALSE}
%\VignetteIndexEntry{PerformanceAnalytics Charts and Tables Presentation - Meielisalp - 2007}
%\VignetteDepends{PerformanceAnalytics}
%\VignetteKeywords{returns, performance, risk, benchmark, portfolio}
%\VignettePackage{PerformanceAnalytics}

% Contents Copyright 2007 Peter Carl and Brian G. Peterson
%
% This file can be redistributed and/or modified under
% the terms of the GNU Public License, version 2.

\mode<presentation>
{
%   \usetheme{whatever}
}

\makeatletter
%%%
\usepackage{subfigure}


\usepackage[english]{babel}
\makeatother

\usepackage[latin1]{inputenc}

\usepackage{times}
\usepackage[T1]{fontenc}


\title[PerformanceAnalytics] % (optional, use only with long paper titles)
{Overview of PerformanceAnalytics' \\ Charts and Tables}
% \subtitle

\author[Brian Peterson]
{Brian~G.~Peterson}

\institute[Diamond] % (optional, but mostly needed)
{
  Diamond Management \& Technology Consultants \\
  Chicago, IL \\
  brian@braverock.com
}

\date[July 2007] % (optional, should be abbreviation of conference name)
{R/Rmetrics User and Developer Workshop, 2007}

\subject{Overview of PerformanceAnalytics' Charts and Tables}

\AtBeginSubsection[]
{
  \begin{frame}<beamer>
    \frametitle{Outline}
    \tableofcontents[currentsection,currentsubsection]
  \end{frame}
}


\begin{document}
% this has to be after \begin{document} to work
\setkeys{Gin}{width=2.5in}
\begin{frame}
  \titlepage
\end{frame}

<<LoadLibrary,echo=F,results=hide>>=
library('PerformanceAnalytics')
@

\begin{frame}
  \frametitle{Outline}
  \tableofcontents
  % You might wish to add the option [pausesections]
\end{frame}

\section{Introduction}

%\subsection{Objectives}

\begin{frame}
  \frametitle{Overview}

  \begin{itemize}
    \item
    Utilize charts and tables to display and analyze data:
      \begin{itemize}
        \item
          asset returns
        \item
          compare an asset to other similar assets
        \item
          compare an asset to one or more benchmarks
      \end{itemize}
    \item
    Utilize common performance and risk measures to aid the investment decision
    \item
    Examples developed using data for six (hypothetical) managers, a peer index, and an asset class index
    \item
    Hypothetical manager data developed from real manager timeseries using \emph{accuracy} and \emph{perturb} packages to perturb data maintaining the statistical distribution properties of the original data.
  \end{itemize}
\end{frame}



\section{Set Up PerformanceAnalytics}

\begin{frame}[fragile]
  \frametitle{Install PerformanceAnalytics.}
  \begin{itemize}
      \item
        As of version 0.9.4, PerformanceAnalytics is available in CRAN
      \item
        Version 0.9.5 was released at the beginning of July
      \item
        Install with: \linebreak \texttt{>
        install.packages("PerformanceAnalytics")}
      \item
        Required packages include \texttt{Hmisc}, \texttt{zoo}, and Rmetrics packages such as \texttt{fExtremes}.
      \item
        Load the library into your active R session
  using: \linebreak \texttt{> library("PerformanceAnalytics")}.
  \end{itemize}
\end{frame}

\begin{frame}[fragile]
  \frametitle{Load and Review Data.}
        % The noweb code must be flush left
        \begin{figure}
        \begin{scriptsize}
<<LoadData,echo=T>>=
data(managers)
head(managers)
@
        \end{scriptsize}
        \end{figure}

