mfanalysisindia
Rajdeep Manaise
May 05, 2020
- Section-1: India’s Mutual Funds-General Details
- 1A. Mutual Funds Summary
- 1B.Top 10 Asset Management Companies(AMC’s) by Number of Funds
- 1C.Top 10 AMC’s from 2009 to 2019
- 1D.Top 10 AMC by Percent Growth 2009 to 2019
- 1F. MFs launched by Top 10 AMC’s in the last three years
- 1G.Total MFs launched since 2010 to 2019
- 1H. AMC’s with schemes aging more than 5 yrs as on 2020-04-07
- Section-2: Assets Under Management(AUM)
- 2A. Top 10 AMC’s as per AUM
- 2B. Top 10 AMC’s whose AUM has grown in the Period
- 2C. 10 AMC’s whose AUM has reduced in the Period
- Section-3: Schemewise Analysis
- 3A. Schemes whose AUM has increased maximum from Mar 2019 to Jun 2019
- Section-4: Industry wise Analysis
- 4A. Change in the investments by Industry Dec 2019 to Mar 2020
#PROJECT-2: MF ANALYSIS INDIA General Analysis of the MF’s of India.Data used is taken from open data available on various sites.
knitr::opts_chunk$set(root.dir = "C:/Users/rsman/Documents/PERS FOLDERS/RAJDEEP/INVESTMENTS/ANALYSIS/DATA")library(lubridate)
library(stringr)
library(pander)
library(data.table)
library(xlsx)
library(dplyr)
library(scales)
library(ggplot2)
library(Quandl)
library(tabulizer)
library(knitr)knitr::opts_chunk$set(echo = TRUE)
knitr::opts_chunk$set(root.dir = "C:/Users/rsman/Documents/PERS FOLDERS/RAJDEEP/INVESTMENTS/ANALYSIS/DATA")
getwd()[1] “C:/Users/rsman/Documents/PERS FOLDERS/RAJDEEP/INVESTMENTS/ANALYSIS/DATA”
#Data from Quandl
#qdl.link <- "https://www.quandl.com/api/v3/databases/AMFI/metadata?api_key=yKy3ZHB-brjUqC83QqRn"
#temp.amfi.data <- tempfile()
#download.file(qdl.link,temp.amfi.data)
#data <- read.table(unz(temp, "a1.dat"))
#unlink(temp)
###Functions
colFmt1 = function(x,color){
outputFormat = knitr::opts_knit$get("rmarkdown.pandoc.to")
if(outputFormat == 'latex')
paste("\\textcolor{",color,"}{",x,"}",sep="")
else if(outputFormat == 'html')
paste("<font color='",color,"'>",x,"</font>",sep="")
else
x
}
colFmt = function(x,color){
outputFormat = knitr::opts_knit$get("rmarkdown.pandoc.to")
if(outputFormat == 'html')
paste("<font color='",color,"'>",x,"</font>",sep="")
else
x
}
makeCharac <- function(rx.vr){
new.vr <- as.character(rx.vr)
return(new.vr)
}
###End Functions
pandoc.header(colFmt("Section-1: India's Mutual Funds-General Details", 'red'), 1, 'setext')Section-1: India’s Mutual Funds-General Details
CWD <- "C:/Users/rsman/Documents/PERS FOLDERS/RAJDEEP/INVESTMENTS/ANALYSIS/DATA/"
setwd("C:/Users/rsman/Documents/PERS FOLDERS/RAJDEEP/INVESTMENTS/ANALYSIS/DATA")
mfdetails <- as.data.frame(read.csv(unzip("AMFI_metadata.csv.zip"), sep = ","))#AMFI Data
mfportfolio <- as.data.frame(read.xlsx("selectedmf.xlsx", sheetName = "Sheet1"))#My selected MF's
amcdetails <- as.data.frame(read.xlsx("amfi_amc.xlsx", sheetName = "Sheet1"))#Fund Companies
aum1 <- as.data.frame(read.xlsx("amfi_aum_amcwise_one.xlsx", sheetIndex = 1))# AUM of Period-1
aum2 <- as.data.frame(read.xlsx("amfi_aum_amcwise_two.xlsx", sheetIndex = 1))#AUM of Period-2
amcdetails <- amcdetails[-which(is.na(amcdetails$AMC)),]
#mfdetails <- read.csv("MFData.csv", sep = ",")
mfdetails$from_date <- as.Date(mfdetails$from_date)
mfdetails$to_date <- as.Date(mfdetails$to_date)
##No of MF per AMC
mfdetails$n_company <- trimws(substr(mfdetails[,2],1, str_locate(mfdetails[,2],"-")-2))
mfdetails[grep(";", mfdetails[,"n_company"]),"n_company"]<-
substr(mfdetails[grep(";", mfdetails[,"n_company"]),"n_company"],str_locate(mfdetails[grep(";", mfdetails[,"n_company"]),"n_company"],";")+1 ,500)
mfdata <- mfdetails
mfdata$n_company <- trimws(substr(mfdetails[,2],1, str_locate(mfdetails[,2],"-")-2))
mfdata[grep(";", mfdata[,"n_company"]),"n_company"]<-
substr(mfdata[grep(";", mfdata[,"n_company"]),"n_company"],str_locate(mfdata[grep(";", mfdata[,"n_company"]),"n_company"],";")+1 ,500)
df.tmp <- mfdata[mfdata$to_date >= Sys.Date()-30,]
df.coys <- data.frame(tapply(df.tmp$name, df.tmp$n_company, length))
rm(df.tmp)
df.coys$company <- rownames(df.coys)
df.coys$no_of_funds <- df.coys$tapply.df.tmp.name..df.tmp.n_company..length.
