In yezhaoqin/morningStarAnalytics: Morning Star Mutual Fund Data Analysis & Visualization
knitr::opts_chunk$set(echo = TRUE)
library(DataComputing)
library(printr)
Sys.setlocale('LC_ALL','C')
Golbal Configuration
filePath <- "~/Desktop/morningstar.csv"
sysKeyword <- "(Summary|Percentile|Sum|Average|Count|Maximum|Minimum|Median|Deviation)"
msciKeyword <-"MSCI"
ftseKeyword <-"(FTSE|Russell)"
stoxxKeyword <-"STOXX"
spdjKeyword <-"(S&P|Dow|DJ)"
esgKeywords <- '(Sustain|ESG|esg|SRI|sri|Social|Governance|Catholic|Ethical)'
envKeywords <-'(Water|Carbon|Climate|Enviro|Green|Energy|Renew|Tech|Fossil|Alternative|Clean|Fuel|Pollution|Prevention)'
blackrockKeyword <- '(iShare|BlackRock|BLK|Blackrock)'
sumF <- function(vec) {sum(as.numeric(gsub(",", "", as.character(vec))), na.rm=TRUE)}
toP <- function(c,d) {paste(as.character(c), "(",as.character(round(d*100,digits=1)),"%)",sep="")}
"^" <- function(x,y) ifelse(y==0,0,base:::"/"(x,y))
provider <- c("MSCI","FTSE Russell","STOXX","SPDJ")
esgIndexTotal <- c(82,5,35,67)
envIndexTotal <- c(7,34,5,11)
myPalette <- c("#722287","#0079C1","#009941","#002960")
fillTheme <- scale_fill_manual(values=myPalette)
colorTheme <- scale_color_manual(values=myPalette)
rawTable <- read.csv(filePath, header=TRUE, sep=",")
universe <- rawTable[!duplicated(rawTable[c('Name')]),] %>%
filter(Name!="") %>%
filter(!is.na(Oldest.Share.Class)) %>%
filter(!grepl(sysKeyword, Name))
MSCI <- universe %>% filter(grepl(msciKeyword,Primary.Prospectus.Benchmark))
FTSE <- universe %>% filter(grepl(ftseKeyword,Primary.Prospectus.Benchmark))
STOXX <- universe %>% filter(grepl(stoxxKeyword,Primary.Prospectus.Benchmark))
SPDJ <- universe %>% filter(grepl(spdjKeyword,Primary.Prospectus.Benchmark))
fundsCount <- c(length(MSCI$Name),length(FTSE$Name),length(STOXX$Name),length(SPDJ$Name))
fundsPercent <- fundsCount/sum(fundsCount)
AUM <- c(sumF(MSCI$Fund.Size.USD),sumF(FTSE$Fund.Size.USD),sumF(STOXX$Fund.Size.USD),sumF(SPDJ$Fund.Size.USD))
countTable <- data.frame(provider=provider, fundsCount=fundsCount, fundsPercent=fundsPercent, AUM=AUM)
\newpage
Analysis of funds tracking each provider
r countTable
Count bar chart
countTable %>%
ggplot(aes_string(x='provider', y='fundsCount', fill='provider')) +
geom_bar(stat="identity") +
geom_label(fill='white',aes(label=toP(fundsCount,fundsPercent))) +
fillTheme
AUM bar chart
countTable %>%
ggplot(aes(x=provider, y=AUM, fill=provider)) +
geom_bar(stat="identity") +
geom_label(fill='white',aes(label=toP(paste(as.character(round(AUM/1000000000, digits=2)),"B", sep=""), AUM/sum(AUM)))) +
fillTheme
Count pie chart
countTable %>%
ggplot(aes(x=factor(1), y=fundsPercent, fill=provider)) +
geom_bar(width=1, stat="identity") +
coord_polar(theta="y") +
theme(axis.text=element_blank(), axis.title=element_blank(), panel.background=element_blank()) +
fillTheme
AUM pie chart
countTable %>%
ggplot(aes(x=factor(1), y=AUM/sum(AUM), fill=provider)) +
geom_bar(width=1, stat="identity") +
coord_polar(theta="y") +
theme(axis.text=element_blank(), axis.title=element_blank(), panel.background=element_blank()) +
fillTheme
\newpage
Analysis of funds tracking ESG indexes of each provider
universeesg <- universe %>% filter(grepl(esgKeywords,Primary.Prospectus.Benchmark))
MSCIesg <- MSCI %>% filter(grepl(esgKeywords,Primary.Prospectus.Benchmark))
FTSEesg <- FTSE %>% filter(grepl(esgKeywords,Primary.Prospectus.Benchmark))
STOXXesg <- STOXX %>% filter(grepl(esgKeywords,Primary.Prospectus.Benchmark))
SPDJesg <- SPDJ %>% filter(grepl(esgKeywords,Primary.Prospectus.Benchmark))
esgCount <- c(length(MSCIesg$Name),length(FTSEesg$Name),length(STOXXesg$Name),length(SPDJesg$Name))
esgPercent <- esgCount/sum(esgCount)
esgAUM <- c(sumF(MSCIesg$Fund.Size.USD),sumF(FTSEesg$Fund.Size.USD),sumF(STOXXesg$Fund.Size.USD),sumF(SPDJesg$Fund.Size.USD))
esgTable <- data.frame(provider=provider, fundsCount=fundsCount, fundsPercent=fundsPercent, AUM=AUM, esgCount=esgCount, esgPercent=esgPercent, esgAUM=esgAUM, esgRatio=esgCount/fundsCount, aumRatio=esgAUM/AUM)