\end{frame}


\section{Review Performance}

%\subsection{Review Performance}

\begin{frame}[fragile]
  \frametitle{Set Up Data for Analysis.}
  \begin{figure}

  \begin{center}
     \begin{scriptsize}
<<CalcDataDimensions,echo=T>>=
dim(managers)
managers.length = dim(managers)[1]
colnames(managers)
manager.col = 1
peers.cols = c(2,3,4,5,6)
indexes.cols = c(7,8)
Rf.col = 10
#factors.cols = NA
trailing12.rows = ((managers.length - 11):managers.length)
trailing12.rows
trailing36.rows = ((managers.length - 35):managers.length)
trailing60.rows = ((managers.length - 59):managers.length)
#assume contiguous NAs - this may not be the way to do it na.contiguous()?
frInception.rows = (length(managers[,1]) -
length(managers[,1][!is.na(managers[,1])]) + 1):length(managers[,1])
@
     \end{scriptsize}
  \end{center}
  \end{figure}
\end{frame}

\begin{frame}[fragile]
  \frametitle{Draw a Performance Summary Chart.}
  \begin{figure}

\centering
\begin{scriptsize}
<<Graph1,echo=T,fig=T>>=
charts.PerformanceSummary(managers[,c(manager.col,indexes.cols)],
colorset=rich6equal, lwd=2, ylog=TRUE)
@
\end{scriptsize}

\end{figure}

\end{frame}

\begin{frame}[fragile]
  \frametitle{Show Calendar Performance.}
  \begin{figure}
    \begin{scriptsize}

<<CalendarReturns,echo=T>>=
t(table.CalendarReturns(managers[,c(manager.col,indexes.cols)]))
@

    \end{scriptsize}
  \end{figure}
\end{frame}


\begin{frame}[fragile]
  \frametitle{Calculate Statistics.}
  \begin{scriptsize}
<<MonthlyReturnStats,echo=T>>=
table.Stats(managers[,c(manager.col,peers.cols)])
@
  \end{scriptsize}
\end{frame}


\begin{frame}[fragile]
  \frametitle{Compare Distributions.}
  \begin{scriptsize}
  \begin{figure}
  \centering
<<Graph10,echo=T,fig=T>>=
chart.Boxplot(managers[ trailing36.rows, c(manager.col, peers.cols,
indexes.cols)], main = "Trailing 36-Month Returns")
@
  \end{figure}
  \end{scriptsize}
\end{frame}


\begin{frame}[fragile]
  \frametitle{Compare Distributions.}
  \begin{figure}
  \begin{tiny}
<<Graph13,echo=T,fig=T>>=
layout(rbind(c(1,2),c(3,4)))
chart.Histogram(managers[,1,drop=F], main = "Plain", methods = NULL)
chart.Histogram(managers[,1,drop=F], main = "Density", breaks=40,
methods = c("add.density", "add.normal"))
chart.Histogram(managers[,1,drop=F], main = "Skew and Kurt", methods = c
("add.centered", "add.rug"))
chart.Histogram(managers[,1,drop=F], main = "Risk Measures", methods = c
("add.risk"))
@
  \end{tiny}
  \end{figure}
\end{frame}


\begin{frame}[fragile]
  \frametitle{Show Relative Return and Risk.}
  \begin{figure}
  \centering
  \begin{scriptsize}
<<Graph3,echo=T,fig=T>>=
chart.RiskReturnScatter(managers[trailing36.rows,1:8], Rf=.03/12, main =
"Trailing 36-Month Performance", colorset=c("red", rep("black",5), "orange",
"green"))
@
  \end{scriptsize}

  \end{figure}

\end{frame}


\begin{frame}[fragile]
  \frametitle{Calculate Statistics.}
  \begin{figure}
\centering
\begin{scriptsize}
<<MonthlyReturnStats,echo=T>>=
table.Stats(managers[,c(manager.col,peers.cols)])
@
\end{scriptsize}
\end{figure}
\end{frame}


\begin{frame}[fragile]
  \frametitle{Examine Performance Consistency.}
  \begin{figure}
\centering
\begin{scriptsize}

<<Graph5,echo=T,fig=T>>=
charts.RollingPerformance(managers[, c(manager.col, peers.cols,
indexes.cols)], Rf=.03/12, colorset = c("red", rep("darkgray",5), "orange",
"green"), lwd = 2)
@