#rownames(df.coys) <- rep(1:dim(df.coys$no_of_funds))
rownames(df.coys) <- rep(1:length(df.coys$no_of_funds))
df.coys <- df.coys[,-1]
df.coys <- subset(df.coys, company %in% amcdetails$AMC)
#MF Gen Details
mf.gen.df <- data.frame(matrix(ncol = 2, nrow = 0))
colnames(mf.gen.df) <- c("Details", "Data")
tmp.df <- data.frame("Details" = "Data Since", "Data" = as.character(min(mfdata$from_date)))
tmp.df$Details <- as.character(tmp.df$Details)
tmp.df$Data <- as.character(tmp.df$Data)
mf.gen.df <- rbind(mf.gen.df, tmp.df)
tmp.df <- data.frame("Details" = "No of Asset Management Companies", "Data" = nrow(df.coys))
mf.gen.df <- rbind(mf.gen.df, tmp.df)
tmp.df <- data.frame("Details" = "No of MF's as on today", "Data" = nrow(mfdata[mfdata$to_date >= Sys.Date()-30,]))
mf.gen.df <- rbind(mf.gen.df, tmp.df)
min_dt <- min(mfdata$from_date)
tmp.df <- data.frame("Details" = paste("No of MF's launched on or before ", min_dt," and still active"), "Data" = nrow(mfdata[mfdata$from_date==min_dt & mfdata$to_date >= Sys.Date()-30,]))
mf.gen.df <- rbind(mf.gen.df, tmp.df)
dt.var <- as.Date(gsub(" ","",paste(as.character(year(Sys.Date())),"-01-01")))
dt.var.lst <- as.Date(gsub(" ","",paste(as.character(year(Sys.Date())-1),"-01-01")))
tmp.df <- data.frame("Details" = paste("MF's Launched in ", year(dt.var.lst)),
"Data" = nrow(mfdata[year(mfdata$from_date) == year(dt.var.lst),]))
mf.gen.df <- rbind(mf.gen.df, tmp.df)
tmp.df <- data.frame("Details" = paste("MF's Launched in ",
year(Sys.Date()), " till now"),"Data" = nrow(mfdata[mfdata$from_date >= dt.var,]))
mf.gen.df <- rbind(mf.gen.df, tmp.df)
pandoc.header('1A. Mutual Funds Summary', 1, 'setext')1A. Mutual Funds Summary
#pander(mf.gen.df,justify = c("left"))
knitr::kable(mf.gen.df, caption = colFmt("MF's General Details", "blue"))| Details | Data |
|---|---|
| Data Since | 2006-04-01 |
| No of Asset Management Companies | 38 |
| No of MF’s as on today | 6035 |
| No of MF’s launched on or before 2006-04-01 and still active | 36 |
| MF’s Launched in 2019 | 1124 |
| MF’s Launched in 2020 till now | 383 |
write.xlsx(mf.gen.df, file = "MFDETAILS.xlsx", sheetName = "GenDetails")
rm(mf.gen.df)
rm(tmp.df)
pandoc.header("1B.Top 10 Asset Management Companies(AMC's) by Number of Funds", 1, 'setext')1B.Top 10 Asset Management Companies(AMC’s) by Number of Funds
df.coys <- df.coys[order(df.coys$no_of_funds, decreasing = TRUE),]
rownames(df.coys) <- NULL
knitr::kable(df.coys[1:10,], caption = colFmt("Top 10 AMC's by Number of Funds", "blue"))| company | no_of_funds |
|---|---|
| ICICI Prudential Mutual Fund | 448 |
| UTI Mutual Fund | 378 |
| Aditya Birla Sun Life Mutual Fund | 303 |
| Franklin Templeton Mutual Fund | 293 |
| IDFC Mutual Fund | 254 |
| Sundaram Mutual Fund | 232 |
| SBI Mutual Fund | 231 |
| DSP Mutual Fund | 223 |
| Axis Mutual Fund | 216 |
| Tata Mutual Fund | 210 |
mf.yrly.calc <- function(mfdata, rx.yr){
mfdata <- mfdata[year(mfdata$from_date) <= rx.yr & mfdata$to_date >= Sys.Date()-30,]
df.coys.tmp <- data.frame(tapply(mfdata$name, mfdata$n_company, length))
df.coys.tmp$company <- rownames(df.coys.tmp)
df.coys.tmp$no_of_funds <- df.coys.tmp[,1]
rownames(df.coys.tmp) <- rep(1:nrow(df.coys.tmp))
df.coys.tmp <- df.coys.tmp[,-1]
colnames(df.coys.tmp) <- c("company", rx.yr)
plot.data <- df.coys.tmp
plot.data$year <- rx.yr
return(list(df.coys.tmp, plot.data))
}
max.yr <- max(year(mfdetails$from_date))-1
st.yr <- max.yr-10
plot.data <- data.frame(matrix(ncol =3, nrow =0))
colnames(plot.data) <- c("company", "no_of_funds", "year")
for (i in st.yr: max.yr) {
rx.lst <- mf.yrly.calc(mfdata[mfdata$to_date >= Sys.Date()-30,], i)
fn.df <- data.frame(rx.lst[1])
colnames(fn.df) <- c("company", i)
df.coys <- left_join(df.coys, fn.df, by = "company")
df.coys[is.na(df.coys)] <- 0
tmp.df <- data.frame(rx.lst[2])
colnames(tmp.df) <- c("company", "no_of_funds", "year")
plot.data <- rbind(plot.data, tmp.df)
}
pandoc.header(paste("1C.Top 10 AMC's from ", st.yr ," to ", max.yr), 1, 'setext')1C.Top 10 AMC’s from 2009 to 2019
df.coys <- df.coys[order(df.coys$no_of_funds, decreasing = TRUE),]
rownames(df.coys) <- NULL
ctr <- ncol(df.coys)
knitr::kable(df.coys[1:10,c(1,3:ctr,2)], caption = colFmt(paste("Top 10 MF Companies and their absolute growth from ",st.yr, " to ", max.yr), "blue"))| company | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | no_of_funds |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ICICI Prudential Mutual Fund | 125 | 147 | 157 | 162 | 293 | 344 | 356 | 363 | 369 | 395 | 441 | 448 |
| UTI Mutual Fund | 86 | 89 | 90 | 92 | 167 | 183 | 229 | 247 | 256 | 288 | 302 | 378 |
| Aditya Birla Sun Life Mutual Fund | 93 | 95 | 97 | 99 | 190 | 226 | 240 | 242 | 242 | 250 | 299 | 303 |
| Franklin Templeton Mutual Fund | 87 | 88 | 90 | 97 | 173 | 187 | 187 | 189 | 189 | 197 | 205 | 293 |