r select(esgTable, provider, esgCount, esgPercent, esgAUM)
Count bar chart
esgTable %>%
ggplot(aes(x=provider, y=esgCount, fill=provider)) +
geom_bar(stat="identity") +
geom_label(fill='white',aes(label=toP(esgCount, esgPercent))) +
fillTheme
AUM bar comparison
esgTable %>%
ggplot(aes(x=provider, y=esgAUM, fill=provider)) +
geom_bar(stat="identity") +
geom_label(fill='white',aes(label=toP(paste(as.character(round(esgAUM/1000000000, digits=2)),"B", sep=""), esgAUM/sum(esgAUM)))) +
fillTheme
Count pie chart
esgTable %>%
ggplot(aes(x=factor(1), y=esgPercent, fill=provider)) +
geom_bar(width=1, stat="identity") +
coord_polar(theta="y") +
theme(axis.text=element_blank(), axis.title=element_blank(), panel.background=element_blank()) +
fillTheme
AUM pie chart
esgTable %>%
ggplot(aes(x=factor(1), y=esgAUM/sum(esgAUM), fill=provider)) +
geom_bar(width=1, stat="identity") +
coord_polar(theta="y") +
theme(axis.text=element_blank(), axis.title=element_blank(), panel.background=element_blank()) +
fillTheme
\newpage
Percentage of funds tracking ESG indexes for each provider
total tracking ESG count: r sum(esgTable$esgCount)
total funds count: r sum(esgTable$fundsCount)
percentage count of total: r sum(esgTable$esgCount)/sum(esgTable$fundsCount)
percentage AUM of total: r sum(esgTable$esgAUM)/sum(esgTable$AUM)
Count ratio for each provider
esgTable %>%
ggplot(aes(x=provider, y=esgRatio, fill=provider)) +
geom_bar(stat="identity") +
geom_label(fill='white',aes(label=round(esgRatio, digits=4))) +
fillTheme
AUM ratio for each provider
esgTable %>%
ggplot(aes(x=provider, y=aumRatio, fill=provider)) +
geom_bar(stat="identity") +
geom_label(fill='white',aes(label=round(aumRatio, digits=4))) +
fillTheme
\newpage
Analysis of funds tracking Environment indexes of each provider
universeenv <- universe %>% filter(grepl(envKeywords,Primary.Prospectus.Benchmark))
MSCIenv <- MSCI %>% filter(grepl(envKeywords,Primary.Prospectus.Benchmark))
FTSEenv <- FTSE %>% filter(grepl(envKeywords,Primary.Prospectus.Benchmark))
STOXXenv <- STOXX %>% filter(grepl(envKeywords,Primary.Prospectus.Benchmark))
SPDJenv <- SPDJ %>% filter(grepl(envKeywords,Primary.Prospectus.Benchmark))
envCount <- c(length(MSCIenv$Name),length(FTSEenv$Name),length(STOXXenv$Name),length(SPDJenv$Name))
envPercent <- envCount/sum(envCount)
envAUM <- c(sumF(MSCIenv$Fund.Size.USD),sumF(FTSEenv$Fund.Size.USD),sumF(STOXXenv$Fund.Size.USD),sumF(SPDJenv$Fund.Size.USD))
envTable <- data.frame(provider=provider, fundsCount=fundsCount, fundsPercent=fundsPercent, AUM=AUM, envCount=envCount, envPercent=envPercent, envAUM=envAUM, envRatio=envCount/fundsCount, aumRatio=envAUM/AUM)
r select(envTable, provider, envCount, envPercent, envAUM)
Count bar chart
envTable %>%
ggplot(aes(x=provider, y=envCount, fill=provider)) +
geom_bar(stat="identity") +
geom_label(fill='white',aes(label=toP(envCount, envPercent))) +
fillTheme
AUM bar chart
envTable %>%
ggplot(aes(x=provider, y=envAUM, fill=provider)) +
geom_bar(stat="identity") +
geom_label(fill='white',aes(label=toP(paste(as.character(round(envAUM/1000000000, digits=2)),"B", sep=""), envAUM/sum(envAUM)))) +
fillTheme
Count pie chart
envTable %>%
ggplot(aes(x=factor(1), y=envPercent, fill=provider)) +
geom_bar(width=1, stat="identity") +
coord_polar(theta="y") +
theme(axis.text=element_blank(), axis.title=element_blank(), panel.background=element_blank()) +
fillTheme
AUM pie chart
envTable %>%
ggplot(aes(x=factor(1), y=envAUM/sum(envAUM), fill=provider)) +
geom_bar(width=1, stat="identity") +
coord_polar(theta="y") +
theme(axis.text=element_blank(), axis.title=element_blank(), panel.background=element_blank()) +
fillTheme
\newpage
Percentage of Environment funds tracking Environment indexes for each provider
total tracking Environment count: r sum(envTable$envCount)
total funds count: r sum(envTable$fundsCount)
percentage count of total: r sum(envTable$envCount)/sum(envTable$fundsCount)
percentage AUM of total: r sum(envTable$envAUM)/sum(envTable$AUM)
Count ratio for each provider
