#R code used in Module: Introduction and Measuring Risk and Return
#Installing packages as needed
#NOTE: This statement essentially checks if PerformanceAnalytics package is available
#locally in your R library distribution. If not, it will install it and then include it
#as a part of this code, so that we can use its functions and features
if (!require(PerformanceAnalytics)) install.packages("PerformanceAnalytics")
## Loading required package: PerformanceAnalytics
## Warning: package 'PerformanceAnalytics' was built under R version 4.1.3
## Loading required package: xts
## Warning: package 'xts' was built under R version 4.1.3
## Loading required package: zoo
## Warning: package 'zoo' was built under R version 4.1.3
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
##
## Attaching package: 'PerformanceAnalytics'
## The following object is masked from 'package:graphics':
##
## legend
if (!require(xts)) install.packages("xts")
if (!require(lubridate)) install.packages("lubridate")
## Loading required package: lubridate
## Warning: package 'lubridate' was built under R version 4.1.2
##
## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, union
#Package Details
#1)Performanceanalytics: A very useful package for investment and financial performance and risk
#analytics. Official Documentation: https://www.rdocumentation.org/packages/PerformanceAnalytics/versions/1.5.3
#Presentation Deck by Package Founders: http://past.rinfinance.com/RinFinance2009/presentations/PA%20Workshop%20Chi%20RFinance%202009-04.pdf
#Quick Video on calculating returns: https://www.youtube.com/watch?v=0rAVPUNf9yI
#2) xts: xts is a useful packge useful in time-series analysis. We use xts package here since
#PerformanceAnalytics functions usually require xts objects (time-series of prices etc.) rather than simple
#lists of prices for more accurate performance evaluation
#3) lubridate: lubridate is a date mainpulation package. We use mdy() function of lubridate to standardize dates of our data
#Useful Resource: https://raw.githubusercontent.com/rstudio/cheatsheets/master/lubridate.pdf
# calling libraries
library(PerformanceAnalytics)
library(xts)
library(lubridate)
############### Video 1: Simple and Compound Interest
#Before running this code - please make sure that contrafund.csv is in your working directory
#use getwd() to find out your working directory and setwd() to set it to any directory you want
getwd()
## [1] "C:/Users/shant/OneDrive/Documents/GA Tech/MGT 6203/Module 6/R code and data files"
setwd('C:/Users/shant/OneDrive/Documents/GA Tech/MGT 6203/Module 6/R code and data files')
#load data and create xts dataset
fund <- read.csv("contrafund.csv")
#converting dates to standard YYYY-MM-DD format
fund$Date <- mdy(fund$Date)
#Sorting data by dates
fund2<- fund[order(fund$Date),]
#create an xts dataset
All.dat <- xts(fund2[,-1],order.by = fund2[,1],)
#Calculate Compound Return for the fund across all the data
Return.cumulative(All.dat$ContraRet,geometric = TRUE)
## ContraRet
## Cumulative Return 141.5677
#Cumulative Returns chart over time
#Check chart in Plots Tab on bottom right in R Studio
chart.CumReturns(All.dat$ContraRet,wealth.index = FALSE, geometric = TRUE)
############### Video 2: Measuring Risk
#Descriptive Statistics of the fund returns
#IMPORTANT NOTE - Arithmetic mean and standard deviation of returns are reported incorrectly
#The correct values as noticed when this code is run are:
#Arithmetic Mean Return = 1.17% and Standard Deviation = 4.32% per month
table.Stats(All.dat$ContraRet)
## ContraRet
## Observations 462.0000
## NAs 0.0000
## Minimum -0.2888
## Quartile 1 -0.0128
## Median 0.0125
## Arithmetic Mean 0.0117
## Geometric Mean 0.0108
## Quartile 3 0.0387
## Maximum 0.1475
## SE Mean 0.0020
## LCL Mean (0.95) 0.0078
## UCL Mean (0.95) 0.0157
## Variance 0.0019
## Stdev 0.0432
## Skewness -0.7924
## Kurtosis 5.5070
#Beta and R-squared estimation:
Mod1 = lm(ContraRet ~ Market.Return,data= All.dat)
summary(Mod1)
##
## Call:
## lm(formula = ContraRet ~ Market.Return, data = All.dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.087523 -0.009502 0.001399 0.010316 0.060957
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0025419 0.0008485 2.996 0.00288 **
## Market.Return 0.9004602 0.0188912 47.666 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01776 on 460 degrees of freedom
## Multiple R-squared: 0.8316, Adjusted R-squared: 0.8313
## F-statistic: 2272 on 1 and 460 DF, p-value: < 2.2e-16
#As seen in model, beta = 0.9005 , Adj. R-sq = 0.8313
#Drawdowns
chart.Drawdown(All.dat$ContraRet)
table.Drawdowns(All.dat$ContraRet,top = 5,digits = 4)
## From Trough To Depth Length To Trough Recovery
## 1 2007-11-30 2009-02-28 2012-02-29 -0.4634 52 16 36
## 2 1987-09-30 1987-11-30 1989-04-30 -0.3416 20 3 17
## 3 2000-04-30 2003-02-28 2004-11-30 -0.3324 56 35 21
## 4 1983-05-31 1984-07-31 1985-03-31 -0.2214 23 15 8
## 5 1981-06-30 1982-07-31 1982-10-31 -0.1952 17 14 3
##End of Code