| IDFC Mutual Fund | 52 | 64 | 70 | 74 | 153 | 178 | 186 | 204 | 220 | 236 | 248 | 254 |
| Sundaram Mutual Fund | 78 | 83 | 83 | 83 | 141 | 145 | 147 | 154 | 160 | 183 | 216 | 232 |
| SBI Mutual Fund | 92 | 95 | 98 | 99 | 198 | 200 | 218 | 219 | 219 | 221 | 231 | 231 |
| DSP Mutual Fund | 52 | 57 | 59 | 65 | 144 | 160 | 170 | 182 | 186 | 205 | 223 | 223 |
| Axis Mutual Fund | 10 | 33 | 40 | 52 | 95 | 107 | 124 | 126 | 145 | 167 | 201 | 216 |
| Tata Mutual Fund | 42 | 43 | 47 | 47 | 92 | 94 | 126 | 126 | 126 | 138 | 198 | 210 |
tmp.df <- df.coys[1:10,] #Name of Top 10 companies
plot.1 <- subset(plot.data, company %in% tmp.df$company)
ggplot(plot.1, aes(x=plot.1$year, y= plot.1$no_of_funds)) +
geom_line(aes(colour=plot.1$company, group=plot.1$company))+
geom_point(aes(colour=plot.1$company), size=3)+scale_fill_brewer(palette = "Set1")+
theme_classic()+
labs(subtitle = "ss")## Warning: Use of `plot.1$company` is discouraged. Use `company` instead.
## Warning: Use of `plot.1$company` is discouraged. Use `company` instead.## Warning: Use of `plot.1$year` is discouraged. Use `year` instead.## Warning: Use of `plot.1$no_of_funds` is discouraged. Use `no_of_funds` instead.## Warning: Use of `plot.1$company` is discouraged. Use `company` instead.## Warning: Use of `plot.1$year` is discouraged. Use `year` instead.## Warning: Use of `plot.1$no_of_funds` is discouraged. Use `no_of_funds` instead.
rm(plot.1)
#calculate the Percent growth
ctr <- ncol(tmp.df)
tmp.df.change <- data.frame(tmp.df[,1])
colnames(tmp.df.change) <- c("company")
tmp.df.percent <- data.frame(matrix(ncol = 3, nrow = 0))
colnames(tmp.df.percent) <- c("company","growth", "period")
for (i in 3:(ctr-1)) {
new.name <- paste(colnames(tmp.df[i]),"-",colnames(tmp.df[i+1]))
name.str <- colnames(tmp.df.change)
new.name <- gsub(" ","", new.name)
new.data <- ((tmp.df[i+1] - tmp.df[i])/tmp.df[i])*100
tmp.df.change <- cbind(tmp.df.change, new.data)
name.str <- c(name.str, new.name)
colnames(tmp.df.change) <- name.str
c1<- tmp.df.change[1]
c2<- data.frame(new.data)
new.df.percnet <- data.frame(c1, c2, new.name)
colnames(new.df.percnet) <- c("company","growth", "period")
tmp.df.percent <- rbind(tmp.df.percent, new.df.percnet)
}
ctr <- ncol(tmp.df.change)
tmp.df.change$avg_growth <- rowMeans(tmp.df.change[,c(2:ctr)])
pandoc.header(paste('1D.Top 10 AMC by Percent Growth', st.yr ,' to ', max.yr), 1, 'setext')1D.Top 10 AMC by Percent Growth 2009 to 2019
options(digits = 3)
knitr::kable(tmp.df.change, caption = colFmt(paste("Top 10 MF Companies and their Percent growth from ",st.yr, " to ", max.yr),"blue"))| company | 2009-2010 | 2010-2011 | 2011-2012 | 2012-2013 | 2013-2014 | 2014-2015 | 2015-2016 | 2016-2017 | 2017-2018 | 2018-2019 | avg_growth |
|---|---|---|---|---|---|---|---|---|---|---|---|
| ICICI Prudential Mutual Fund | 17.60 | 6.80 | 3.18 | 80.9 | 17.41 | 3.49 | 1.966 | 1.65 | 7.046 | 11.65 | 15.2 |
| UTI Mutual Fund | 3.49 | 1.12 | 2.22 | 81.5 | 9.58 | 25.14 | 7.860 | 3.64 | 12.500 | 4.86 | 15.2 |
| Aditya Birla Sun Life Mutual Fund | 2.15 | 2.10 | 2.06 | 91.9 | 18.95 | 6.20 | 0.833 | 0.00 | 3.306 | 19.60 | 14.7 |
| Franklin Templeton Mutual Fund | 1.15 | 2.27 | 7.78 | 78.4 | 8.09 | 0.00 | 1.070 | 0.00 | 4.233 | 4.06 | 10.7 |
| IDFC Mutual Fund | 23.08 | 9.38 | 5.71 | 106.8 | 16.34 | 4.49 | 9.677 | 7.84 | 7.273 | 5.08 | 19.6 |
| Sundaram Mutual Fund | 6.41 | 0.00 | 0.00 | 69.9 | 2.84 | 1.38 | 4.762 | 3.90 | 14.375 | 18.03 | 12.2 |
| SBI Mutual Fund | 3.26 | 3.16 | 1.02 | 100.0 | 1.01 | 9.00 | 0.459 | 0.00 | 0.913 | 4.53 | 12.3 |
| DSP Mutual Fund | 9.62 | 3.51 | 10.17 | 121.5 | 11.11 | 6.25 | 7.059 | 2.20 | 10.215 | 8.78 | 19.0 |
| Axis Mutual Fund | 230.00 | 21.21 | 30.00 | 82.7 | 12.63 | 15.89 | 1.613 | 15.08 | 15.172 | 20.36 | 44.5 |
| Tata Mutual Fund | 2.38 | 9.30 | 0.00 | 95.7 | 2.17 | 34.04 | 0.000 | 0.00 | 9.524 | 43.48 | 19.7 |
ggplot(tmp.df.percent, aes(x=tmp.df.percent$period, y= tmp.df.percent$growth)) +
geom_line(aes(colour=tmp.df.percent$company, group=tmp.df.percent$company))+
geom_point(aes(colour=tmp.df.percent$company), size=3)+scale_fill_brewer(palette = "Set1")+
theme_classic()+ theme(axis.text.x=element_text(angle = 90, hjust = 0))+
labs(subtitle = "ss")## Warning: Use of `tmp.df.percent$company` is discouraged. Use `company` instead.## Warning: Use of `tmp.df.percent$company` is discouraged. Use `company` instead.## Warning: Use of `tmp.df.percent$period` is discouraged. Use `period` instead.## Warning: Use of `tmp.df.percent$growth` is discouraged. Use `growth` instead.## Warning: Use of `tmp.df.percent$company` is discouraged. Use `company` instead.## Warning: Use of `tmp.df.percent$period` is discouraged. Use `period` instead.## Warning: Use of `tmp.df.percent$growth` is discouraged. Use `growth` instead.