envTable %>%
ggplot(aes(x=provider, y=envRatio, fill=provider)) +
geom_bar(stat="identity") +
geom_label(fill='white',aes(label=round(envRatio, digits=4))) +
fillTheme
AUM ratio for each provider
envTable %>%
ggplot(aes(x=provider, y=aumRatio, fill=provider)) +
geom_bar(stat="identity") +
geom_label(fill='white',aes(label=round(aumRatio, digits=4))) +
fillTheme
\newpage
ESG & Environment cross comparison
ESG/Environment (# of Indexes) Ratio
data.frame(esg=esgIndexTotal, env=envIndexTotal, ratio=esgIndexTotal/envIndexTotal) %>%
ggplot(aes(x=provider, y=ratio, fill=provider)) +
geom_bar(stat="identity") +
geom_label(fill='white',aes(label=round(ratio, digits=1))) +
fillTheme + ylim(0, 12)
ESG/Environment (# of Indexes In Use) Ratio
esgTable %>%
mutate(ratio=esgCount^envCount) %>%
ggplot(aes(x=provider, y=ratio, fill=provider)) +
geom_bar(stat="identity") +
geom_label(fill='white',aes(label=round(ratio, digits=1))) +
fillTheme + ylim(0, 12)
ESG/Environment (% of Indexes In Use) Ratio
esgTable %>%
mutate(ratio=(esgCount/esgIndexTotal)^(envCount/envIndexTotal)) %>%
ggplot(aes(x=provider, y=ratio, fill=provider)) +
geom_bar(stat="identity") +
geom_label(fill='white',aes(label=round(ratio, digits=1))) +
fillTheme + ylim(0, 12)
\newpage
Function 1: get timeseries given any universe and keyword
getYearCountForUniverse <- function(universe, keyword="", yearLimit=1930) {
dateU <- universe %>%
filter(grepl(keyword, Name)) %>%
filter(grepl("/",as.character(Inception.Date))) %>%
mutate(date=as.character(Inception.Date))
yearVec <- dateU$date
for (i in 1:length(yearVec)) {
d <- yearVec[i]
st <- substring(d, nchar(d)-1, nchar(d))
if (as.numeric(st) < 17) {
yearVec[i] <- as.numeric(paste("20", st, sep=""))
} else {
yearVec[i] <- as.numeric(paste("19", st, sep=""))
}
}
yearVec <- as.numeric(yearVec)
yearDF <- data.frame(year=yearVec) %>%
group_by(year) %>%
filter(year >= yearLimit) %>%
summarize(count=n())
return(yearDF)
}
plotTimeSeriesForUniverse <- function(universe, keyword="", yearLimit=1930) {
getYearCountForUniverse(universe, keyword, yearLimit) %>%
ggplot(aes(x=year, y=count)) +
geom_line() +
scale_x_continuous(breaks=seq(yearLimit, 2016, 5)) +
colorTheme
}
plotTimeSeriesForAllScope <- function(universe, esgU, envU, keyword="", yearLimit=1930) {
yearDF <- getYearCountForUniverse(universe, keyword, yearLimit)
esgDF <- getYearCountForUniverse(esgU, keyword, yearLimit)
envDF <- getYearCountForUniverse(envU, keyword, yearLimit)
colnames(esgDF) <- c('year', 'esg')
colnames(envDF) <- c('year', 'env')
fullYears <- yearDF %>%
full_join(esgDF, by="year") %>%
full_join(envDF, by="year")
fullYears[is.na(fullYears)] <- 0
fullYearsNarrow <- gather(fullYears, year)
colnames(fullYearsNarrow) <- c("year", "type", "count")
fullYearsNarrow %>%
filter(year >= yearLimit) %>%
ggplot(aes(x=year, y=count, group=type, col=type)) +
geom_line() +
colorTheme
}
plotTimeSeriesForEsgEnv <- function(esgU, envU, keyword="", yearLimit=1930) {
esgDF <- getYearCountForUniverse(esgU, keyword, yearLimit)
envDF <- getYearCountForUniverse(envU, keyword, yearLimit)
colnames(esgDF) <- c('year', 'esg')
colnames(envDF) <- c('year', 'env')
fullYears <- esgDF %>%
full_join(envDF, by="year")
fullYears[is.na(fullYears)] <- 0
fullYearsNarrow <- gather(fullYears, year)
colnames(fullYearsNarrow) <- c("year", "type", "count")
fullYearsNarrow %>%
filter(year >= yearLimit) %>%
ggplot(aes(x=year, y=count, group=type, col=type)) +
geom_line() +
colorTheme
}
plotTimeSeriesForProviders <- function(MSCI, FTSE, SPDJ, STOXX, keyword="", yearLimit=1930) {
msciDF <- getYearCountForUniverse(MSCI, keyword, yearLimit)
ftseDF <- getYearCountForUniverse(FTSE, keyword, yearLimit)
spdjDF <- getYearCountForUniverse(SPDJ, keyword, yearLimit)
stoxxDF <- getYearCountForUniverse(STOXX, keyword, yearLimit)
colnames(msciDF) <- c('year', 'MSCI')
colnames(ftseDF) <- c('year', 'FTSE')
colnames(spdjDF) <- c('year', 'SPDJ')
colnames(stoxxDF) <- c('year', 'STOXX')
fullYears <- msciDF %>%
full_join(ftseDF, by="year") %>%