\end{scriptsize}
\end{figure}
\end{frame}


\begin{frame}[fragile]
  \frametitle{Display Relative Performance.}
  \begin{figure}
\centering
\begin{scriptsize}

<<Graph6,echo=T,fig=T>>=
chart.RelativePerformance(managers[ , manager.col, drop = FALSE],
managers[ , c(peers.cols, 7)], colorset = tim8equal[-1], lwd = 2, legend.loc
= "topleft")
@

\end{scriptsize}
\end{figure}
\end{frame}



\begin{frame}[fragile]
  \frametitle{Compare to a Benchmark.}
  \begin{figure}
\centering
\begin{scriptsize}

<<Graph6a,echo=T,fig=T>>=
chart.RelativePerformance(managers[ , c(manager.col, peers.cols) ],
managers[, 8, drop=F], colorset = rainbow8equal, lwd = 2, legend.loc =
"topleft")
@

\end{scriptsize}
\end{figure}
\end{frame}


\begin{frame}[fragile]
\frametitle{Compare to a Benchmark.}
\begin{figure}
\centering
\begin{tiny}
<<tableCAPM,echo=T>>=
table.CAPM(managers[trailing36.rows, c(manager.col, peers.cols)], managers[ trailing36.rows, 8, drop=FALSE], Rf = managers[ trailing36.rows, Rf.col, drop=F])
@
\end{tiny}
\end{figure}
\end{frame}

\begin{frame}
  \frametitle{table.CAPM underlying techniques}
  \begin{itemize}
    \item
    Return.annualized --- Annualized return using \\
     \begin{equation}
        prod(1+R_{a})^{\frac{scale}{n}}-1=\sqrt[n]{prod(1+R_{a})^{scale}}-1
     \end{equation}
    \item
    TreynorRatio --- ratio of asset's Excess Return to Beta $\beta$ of the benchmark
    \begin{equation}
        \frac{(\overline{R_{a}-R_{f}})}{\beta_{a,b}}
    \end{equation}
    \item
    ActivePremium --- investment's annualized return minus the benchmark's annualized return
    \item
    Tracking Error --- A measure of the unexplained portion of performance relative to a benchmark, given by \\
    \begin{equation}
        TrackingError = \sqrt{\sum\frac{(R_{a}-R_{b})^{2}}{len(R_{a})\sqrt{scale}}}
    \end{equation}
    \item
    InformationRatio --- ActivePremium/TrackingError
  \end{itemize}
\end{frame}

\begin{frame}[fragile]
\frametitle{Compare to a Benchmark.}
\begin{figure}
\centering
\begin{scriptsize}

<<Graph8,echo=T,fig=T>>=
#source("PerformanceAnalytics/R/Return.excess.R")
charts.RollingRegression(managers[, c(manager.col, peers.cols), drop =
FALSE], managers[, 8, drop = FALSE], Rf = .03/12, colorset = redfocus, lwd =
2)
@

\end{scriptsize}
\end{figure}
\end{frame}



\begin{frame}[fragile]
  \frametitle{Calculate Downside Risk.}
  \begin{figure}
\centering
\begin{scriptsize}

<<tableDownside,echo=T>>=
table.DownsideRisk(managers[,1:6],Rf=.03/12)
@

\end{scriptsize}
\end{figure}
\end{frame}

\begin{frame}
    \frametitle{Semivariance and Downside Deviation}
    \begin{itemize}
        \item
        Downside Deviation as proposed by Sharpe is a generalization of semivariance which calculates bases on the deviation below a Minimumn Acceptable Return(MAR)
        \begin{equation}
            \delta_{MAR} = \sqrt{ \frac{\sum^{n}_{t=1}(R_{t} - MAR)^{2}}{n} }
        \end{equation}
        \item
        Downside Deviation may be used to calculate semideviation by setting MAR=mean(R) or may also be used with MAR=0
        \item
        Downside Deviation (and its special cases semideviation and semivariance) is useful in several performance to risk ratios, and in several portfolio optimization problems.
    \end{itemize}
\end{frame}