rm(tmp.df.change)
rm(tmp.df.percent)
cat("Looking at the chart above we can see that all the AMC's have been launching new funds in an identical manner. There was one spike in the year 2013 and there was a significant rise in the number of funds launched by AMC's. This data is being analsyed further in the document. ")Looking at the chart above we can see that all the AMC’s have been launching new funds in an identical manner. There was one spike in the year 2013 and there was a significant rise in the number of funds launched by AMC’s. This data is being analsyed further in the document.
#New Launch trend monthwise
mf.mthly.df <- data.frame(matrix(ncol = 4, nrow = 0))
colnames(mf.mthly.df) <- c("company", "no_of_funds", "month", "year")
mf.mthly.calc <- function(rx.df, rx.yr){
mf.mthly.df <- data.frame(matrix(ncol = 4, nrow = 0))
colnames(mf.mthly.df) <- c("company", "no_of_funds", "month", "year")
lpr.df <- rx.df[year(rx.df$from_date) == rx.yr,]
for (i in 1:12) {
loop.df <- lpr.df[month(lpr.df$from_date)==i,]
df.coys.tmp <- data.frame(tapply(loop.df$name, loop.df$n_company, length))
df.coys.tmp$company <- rownames(df.coys.tmp)
df.coys.tmp$no_of_funds <- df.coys.tmp[,1]
df.coys.tmp$month <- month.name[i]
df.coys.tmp$year <- rx.yr
df.coys.tmp <- df.coys.tmp[,-1]
colnames(df.coys.tmp) <- c("company", "no_of_funds", "month", "year")
rownames(df.coys.tmp) <- rep(1:nrow(df.coys.tmp))
mf.mthly.df <- rbind(mf.mthly.df, df.coys.tmp)
}
df.mths.tmp <- data.frame(tapply(lpr.df$n_company, months(lpr.df$from_date), length))
colnames(df.mths.tmp) <- c("no_of_funds")
df.mths.tmp$month <- rownames(df.mths.tmp)
df.mths.tmp$year <- rx.yr
rownames(df.mths.tmp) <- rep(1:nrow(df.mths.tmp))
return(list(mf.mthly.df, df.mths.tmp))
}
mthly.plot.df <- data.frame(matrix(ncol = 4, nrow = 0))
colnames(mthly.plot.df) <- c("company", "no_of_funds", "month", "year")
mthly.tot.plot <- data.frame(matrix(ncol=3, nrow = 0))
colnames(mthly.tot.plot) <- c("no_of_funds", "month", "year")
for (j in st.yr:max.yr) {
#j<- j+1
rx.lst <- mf.mthly.calc(mfdetails, j)
tmp.df <- data.frame(rx.lst[1])
mthly.plot.df <- rbind(mthly.plot.df, tmp.df)
mth.tot.df <- data.frame(rx.lst[2])
mthly.tot.plot <- rbind(mthly.tot.plot, mth.tot.df)
}
ploter.fn.1 <- function(rx.df){
plot.data <- rx.df
gg.plot <- ggplot(plot.data , aes(x=plot.data$month , y= plot.data$no_of_funds )) +
geom_line(aes(colour=plot.data$year, group=plot.data$year), show.legend = FALSE)+
geom_point(aes(colour=plot.data$year), size=3, show.legend = FALSE)+
facet_wrap(~company, nrow=1, scales = "free")+
theme(axis.text.x=element_text(angle = 90, hjust = 0))+
scale_x_discrete(limits = c("January",
"February","March","April",
"May","June","July","August","September","October","November","December"))
gg.plot
}
plot.data <- plot.data[plot.data$year == max.yr,]
pandoc.header("1F. MFs launched by Top 10 AMC's in the last three years", 1, 'setext')1F. MFs launched by Top 10 AMC’s in the last three years
tmp.df <- df.coys[1:2,] #Name of Top 3 companies
plot.data <- subset(mthly.plot.df, company %in% tmp.df$company)
ploter.fn.1(plot.data[plot.data$year >= max.yr-2 ,])## Warning: Use of `plot.data$year` is discouraged. Use `year` instead.## Warning: Use of `plot.data$year` is discouraged. Use `year` instead.## Warning: Use of `plot.data$month` is discouraged. Use `month` instead.## Warning: Use of `plot.data$no_of_funds` is discouraged. Use `no_of_funds`
## instead.## Warning: Use of `plot.data$year` is discouraged. Use `year` instead.## Warning: Use of `plot.data$month` is discouraged. Use `month` instead.## Warning: Use of `plot.data$no_of_funds` is discouraged. Use `no_of_funds`