full_join(spdjDF, by="year") %>%
full_join(stoxxDF, by="year")
fullYears[is.na(fullYears)] <- 0
fullYearsNarrow <- gather(fullYears, year)
colnames(fullYearsNarrow) <- c("year", "type", "count")
fullYearsNarrow %>%
filter(year >= yearLimit) %>%
ggplot(aes(x=year, y=count, group=type, col=type)) +
geom_line() +
colorTheme
}
example: universe time series
plotTimeSeriesForAllScope(universe, universeesg, universeenv)
example: ESG vs ENV time series
plotTimeSeriesForEsgEnv(universeesg, universeenv)
example: MSCI ESG vs ENV time series
plotTimeSeriesForEsgEnv(MSCIesg, MSCIenv)
example: all provider time series
plotTimeSeriesForProviders(MSCI, FTSE, SPDJ, STOXX, yearLimit=1980)
\newpage
Function 2: check any keyword's distribution of index provider
institutionChoice <- function(keyword, universe) {
companyUniverse <- universe %>% filter(grepl(keyword, Name))
MSCI <- companyUniverse %>% filter(grepl(msciKeyword,Primary.Prospectus.Benchmark))
FTSE <- companyUniverse %>% filter(grepl(ftseKeyword,Primary.Prospectus.Benchmark))
STOXX <- companyUniverse %>% filter(grepl(stoxxKeyword,Primary.Prospectus.Benchmark))
SPDJ <- companyUniverse %>% filter(grepl(spdjKeyword,Primary.Prospectus.Benchmark))
fundsCount <- c(length(MSCI$Name),length(FTSE$Name),length(STOXX$Name),length(SPDJ$Name))
fundsPercent <- fundsCount/sum(fundsCount)
countTable <- data.frame(provider=provider, fundsCount=fundsCount, fundsPercent=fundsPercent)
return(countTable)
}
plotInstitutionChoice <- function(keyword, universe) {
companyUniverse <- universe %>% filter(grepl(keyword, Name))
MSCI <- companyUniverse %>% filter(grepl(msciKeyword,Primary.Prospectus.Benchmark))
FTSE <- companyUniverse %>% filter(grepl(ftseKeyword,Primary.Prospectus.Benchmark))
STOXX <- companyUniverse %>% filter(grepl(stoxxKeyword,Primary.Prospectus.Benchmark))
SPDJ <- companyUniverse %>% filter(grepl(spdjKeyword,Primary.Prospectus.Benchmark))
fundsCount <- c(length(MSCI$Name),length(FTSE$Name),length(STOXX$Name),length(SPDJ$Name))
fundsPercent <- fundsCount/sum(fundsCount)
data.frame(provider=provider, fundsCount=fundsCount, fundsPercent=fundsPercent) %>%
ggplot(aes(x=provider, y=fundsCount, fill=provider)) +
geom_bar(stat="identity") +
geom_label(fill='white',aes(label=toP(fundsCount, fundsPercent))) +
fillTheme
}
getInstitutionChoice <- function(keyword, universe) {
companyUniverse <- universe %>% filter(grepl(keyword, Name))
MSCI <- companyUniverse %>% filter(grepl(msciKeyword,Primary.Prospectus.Benchmark))
FTSE <- companyUniverse %>% filter(grepl(ftseKeyword,Primary.Prospectus.Benchmark))
STOXX <- companyUniverse %>% filter(grepl(stoxxKeyword,Primary.Prospectus.Benchmark))
SPDJ <- companyUniverse %>% filter(grepl(spdjKeyword,Primary.Prospectus.Benchmark))
return(list(MSCI=MSCI, FTSE=FTSE, STOXX=STOXX, SPDJ=SPDJ))
}
exsample: BlackRock
institutionChoice(blackrockKeyword, universe)
plotInstitutionChoice(blackrockKeyword, universe)
exsample: BlackRock ESG
plotInstitutionChoice(blackrockKeyword, universeesg)
exsample: BlackRock ESG
institutionChoice(blackrockKeyword, universeesg)
plotInstitutionChoice(blackrockKeyword, universeesg)
exsample: ETF
institutionChoice('ETF', universe)
plotInstitutionChoice('ETF', universe)
\newpage
Function 3: check any company's index choice provider popularity
plotUniverseByBrandName <- function(universe, floor=5) {
universe %>%
group_by(Branding.Name) %>%
summarize(count=n()) %>%
filter(count >= floor) %>%
ggplot(aes(x=Branding.Name, y=count, fill=Branding.Name)) +
geom_bar(stat="identity") +
geom_label(fill='white',aes(label=count)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
}
exsample: universe plot
plotUniverseByBrandName(universe, floor=30)
exsample: universe ESG plot
plotUniverseByBrandName(universeesg, floor=2)
exsample: universe ENV plot
plotUniverseByBrandName(universeenv, floor=0)
exsample: MSCI plot
plotUniverseByBrandName(MSCI, floor=10)
exsample: MSCI ESG plot
plotUniverseByBrandName(MSCIesg, floor=0)
yezhaoqin/morningStarAnalytics documentation built on May 4, 2019, 2:32 p.m.