\begin{frame}[shrink=10]
    \frametitle{Value at Risk}
    \begin{itemize}
        \item
        Value at Risk (VaR) has become a required standard risk measure recognized by Basel II and MiFID
        \item
        traditional mean-VaR may be derived historically, or estimated parametrically using \\
           \begin{equation}
                z_{c} = q_{p}=qnorm(p)
            \end{equation}
            \begin{equation}
                VaR=\bar{R} -  z_{c} \cdot \sqrt{\sigma}
            \end{equation}
        \item
        even with robust covariance matrix or Monte Carlo simulation, mean-VaR is not reliable for non-normal asset distributions
        \item
        for non-normal assets, VaR estimates calculated using GPD (as in VaR.GPD) or Cornish Fisher perform best
        \item
        modified Cornish Fisher VaR takes higher moments of the distribution into account: \\
           \begin{equation}
                z_{cf}=z_{c}+\frac{(z_{c}^{2}-1)S}{6}+\frac{(z_{c}^{3}-3z_{c})K}{24}+\frac{(2z_{c}^{3}-5z_{c})S^{2}}{36}
            \end{equation}
            \begin{equation}
                modVaR =\bar{R} - z_{cf}\sqrt{\sigma}
            \end{equation}
        \item
        modified VaR also meets the definition of a coherent risk measure per Artzner,et.al.(1997)
    \end{itemize}
\end{frame}

\begin{frame}[shrink=10]
  \frametitle{Risk/Reward Ratios in \emph{PerformanceAnalytics}}

  \begin{itemize}
    \item
    SharpeRatio --- return per unit of risk represented by variance, may also be annualized by \\
    \begin{equation}
        \frac{\sqrt[n]{prod(1+R_{a})^{scale}}-1}{\sqrt{scale}\cdot\sqrt{\sigma}}
    \end{equation}
    \item
    Sortino Ratio --- improvement on Sharpe Ration utilizing downside deviation as the measure of risk \\
    \begin{equation}
        \frac{(\overline{R_{a} - MAR})}{\delta_{MAR}}
    \end{equation}
    \item
    Calmar and Sterling Ratios --- ratio of annualized return (Eq. 1) over the absolute value of the maximum drawdown
    \item
    Sortino's Upside Potential Ratio --- upside semdiviation from MAR over downside deviation from MAR
    \begin{equation}
        \frac{ \sum^{n}_{t=1} (R_{t} - MAR) }{ \delta_{MAR} }
    \end{equation}
    \item
    Favre's modified Sharpe Ratio --- ratio of excess return over Cornish-Fisher VaR
    \begin{equation}
        \frac{(\overline{R_{a}-R_{f}})}{modVaR_{R_{a},p}}
    \end{equation}
    \item
    \emph{NOTE}: The newest measures such as modified Sharpe and Sortino's UPR are far more reliable than older measures, but everyone still seems to look at older measures.
  \end{itemize}
\end{frame}

\section{Summary}

\begin{frame}[fragile]
  \frametitle<presentation>{Summary}

  \begin{itemize}
    \item
        Performance and Risk analysis are greatly facilitated by the use of charts and tables.
    \item
        The display of your infomation is in many cases as important as the analysis.
    \item
        The observer should have gained a working knowledge of how specific visual techniques may be utilized to aid investment decision making.
  \end{itemize}

  % The following outlook is optional.
  \vskip0pt plus.5fill
  \begin{itemize}
  \item
    Further Work
    \begin{itemize}
    \item
      Additional parameterization to make charts and tables more useful.
    \item
      Pertrac or Morningstar-style sample reports.
    \item
      Functions and graphics for more complicated topics such as factor analysis and optimization.
    \end{itemize}
  \end{itemize}
  \begin{figure}
    \centering
%       \subfigure
%           {
%             \includegraphics[totalheight=0.1\textheight,width=.36\textwidth]{Diamond_Logo_Tag.jpg}
%           }
      \hfill
      \subfigure
          {
            \includegraphics[totalheight=0.15\textheight,width=.15\textwidth]{Rlogo.jpg}
          }
  \end{figure}
\end{frame}

\end{document}