## instead.
tmp.df <- df.coys[3:4,] #Name of Top 4-6 companies
plot.data <- subset(mthly.plot.df, company %in% tmp.df$company)
ploter.fn.1(plot.data[plot.data$year >= max.yr-2,])## Warning: Use of `plot.data$year` is discouraged. Use `year` instead.## Warning: Use of `plot.data$year` is discouraged. Use `year` instead.## Warning: Use of `plot.data$month` is discouraged. Use `month` instead.## Warning: Use of `plot.data$no_of_funds` is discouraged. Use `no_of_funds`
## instead.## Warning: Use of `plot.data$year` is discouraged. Use `year` instead.## Warning: Use of `plot.data$month` is discouraged. Use `month` instead.## Warning: Use of `plot.data$no_of_funds` is discouraged. Use `no_of_funds`
## instead.
tmp.df <- df.coys[5:6,] #Name of Top 7-9 companies
plot.data <- subset(mthly.plot.df, company %in% tmp.df$company)
ploter.fn.1(plot.data[plot.data$year >= max.yr-2,])## Warning: Use of `plot.data$year` is discouraged. Use `year` instead.## Warning: Use of `plot.data$year` is discouraged. Use `year` instead.## Warning: Use of `plot.data$month` is discouraged. Use `month` instead.## Warning: Use of `plot.data$no_of_funds` is discouraged. Use `no_of_funds`
## instead.## Warning: Use of `plot.data$year` is discouraged. Use `year` instead.## Warning: Use of `plot.data$month` is discouraged. Use `month` instead.## Warning: Use of `plot.data$no_of_funds` is discouraged. Use `no_of_funds`
## instead.
tmp.df <- df.coys[7:8,] #Name of Top 10-12 companies
plot.data <- subset(mthly.plot.df, company %in% tmp.df$company)
ploter.fn.1(plot.data[plot.data$year >= max.yr-2,])## Warning: Use of `plot.data$year` is discouraged. Use `year` instead.## Warning: Use of `plot.data$year` is discouraged. Use `year` instead.## Warning: Use of `plot.data$month` is discouraged. Use `month` instead.## Warning: Use of `plot.data$no_of_funds` is discouraged. Use `no_of_funds`
## instead.## Warning: Use of `plot.data$year` is discouraged. Use `year` instead.## Warning: Use of `plot.data$month` is discouraged. Use `month` instead.## Warning: Use of `plot.data$no_of_funds` is discouraged. Use `no_of_funds`
## instead.
tmp.df <- df.coys[9:10,] #Name of Top 10-12 companies
plot.data <- subset(mthly.plot.df, company %in% tmp.df$company)
ploter.fn.1(plot.data[plot.data$year >= max.yr-2,])## Warning: Use of `plot.data$year` is discouraged. Use `year` instead.## Warning: Use of `plot.data$year` is discouraged. Use `year` instead.## Warning: Use of `plot.data$month` is discouraged. Use `month` instead.## Warning: Use of `plot.data$no_of_funds` is discouraged. Use `no_of_funds`
## instead.## Warning: Use of `plot.data$year` is discouraged. Use `year` instead.## Warning: Use of `plot.data$month` is discouraged. Use `month` instead.## Warning: Use of `plot.data$no_of_funds` is discouraged. Use `no_of_funds`
## instead.
cat("Looking at the chart above we can see that there is no pattern of AMC's launching new funds. Some AMC's launch funds in Jan-Feb period while the others launch them during different times of the year.")Looking at the chart above we can see that there is no pattern of AMC’s launching new funds. Some AMC’s launch funds in Jan-Feb period while the others launch them during different times of the year.
ploter.fn.2 <- function(rx.df, yr.1, yr.2){
rx.df <- rx.df[rx.df$year==yr.1 | rx.df$year==yr.2, ]
gg.plot <- ggplot(rx.df , aes(x=rx.df$month , y= rx.df$no_of_funds )) +
geom_line(aes(colour=rx.df$year, group=rx.df$year), show.legend = FALSE)+
geom_point(aes(colour=rx.df$year), size=3, show.legend = FALSE)+scale_fill_brewer(palette = "Set1")+
facet_wrap(~year, nrow=1, scales = "free")+
theme(axis.text.x=element_text(angle = 90, hjust = 0))+
scale_x_discrete(limits = c("January",
"February","March","April",
"May","June","July","August","September","October","November","December"))
#return(g1.plot)
gg.plot
}
pandoc.header(paste('1G.Total MFs launched since', max.yr-9 ,' to ', max.yr), 1, 'setext')1G.Total MFs launched since 2010 to 2019
ploter.fn.2(mthly.tot.plot, max.yr-9, max.yr-8)## Warning: Use of `rx.df$year` is discouraged. Use `year` instead.## Warning: Use of `rx.df$year` is discouraged. Use `year` instead.## Warning: Use of `rx.df$month` is discouraged. Use `month` instead.## Warning: Use of `rx.df$no_of_funds` is discouraged. Use `no_of_funds` instead.## Warning: Use of `rx.df$year` is discouraged. Use `year` instead.## Warning: Use of `rx.df$month` is discouraged. Use `month` instead.## Warning: Use of `rx.df$no_of_funds` is discouraged. Use `no_of_funds` instead.
ploter.fn.2(mthly.tot.plot, max.yr-7, max.yr-6)## Warning: Use of `rx.df$year` is discouraged. Use `year` instead.## Warning: Use of `rx.df$year` is discouraged. Use `year` instead.## Warning: Use of `rx.df$month` is discouraged. Use `month` instead.## Warning: Use of `rx.df$no_of_funds` is discouraged. Use `no_of_funds` instead.## Warning: Use of `rx.df$year` is discouraged. Use `year` instead.## Warning: Use of `rx.df$month` is discouraged. Use `month` instead.## Warning: Use of `rx.df$no_of_funds` is discouraged. Use `no_of_funds` instead.