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knitr::opts_chunk$set(echo = TRUE) library(DataComputing) library(printr) Sys.setlocale('LC_ALL','C')
Golbal Configuration
filePath <- "~/Desktop/morningstar.csv" sysKeyword <- "(Summary|Percentile|Sum|Average|Count|Maximum|Minimum|Median|Deviation)" msciKeyword <-"MSCI" ftseKeyword <-"(FTSE|Russell)" stoxxKeyword <-"STOXX" spdjKeyword <-"(S&P|Dow|DJ)" esgKeywords <- '(Sustain|ESG|esg|SRI|sri|Social|Governance|Catholic|Ethical)' envKeywords <-'(Water|Carbon|Climate|Enviro|Green|Energy|Renew|Tech|Fossil|Alternative|Clean|Fuel|Pollution|Prevention)' blackrockKeyword <- '(iShare|BlackRock|BLK|Blackrock)' sumF <- function(vec) {sum(as.numeric(gsub(",", "", as.character(vec))), na.rm=TRUE)} toP <- function(c,d) {paste(as.character(c), "(",as.character(round(d*100,digits=1)),"%)",sep="")} "^" <- function(x,y) ifelse(y==0,0,base:::"/"(x,y)) provider <- c("MSCI","FTSE Russell","STOXX","SPDJ") esgIndexTotal <- c(82,5,35,67) envIndexTotal <- c(7,34,5,11) myPalette <- c("#722287","#0079C1","#009941","#002960") fillTheme <- scale_fill_manual(values=myPalette) colorTheme <- scale_color_manual(values=myPalette)
rawTable <- read.csv(filePath, header=TRUE, sep=",") universe <- rawTable[!duplicated(rawTable[c('Name')]),] %>% filter(Name!="") %>% filter(!is.na(Oldest.Share.Class)) %>% filter(!grepl(sysKeyword, Name)) MSCI <- universe %>% filter(grepl(msciKeyword,Primary.Prospectus.Benchmark)) FTSE <- universe %>% filter(grepl(ftseKeyword,Primary.Prospectus.Benchmark)) STOXX <- universe %>% filter(grepl(stoxxKeyword,Primary.Prospectus.Benchmark)) SPDJ <- universe %>% filter(grepl(spdjKeyword,Primary.Prospectus.Benchmark)) fundsCount <- c(length(MSCI$Name),length(FTSE$Name),length(STOXX$Name),length(SPDJ$Name)) fundsPercent <- fundsCount/sum(fundsCount) AUM <- c(sumF(MSCI$Fund.Size.USD),sumF(FTSE$Fund.Size.USD),sumF(STOXX$Fund.Size.USD),sumF(SPDJ$Fund.Size.USD)) countTable <- data.frame(provider=provider, fundsCount=fundsCount, fundsPercent=fundsPercent, AUM=AUM)
\newpage
Analysis of funds tracking each provider
r countTable
Count bar chart
countTable %>% ggplot(aes_string(x='provider', y='fundsCount', fill='provider')) + geom_bar(stat="identity") + geom_label(fill='white',aes(label=toP(fundsCount,fundsPercent))) + fillTheme
AUM bar chart
countTable %>% ggplot(aes(x=provider, y=AUM, fill=provider)) + geom_bar(stat="identity") + geom_label(fill='white',aes(label=toP(paste(as.character(round(AUM/1000000000, digits=2)),"B", sep=""), AUM/sum(AUM)))) + fillTheme
Count pie chart
countTable %>% ggplot(aes(x=factor(1), y=fundsPercent, fill=provider)) + geom_bar(width=1, stat="identity") + coord_polar(theta="y") + theme(axis.text=element_blank(), axis.title=element_blank(), panel.background=element_blank()) + fillTheme
AUM pie chart
countTable %>% ggplot(aes(x=factor(1), y=AUM/sum(AUM), fill=provider)) + geom_bar(width=1, stat="identity") + coord_polar(theta="y") + theme(axis.text=element_blank(), axis.title=element_blank(), panel.background=element_blank()) + fillTheme
\newpage
Analysis of funds tracking ESG indexes of each provider
universeesg <- universe %>% filter(grepl(esgKeywords,Primary.Prospectus.Benchmark)) MSCIesg <- MSCI %>% filter(grepl(esgKeywords,Primary.Prospectus.Benchmark)) FTSEesg <- FTSE %>% filter(grepl(esgKeywords,Primary.Prospectus.Benchmark)) STOXXesg <- STOXX %>% filter(grepl(esgKeywords,Primary.Prospectus.Benchmark)) SPDJesg <- SPDJ %>% filter(grepl(esgKeywords,Primary.Prospectus.Benchmark)) esgCount <- c(length(MSCIesg$Name),length(FTSEesg$Name),length(STOXXesg$Name),length(SPDJesg$Name)) esgPercent <- esgCount/sum(esgCount) esgAUM <- c(sumF(MSCIesg$Fund.Size.USD),sumF(FTSEesg$Fund.Size.USD),sumF(STOXXesg$Fund.Size.USD),sumF(SPDJesg$Fund.Size.USD)) esgTable <- data.frame(provider=provider, fundsCount=fundsCount, fundsPercent=fundsPercent, AUM=AUM, esgCount=esgCount, esgPercent=esgPercent, esgAUM=esgAUM, esgRatio=esgCount/fundsCount, aumRatio=esgAUM/AUM)