ploter.fn.2(mthly.tot.plot, max.yr-5, max.yr-4)## Warning: Use of `rx.df$year` is discouraged. Use `year` instead.## Warning: Use of `rx.df$year` is discouraged. Use `year` instead.## Warning: Use of `rx.df$month` is discouraged. Use `month` instead.## Warning: Use of `rx.df$no_of_funds` is discouraged. Use `no_of_funds` instead.## Warning: Use of `rx.df$year` is discouraged. Use `year` instead.## Warning: Use of `rx.df$month` is discouraged. Use `month` instead.## Warning: Use of `rx.df$no_of_funds` is discouraged. Use `no_of_funds` instead.
ploter.fn.2(mthly.tot.plot, max.yr-3, max.yr-2)## Warning: Use of `rx.df$year` is discouraged. Use `year` instead.## Warning: Use of `rx.df$year` is discouraged. Use `year` instead.## Warning: Use of `rx.df$month` is discouraged. Use `month` instead.## Warning: Use of `rx.df$no_of_funds` is discouraged. Use `no_of_funds` instead.## Warning: Use of `rx.df$year` is discouraged. Use `year` instead.## Warning: Use of `rx.df$month` is discouraged. Use `month` instead.## Warning: Use of `rx.df$no_of_funds` is discouraged. Use `no_of_funds` instead.
ploter.fn.2(mthly.tot.plot, max.yr-1, max.yr)## Warning: Use of `rx.df$year` is discouraged. Use `year` instead.## Warning: Use of `rx.df$year` is discouraged. Use `year` instead.## Warning: Use of `rx.df$month` is discouraged. Use `month` instead.## Warning: Use of `rx.df$no_of_funds` is discouraged. Use `no_of_funds` instead.## Warning: Use of `rx.df$year` is discouraged. Use `year` instead.## Warning: Use of `rx.df$month` is discouraged. Use `month` instead.## Warning: Use of `rx.df$no_of_funds` is discouraged. Use `no_of_funds` instead.
cat("Looking at the chart above we can see that there were two spikes in the number of new funds being lauched, first spikeis around January to February and the second spike is around November to December. In all, maximum number of funds are launched in the period from November to March. This data is being analsyed further in the document to reach conculsions regarding the performance of these funds and the subscription.")Looking at the chart above we can see that there were two spikes in the number of new funds being lauched, first spikeis around January to February and the second spike is around November to December. In all, maximum number of funds are launched in the period from November to March. This data is being analsyed further in the document to reach conculsions regarding the performance of these funds and the subscription.
pandoc.header(paste("1H. AMC's with schemes aging more than 5 yrs as on ", Sys.Date()-30) , 1, 'setext')1H. AMC’s with schemes aging more than 5 yrs as on 2020-04-07
old.schemes <- mfdata[mfdata$to_date - mfdata$from_date > 1825 & mfdata$to_date > Sys.Date()-30,]#schemes more than 5 yrs
old.schem.plot <- data.frame(tapply(old.schemes$name, old.schemes$n_company, length))
old.schem.plot$amc <- rownames(old.schem.plot)
old.schem.plot$no_of_funds <- old.schem.plot$tapply.old.schemes.name..old.schemes.n_company..length.
old.schem.plot <- old.schem.plot[,-1]
rownames(old.schem.plot) <- rep(1:nrow(old.schem.plot))
old.schem.plot <- old.schem.plot[order(old.schem.plot$no_of_funds, decreasing = TRUE),]
knitr::kable(old.schem.plot[1:10,], caption = colFmt("Top 10 AMC's with maximum number of Funds more than 5 years old ", "blue"))| amc | no_of_funds | |
|---|---|---|
| 13 | ICICI Prudential Mutual Fund | 349 |
| 25 | Nippon India Mutual Fund | 269 |
| 1 | Aditya Birla Sun Life Mutual Fund | 240 |
| 31 | SBI Mutual Fund | 211 |
| 37 | UTI Mutual Fund | 209 |
| 10 | Franklin Templeton Mutual Fund | 187 |
| 15 | IDFC Mutual Fund | 183 |
| 7 | DSP Mutual Fund | 170 |
| 11 | HDFC Mutual Fund | 168 |
| 18 | Invesco Mutual Fund | 148 |
rm(old.schemes)
min_dt <- min(mfdata$from_date)
aa <- mfdata[mfdata$from_date==min_dt & mfdata$to_date >= Sys.Date()-30,]knitr::opts_chunk$set(echo = TRUE)
aum1 <- as.data.frame(read.xlsx("amfi_aum_amcwise_one.xlsx", sheetIndex = 1))
aum2 <- as.data.frame(read.xlsx("amfi_aum_amcwise_two.xlsx", sheetIndex = 1))
period_1 <- names(aum1)[1]
period_2 <- names(aum2)[1]
pandoc.header(colFmt("Section-2: Assets Under Management(AUM)", "red"), 1, 'setext' )Section-2: Assets Under Management(AUM)
pd1 <- colnames(aum1[1])
aum1 <- aum1[,-1]
aum1 <- aum1[-1:-5,]
colnames(aum1) <- c("amc", "period1", "fof")
aum1$period1 <- as.numeric(as.character(aum1$period1))
aum1$fof <- as.numeric(as.character(aum1$fof))
pd2 <- colnames(aum2[1])
aum2 <- aum2[,-1]
aum2 <- aum2[-1:-5,]
colnames(aum2) <- c("amc", "period2", "fof")
aum2$period2 <- as.numeric(as.character(aum2$period2))
aum2$fof <- as.numeric(as.character(aum2$fof))
#fin.table.1a[is.na(fin.table.1a)] <- 0
aum.temp1 <- aum1[1:2]
aum.temp2 <- aum2[1:2]
aum.change <- merge(aum.temp1, aum.temp2, by.x = "amc", by.y = "amc")
#aum.change <- data.frame(aum1[1], aum1[2], aum2[2])
aum.change <- aum.change[aum.change$period1 !=0 & aum.change$period2 !=0,]
aum.change <- aum.change[-which(is.na(aum.change$amc)),]
aum.change$percentchange <- 0
aum.change$percentchange <- ifelse(aum.change$period1 == 0,
100,round(100*(aum.change$period2 - aum.change$period1)/aum.change$period1,2) )
cat("Period:- ", period_1, "*** to ***", period_2)Period:- Average.Assets.under.Management..AAUM..for.the.quarter.of.October…December.2019..Rs.in.Lakhs. *** to *** Average.Assets.under.Management..AAUM..for.the.quarter.of.January…March.2020..Rs.in.Lakhs.
ploter.fn.3 <- function(rx.df){
plot.data <- rx.df
ggplot(plot.data, aes(x=plot.data$amc, y=plot.data$percentchange, fill = rainbow(nrow(plot.data)))) +
geom_bar(stat = "identity", show.legend = FALSE)+
coord_flip()
}
pandoc.header("2A. Top 10 AMC's as per AUM", 1, 'setext')2A. Top 10 AMC’s as per AUM
aum.plot <- aum.change[order(aum.change$period2, decreasing = TRUE),]
aum.plot <- aum.plot[1:10,]
aum.plot$amc <- factor(as.factor(aum.plot$amc), levels=aum.plot$amc)
ggplot(aum.plot, aes(x=aum.plot$amc, y=aum.plot$period2, fill=rainbow(10)))+
geom_bar(stat = "identity", show.legend = FALSE)+coord_flip()+
scale_y_continuous(name="Amount in Lakhs", labels = comma)## Warning: Use of `aum.plot$amc` is discouraged. Use `amc` instead.## Warning: Use of `aum.plot$period2` is discouraged. Use `period2` instead.
##headings from pd1 and pd2 need to be extracted
pandoc.header("2B. Top 10 AMC's whose AUM has grown in the Period", 1, 'setext')2B. Top 10 AMC’s whose AUM has grown in the Period
aum.plot <- aum.change[aum.change$percentchange > 0,]
aum.plot <- aum.plot[order(aum.plot$percentchange, decreasing = TRUE),]
plot2.1 <- aum.plot[1:10,]
plot2.1$amc <- factor(as.factor(plot2.1$amc), levels = plot2.1$amc)
ploter.fn.3(plot2.1)## Warning: Use of `plot.data$amc` is discouraged. Use `amc` instead.## Warning: Use of `plot.data$percentchange` is discouraged. Use `percentchange`
## instead.
pandoc.header("2C. 10 AMC's whose AUM has reduced in the Period", 1, 'setext')2C. 10 AMC’s whose AUM has reduced in the Period
aum.plot <- aum.change[aum.change$percentchange < 0,]
aum.plot <- aum.plot[order(aum.plot$percentchange, decreasing = TRUE),]
plot2.1 <- aum.plot[1:10,]
plot2.1$amc <- factor(as.factor(plot2.1$amc), levels = plot2.1$amc)
ploter.fn.3(plot2.1)## Warning: Use of `plot.data$amc` is discouraged. Use `amc` instead.
## Warning: Use of `plot.data$percentchange` is discouraged. Use `percentchange`
## instead.
knitr::opts_chunk$set(echo = TRUE)
pandoc.header(colFmt("Section-3: Schemewise Analysis", "red"), 1, 'setext')Section-3: Schemewise Analysis
pandoc.header("3A. Schemes whose AUM has increased maximum from Mar 2019 to Jun 2019", 1, 'setext')3A. Schemes whose AUM has increased maximum from Mar 2019 to Jun 2019
aum.s1 <- as.data.frame(read.xlsx("amfi_aum_schemewise_one.xlsx", sheetIndex = 1))
aum.s2 <- as.data.frame(read.xlsx("amfi_aum_schemewise_two.xlsx", sheetIndex = 1))
aum.s1 <- aum.s1[-1:-6,]
aum.s1 <- aum.s1[-which(is.na(aum.s1$NA..1)),]
colnames(aum.s1) <- c("amfi_code", "scheme", "period1", "fof")
aum.s2 <- aum.s2[-1:-6,]
aum.s2 <- aum.s2[-which(is.na(aum.s2$NA..1)),]
colnames(aum.s2) <- c("amfi_code", "scheme", "period2", "fof")
aum.change.scheme <- merge(aum.s1, aum.s2, by.x = "amfi_code", by.y = "amfi_code")
aum.change.scheme$period1 <- as.numeric(as.character(aum.change.scheme$period1))
aum.change.scheme$period2 <- as.numeric(as.character(aum.change.scheme$period2))
aum.change.scheme$percentchange <- ifelse(aum.change.scheme$period1 == 0,
100,round(100*(aum.change.scheme$period2 -
aum.change.scheme$period1)/aum.change.scheme$period1,2))
old.schemes <- mfdata[mfdata$to_date - mfdata$from_date > 1825 & mfdata$to_date > Sys.Date()-30,]#schemes more than 5 yrs
aum.change.scheme <- subset(aum.change.scheme, amfi_code %in% old.schemes$code)
aum.change.scheme <- aum.change.scheme[order(aum.change.scheme$percentchange, decreasing = TRUE),]
#aum.change.scheme <- aum.change.scheme[aum.change.scheme$]
knitr::kable(aum.change.scheme[1:20,c(2,8)], caption = colFmt("Top 20 Schemes and their Percent growth in AUM in the last three months ", "blue"))| scheme.x | percentchange | |
|---|---|---|
| 2858 | BNP Paribas Short Term Fund - Direct Plan - Monthly Dividend Option | 26808 |
| 3702 | BNP PARIBAS LIQUID FUND – DIRECT PLAN - WEEKLY DIVIDEND OPTION | 20213 |
| 2355 | DSP Strategic Bond Fund - Direct Plan - Daily Dividend | 6305 |
| 2641 | Sundaram Money Fund - Direct Plan - Weekly Dividend Reinvestment Option | 4124 |
| 1439 | Essel Ultra Short Term Fund - Regular Plan- Monthly Dividend Option | 4105 |
| 2813 | BNP Paribas LOW DURATION Fund - Direct Plan - Daily Dividend Option | 4073 |
| 1677 | Invesco India Short Term Fund- Discretionary Dividend | 1763 |
| 3269 | Quant Multi Asset Fund-GROWTH OPTION-Direct Plan | 827 |
| 2306 | Indiabulls Ultra Short Term Fund - Direct Plan - Growth Option | 674 |
| 1871 | IDFC Low Duration Fund -Direct Plan-Monthly Dividend | 656 |