r select(esgTable, provider, esgCount, esgPercent, esgAUM)
Count bar chart
esgTable %>% ggplot(aes(x=provider, y=esgCount, fill=provider)) + geom_bar(stat="identity") + geom_label(fill='white',aes(label=toP(esgCount, esgPercent))) + fillTheme
AUM bar comparison
esgTable %>% ggplot(aes(x=provider, y=esgAUM, fill=provider)) + geom_bar(stat="identity") + geom_label(fill='white',aes(label=toP(paste(as.character(round(esgAUM/1000000000, digits=2)),"B", sep=""), esgAUM/sum(esgAUM)))) + fillTheme
Count pie chart
esgTable %>% ggplot(aes(x=factor(1), y=esgPercent, fill=provider)) + geom_bar(width=1, stat="identity") + coord_polar(theta="y") + theme(axis.text=element_blank(), axis.title=element_blank(), panel.background=element_blank()) + fillTheme
AUM pie chart
esgTable %>% ggplot(aes(x=factor(1), y=esgAUM/sum(esgAUM), fill=provider)) + geom_bar(width=1, stat="identity") + coord_polar(theta="y") + theme(axis.text=element_blank(), axis.title=element_blank(), panel.background=element_blank()) + fillTheme
\newpage
Percentage of funds tracking ESG indexes for each provider
total tracking ESG count: r sum(esgTable$esgCount)
total funds count: r sum(esgTable$fundsCount)
percentage count of total: r sum(esgTable$esgCount)/sum(esgTable$fundsCount)
percentage AUM of total: r sum(esgTable$esgAUM)/sum(esgTable$AUM)
Count ratio for each provider
esgTable %>% ggplot(aes(x=provider, y=esgRatio, fill=provider)) + geom_bar(stat="identity") + geom_label(fill='white',aes(label=round(esgRatio, digits=4))) + fillTheme
AUM ratio for each provider
esgTable %>% ggplot(aes(x=provider, y=aumRatio, fill=provider)) + geom_bar(stat="identity") + geom_label(fill='white',aes(label=round(aumRatio, digits=4))) + fillTheme
\newpage
Analysis of funds tracking Environment indexes of each provider
universeenv <- universe %>% filter(grepl(envKeywords,Primary.Prospectus.Benchmark)) MSCIenv <- MSCI %>% filter(grepl(envKeywords,Primary.Prospectus.Benchmark)) FTSEenv <- FTSE %>% filter(grepl(envKeywords,Primary.Prospectus.Benchmark)) STOXXenv <- STOXX %>% filter(grepl(envKeywords,Primary.Prospectus.Benchmark)) SPDJenv <- SPDJ %>% filter(grepl(envKeywords,Primary.Prospectus.Benchmark)) envCount <- c(length(MSCIenv$Name),length(FTSEenv$Name),length(STOXXenv$Name),length(SPDJenv$Name)) envPercent <- envCount/sum(envCount) envAUM <- c(sumF(MSCIenv$Fund.Size.USD),sumF(FTSEenv$Fund.Size.USD),sumF(STOXXenv$Fund.Size.USD),sumF(SPDJenv$Fund.Size.USD)) envTable <- data.frame(provider=provider, fundsCount=fundsCount, fundsPercent=fundsPercent, AUM=AUM, envCount=envCount, envPercent=envPercent, envAUM=envAUM, envRatio=envCount/fundsCount, aumRatio=envAUM/AUM)
r select(envTable, provider, envCount, envPercent, envAUM)
Count bar chart
envTable %>% ggplot(aes(x=provider, y=envCount, fill=provider)) + geom_bar(stat="identity") + geom_label(fill='white',aes(label=toP(envCount, envPercent))) + fillTheme
AUM bar chart
envTable %>% ggplot(aes(x=provider, y=envAUM, fill=provider)) + geom_bar(stat="identity") + geom_label(fill='white',aes(label=toP(paste(as.character(round(envAUM/1000000000, digits=2)),"B", sep=""), envAUM/sum(envAUM)))) + fillTheme
Count pie chart
envTable %>% ggplot(aes(x=factor(1), y=envPercent, fill=provider)) + geom_bar(width=1, stat="identity") + coord_polar(theta="y") + theme(axis.text=element_blank(), axis.title=element_blank(), panel.background=element_blank()) + fillTheme
AUM pie chart
envTable %>% ggplot(aes(x=factor(1), y=envAUM/sum(envAUM), fill=provider)) + geom_bar(width=1, stat="identity") + coord_polar(theta="y") + theme(axis.text=element_blank(), axis.title=element_blank(), panel.background=element_blank()) + fillTheme
\newpage
Percentage of Environment funds tracking Environment indexes for each provider
total tracking Environment count: r sum(envTable$envCount)
total funds count: r sum(envTable$fundsCount)
percentage count of total: r sum(envTable$envCount)/sum(envTable$fundsCount)
percentage AUM of total: r sum(envTable$envAUM)/sum(envTable$AUM)
Count ratio for each provider