| 2724 | L&T Banking and PSU Debt Fund - Direct Plan - Daily Dividend Option | 625 |
| 3342 | L&T Liquid Fund - Direct Plan - Weekly Dividend | 475 |
| 1049 | Principal Ultra Short Term Fund-Monthly Dividend Option | 474 |
| 2756 | SBI MAGNUM ULTRA SHORT DURATION FUND -DIRECT PLAN - WEEKLY DIVIDEND | 462 |
| 3118 | Invesco India Treasury Advantage Fund - Direct Plan - Discretionary Dividend option | 379 |
| 3023 | Invesco India Banking & PSU Debt Fund - Direct Plan -Daily Dividend Option | 341 |
| 1491 | ICICI Prudential Gold ETF | 327 |
| 2708 | Kotak Global Emerging Market Fund - Growth - Direct | 323 |
| 2890 | ICICI Prudential Money Market Fund Option - Direct Plan -Monthly Dividend | 319 |
| 1201 | HSBC Flexi Debt Fund - Half Yearly Dividend | 318 |
rm(old.schemes)
rm(aum.change.scheme)
rm(aum.s1)
rm(aum.s2)knitr::opts_chunk$set(echo = TRUE)
pandoc.header(colFmt("Section-4: Industry wise Analysis", "red"),1,'setext')Section-4: Industry wise Analysis
pandoc.header("4A. Change in the investments by Industry Dec 2019 to Mar 2020", 1, 'setext')4A. Change in the investments by Industry Dec 2019 to Mar 2020
funds.s2 <- as.data.frame(read.xlsx("sebi_DepyofFunds_2.xlsx", sheetIndex = 1))
funds.s1 <- as.data.frame(read.xlsx("sebi_DepyofFunds_1.xlsx", sheetIndex = 1))
sector.details <- as.data.frame(read.xlsx("sectors.xlsx", sheetIndex = 1))
funds.s1 <- funds.s1[,c(-2,-5)]
funds.s2 <- funds.s2[,c(-2,-5)]
colnames(funds.s1) <- c("sector", "aum", "percent")
colnames(funds.s2) <- c("sector", "aum_new", "percent_new")
sector.change <- merge(sector.details, funds.s1, by.x = "sectors", by.y = "sector")
sector.change <- merge(sector.change, funds.s2, by.x = "sectors", by.y = "sector")
sector.change$aum <- as.numeric(as.character(sector.change$aum))
sector.change$percent <- as.numeric(as.character(sector.change$percent))
sector.change$aum_new <- as.numeric(as.character(sector.change$aum_new))
sector.change$percent_new <- as.numeric(as.character(sector.change$percent_new))
sector.change$percent_change <- sector.change$percent_new - sector.change$percent
sector.change$type <- ifelse(sector.change$percent_change >= 0,"Increase","Decrease")
sector.change <- sector.change[order(sector.change$percent_change),]
sector.change$sectors <- factor(as.factor(sector.change$sectors), levels = sector.change$sectors)
knitr::kable(sector.change[1:5,c(1,5,6,7,8)], caption = colFmt("Five sectors with maximum reduction in investment in the last three months", "blue"))| sectors | aum_new | percent_new | percent_change | type | |
|---|---|---|---|---|---|
| 3 | BANKS | 191055 | 21.264 | -4.035 | Decrease |
| 15 | FINANCE | 84479 | 9.403 | -0.833 | Decrease |
| 7 | CONSTRUCTION PROJECT | 25270 | 2.813 | -0.739 | Decrease |
| 24 | NON - FERROUS METALS | 6727 | 0.749 | -0.440 | Decrease |
| 1 | AUTO | 26992 | 3.004 | -0.435 | Decrease |
knitr::kable(sector.change[(nrow(sector.change)-4) : nrow(sector.change),c(1,5,6,7,8)], caption = colFmt("Top Five sectors where MF's have increased investment in the last three months", "blue"))| sectors | aum_new | percent_new | percent_change | type | |
|---|---|---|---|---|---|
| 31 | POWER | 32830 | 3.65 | 0.520 | Increase |
| 33 | SOFTWARE | 80444 | 8.95 | 0.985 | Increase |
| 35 | TELECOM - SERVICES | 26063 | 2.90 | 0.994 | Increase |
| 30 | PHARMACEUTICALS | 59082 | 6.58 | 1.470 | Increase |
| 9 | CONSUMER NON DURABLES | 84709 | 9.43 | 1.757 | Increase |
ggplot(sector.change, aes(x=sector.change$sectors, y=sector.change$percent_change))+
geom_bar(stat = 'identity', aes(fill=sector.change$type), width = .5, show.legend = FALSE)+coord_flip()## Warning: Use of `sector.change$type` is discouraged. Use `type` instead.## Warning: Use of `sector.change$sectors` is discouraged. Use `sectors` instead.## Warning: Use of `sector.change$percent_change` is discouraged. Use
## `percent_change` instead.
sector.change <- sector.change[sector.change$percent_change > 0.0125 | sector.change$percent_change < -.01, ]
ggplot(sector.change, aes(x=sector.change$sectors, y=sector.change$percent_change))+
geom_bar(stat = 'identity', aes(fill=sector.change$type), width = .5, show.legend = FALSE)+coord_flip()## Warning: Use of `sector.change$type` is discouraged. Use `type` instead.## Warning: Use of `sector.change$sectors` is discouraged. Use `sectors` instead.## Warning: Use of `sector.change$percent_change` is discouraged. Use
## `percent_change` instead.