envTable %>% ggplot(aes(x=provider, y=envRatio, fill=provider)) + geom_bar(stat="identity") + geom_label(fill='white',aes(label=round(envRatio, digits=4))) + fillTheme
AUM ratio for each provider
envTable %>% ggplot(aes(x=provider, y=aumRatio, fill=provider)) + geom_bar(stat="identity") + geom_label(fill='white',aes(label=round(aumRatio, digits=4))) + fillTheme
\newpage
ESG & Environment cross comparison
ESG/Environment (# of Indexes) Ratio
data.frame(esg=esgIndexTotal, env=envIndexTotal, ratio=esgIndexTotal/envIndexTotal) %>% ggplot(aes(x=provider, y=ratio, fill=provider)) + geom_bar(stat="identity") + geom_label(fill='white',aes(label=round(ratio, digits=1))) + fillTheme + ylim(0, 12)
ESG/Environment (# of Indexes In Use) Ratio
esgTable %>% mutate(ratio=esgCount^envCount) %>% ggplot(aes(x=provider, y=ratio, fill=provider)) + geom_bar(stat="identity") + geom_label(fill='white',aes(label=round(ratio, digits=1))) + fillTheme + ylim(0, 12)
ESG/Environment (% of Indexes In Use) Ratio
esgTable %>% mutate(ratio=(esgCount/esgIndexTotal)^(envCount/envIndexTotal)) %>% ggplot(aes(x=provider, y=ratio, fill=provider)) + geom_bar(stat="identity") + geom_label(fill='white',aes(label=round(ratio, digits=1))) + fillTheme + ylim(0, 12)
\newpage
Function 1: get timeseries given any universe and keyword
getYearCountForUniverse <- function(universe, keyword="", yearLimit=1930) { dateU <- universe %>% filter(grepl(keyword, Name)) %>% filter(grepl("/",as.character(Inception.Date))) %>% mutate(date=as.character(Inception.Date)) yearVec <- dateU$date for (i in 1:length(yearVec)) { d <- yearVec[i] st <- substring(d, nchar(d)-1, nchar(d)) if (as.numeric(st) < 17) { yearVec[i] <- as.numeric(paste("20", st, sep="")) } else { yearVec[i] <- as.numeric(paste("19", st, sep="")) } } yearVec <- as.numeric(yearVec) yearDF <- data.frame(year=yearVec) %>% group_by(year) %>% filter(year >= yearLimit) %>% summarize(count=n()) return(yearDF) } plotTimeSeriesForUniverse <- function(universe, keyword="", yearLimit=1930) { getYearCountForUniverse(universe, keyword, yearLimit) %>% ggplot(aes(x=year, y=count)) + geom_line() + scale_x_continuous(breaks=seq(yearLimit, 2016, 5)) + colorTheme } plotTimeSeriesForAllScope <- function(universe, esgU, envU, keyword="", yearLimit=1930) { yearDF <- getYearCountForUniverse(universe, keyword, yearLimit) esgDF <- getYearCountForUniverse(esgU, keyword, yearLimit) envDF <- getYearCountForUniverse(envU, keyword, yearLimit) colnames(esgDF) <- c('year', 'esg') colnames(envDF) <- c('year', 'env') fullYears <- yearDF %>% full_join(esgDF, by="year") %>% full_join(envDF, by="year") fullYears[is.na(fullYears)] <- 0 fullYearsNarrow <- gather(fullYears, year) colnames(fullYearsNarrow) <- c("year", "type", "count") fullYearsNarrow %>% filter(year >= yearLimit) %>% ggplot(aes(x=year, y=count, group=type, col=type)) + geom_line() + colorTheme } plotTimeSeriesForEsgEnv <- function(esgU, envU, keyword="", yearLimit=1930) { esgDF <- getYearCountForUniverse(esgU, keyword, yearLimit) envDF <- getYearCountForUniverse(envU, keyword, yearLimit) colnames(esgDF) <- c('year', 'esg') colnames(envDF) <- c('year', 'env') fullYears <- esgDF %>% full_join(envDF, by="year") fullYears[is.na(fullYears)] <- 0 fullYearsNarrow <- gather(fullYears, year) colnames(fullYearsNarrow) <- c("year", "type", "count") fullYearsNarrow %>% filter(year >= yearLimit) %>% ggplot(aes(x=year, y=count, group=type, col=type)) + geom_line() + colorTheme } plotTimeSeriesForProviders <- function(MSCI, FTSE, SPDJ, STOXX, keyword="", yearLimit=1930) { msciDF <- getYearCountForUniverse(MSCI, keyword, yearLimit) ftseDF <- getYearCountForUniverse(FTSE, keyword, yearLimit) spdjDF <- getYearCountForUniverse(SPDJ, keyword, yearLimit) stoxxDF <- getYearCountForUniverse(STOXX, keyword, yearLimit) colnames(msciDF) <- c('year', 'MSCI') colnames(ftseDF) <- c('year', 'FTSE') colnames(spdjDF) <- c('year', 'SPDJ') colnames(stoxxDF) <- c('year', 'STOXX') fullYears <- msciDF %>% full_join(ftseDF, by="year") %>% full_join(spdjDF, by="year") %>% full_join(stoxxDF, by="year") fullYears[is.na(fullYears)] <- 0 fullYearsNarrow <- gather(fullYears, year) colnames(fullYearsNarrow) <- c("year", "type", "count") fullYearsNarrow %>% filter(year >= yearLimit) %>% ggplot(aes(x=year, y=count, group=type, col=type)) + geom_line() + colorTheme }
example: universe time series
plotTimeSeriesForAllScope(universe, universeesg, universeenv)
example: ESG vs ENV time series
plotTimeSeriesForEsgEnv(universeesg, universeenv)
example: MSCI ESG vs ENV time series
plotTimeSeriesForEsgEnv(MSCIesg, MSCIenv)
example: all provider time series
plotTimeSeriesForProviders(MSCI, FTSE, SPDJ, STOXX, yearLimit=1980)
\newpage
Function 2: check any keyword's distribution of index provider
institutionChoice <- function(keyword, universe) { companyUniverse <- universe %>% filter(grepl(keyword, Name)) MSCI <- companyUniverse %>% filter(grepl(msciKeyword,Primary.Prospectus.Benchmark)) FTSE <- companyUniverse %>% filter(grepl(ftseKeyword,Primary.Prospectus.Benchmark)) STOXX <- companyUniverse %>% filter(grepl(stoxxKeyword,Primary.Prospectus.Benchmark)) SPDJ <- companyUniverse %>% filter(grepl(spdjKeyword,Primary.Prospectus.Benchmark)) fundsCount <- c(length(MSCI$Name),length(FTSE$Name),length(STOXX$Name),length(SPDJ$Name)) fundsPercent <- fundsCount/sum(fundsCount) countTable <- data.frame(provider=provider, fundsCount=fundsCount, fundsPercent=fundsPercent) return(countTable) } plotInstitutionChoice <- function(keyword, universe) { companyUniverse <- universe %>% filter(grepl(keyword, Name)) MSCI <- companyUniverse %>% filter(grepl(msciKeyword,Primary.Prospectus.Benchmark)) FTSE <- companyUniverse %>% filter(grepl(ftseKeyword,Primary.Prospectus.Benchmark)) STOXX <- companyUniverse %>% filter(grepl(stoxxKeyword,Primary.Prospectus.Benchmark)) SPDJ <- companyUniverse %>% filter(grepl(spdjKeyword,Primary.Prospectus.Benchmark)) fundsCount <- c(length(MSCI$Name),length(FTSE$Name),length(STOXX$Name),length(SPDJ$Name)) fundsPercent <- fundsCount/sum(fundsCount) data.frame(provider=provider, fundsCount=fundsCount, fundsPercent=fundsPercent) %>% ggplot(aes(x=provider, y=fundsCount, fill=provider)) + geom_bar(stat="identity") + geom_label(fill='white',aes(label=toP(fundsCount, fundsPercent))) + fillTheme } getInstitutionChoice <- function(keyword, universe) { companyUniverse <- universe %>% filter(grepl(keyword, Name)) MSCI <- companyUniverse %>% filter(grepl(msciKeyword,Primary.Prospectus.Benchmark)) FTSE <- companyUniverse %>% filter(grepl(ftseKeyword,Primary.Prospectus.Benchmark)) STOXX <- companyUniverse %>% filter(grepl(stoxxKeyword,Primary.Prospectus.Benchmark)) SPDJ <- companyUniverse %>% filter(grepl(spdjKeyword,Primary.Prospectus.Benchmark)) return(list(MSCI=MSCI, FTSE=FTSE, STOXX=STOXX, SPDJ=SPDJ)) }
exsample: BlackRock
institutionChoice(blackrockKeyword, universe)
plotInstitutionChoice(blackrockKeyword, universe)
exsample: BlackRock ESG
plotInstitutionChoice(blackrockKeyword, universeesg)
exsample: BlackRock ESG
institutionChoice(blackrockKeyword, universeesg)
plotInstitutionChoice(blackrockKeyword, universeesg)
exsample: ETF
institutionChoice('ETF', universe)
plotInstitutionChoice('ETF', universe)
\newpage
Function 3: check any company's index choice provider popularity
plotUniverseByBrandName <- function(universe, floor=5) { universe %>% group_by(Branding.Name) %>% summarize(count=n()) %>% filter(count >= floor) %>% ggplot(aes(x=Branding.Name, y=count, fill=Branding.Name)) + geom_bar(stat="identity") + geom_label(fill='white',aes(label=count)) + theme(axis.text.x = element_text(angle = 45, hjust = 1)) }
exsample: universe plot
plotUniverseByBrandName(universe, floor=30)
exsample: universe ESG plot
plotUniverseByBrandName(universeesg, floor=2)
exsample: universe ENV plot
plotUniverseByBrandName(universeenv, floor=0)
exsample: MSCI plot
plotUniverseByBrandName(MSCI, floor=10)
exsample: MSCI ESG plot
plotUniverseByBrandName(MSCIesg, floor=0)
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