Franklin-India-Prima.R.md

Franklin Prima Portfolio at the turn

In our notebook on mutal fund drawdowns, we saw how Franklin India Prima Fund drew down the least compared to other large midcap mutual funds. How did the portfolio change through time?

library(tidyverse)
library(ggthemes)
library(odbc)
library(plutoR)
library(quantmod)
library(PerformanceAnalytics)

options("scipen"=999)
options(stringsAsFactors = FALSE)
options(repr.plot.width=16, repr.plot.height=8)

source("config.R")
source("goofy/plot.common.R")
source("goofy/misc.common.R")

indices <- Indices()
mfi <- MutualFundsIndia()

── �[1mAttaching packages�[22m ─────────────────────────────────────── tidyverse 1.2.1 ──
�[32m✔�[39m �[34mggplot2�[39m 3.2.0     �[32m✔�[39m �[34mpurrr  �[39m 0.3.2
�[32m✔�[39m �[34mtibble �[39m 2.1.3     �[32m✔�[39m �[34mdplyr  �[39m 0.8.1
�[32m✔�[39m �[34mtidyr  �[39m 0.8.3     �[32m✔�[39m �[34mstringr�[39m 1.4.0
�[32m✔�[39m �[34mreadr  �[39m 1.3.1     �[32m✔�[39m �[34mforcats�[39m 0.4.0
── �[1mConflicts�[22m ────────────────────────────────────────── tidyverse_conflicts() ──
�[31m✖�[39m �[34mdplyr�[39m::�[32mfilter()�[39m masks �[34mstats�[39m::filter()
�[31m✖�[39m �[34mdplyr�[39m::�[32mlag()�[39m    masks �[34mstats�[39m::lag()
Loading required package: xts
Loading required package: zoo

Attaching package: ‘zoo’

The following objects are masked from ‘package:base’:

    as.Date, as.Date.numeric


Attaching package: ‘xts’

The following objects are masked from ‘package:dplyr’:

    first, last

Loading required package: TTR
Version 0.4-0 included new data defaults. See ?getSymbols.

Attaching package: ‘PerformanceAnalytics’

The following object is masked from ‘package:graphics’:

    legend

Registering fonts with R

Attaching package: ‘gridExtra’

The following object is masked from ‘package:dplyr’:

    combine


Attaching package: ‘lubridate’

The following object is masked from ‘package:base’:

    date

The big picture

How did the fund perform over the Midcap 150 TR index over a 5 year period?

indexName <- "NIFTY MIDCAP 150 TR"
schemeCode <- 100473
schemeName <- "Franklin India Prima"
startDate <- as.Date("2014-01-01")
endDate <- as.Date("2019-06-30")

#get the daily returns of the fund
navs <- data.frame(mfi$NavTimeSeries() %>%
        filter(SCHEME_CODE == schemeCode & TIME_STAMP >= startDate & TIME_STAMP <= endDate) %>%
        select(TIME_STAMP, NAV))
    
navXts <- dailyReturn(xts(navs$NAV, navs$TIME_STAMP))

#grab the benchmark
bm <- data.frame(indices$NseTimeSeries() %>%
    filter(NAME == indexName & TIME_STAMP >= startDate & TIME_STAMP <= endDate) %>%
    select(TIME_STAMP, CLOSE))

navXts <- merge.xts(navXts, dailyReturn(xts(bm$CLOSE, bm$TIME_STAMP)))
names(navXts) <- c("FUND", indexName)

# plot the cumulative returns
Common.PlotCumReturns(navXts, sprintf("%s vs. %s", schemeName, indexName), "")

How was the fund positioned going into 2018 vs. where it was in March 2019?

date1 <- as.Date("2017-12-31")
date2 <- as.Date("2019-03-31")

sectorAlloc1 <- mfi$Portfolio() %>%
    filter(SCHEME_CODE == schemeCode 
           & INSTRUMENT == 'EQUITY' 
           & !is.null(WEIGHTAGE)
           & WEIGHTAGE > 0
           & PORTFOLIO_DATE == date1
           & (INSTRUMENT_TYPE == 'STOCK' 
              | INSTRUMENT_TYPE == 'E' 
              | INSTRUMENT_TYPE == '' 
              | is.null(INSTRUMENT_TYPE))) %>%
    group_by(INDUSTRY_BSE) %>%
    summarize(ALLOC = sum(WEIGHTAGE)) %>%
    arrange(desc(ALLOC)) %>%
    collect()

sectorAlloc1Df <- sectorAlloc1 %>%
    mutate(INDUSTRY_BSE = replace(INDUSTRY_BSE, INDUSTRY_BSE == '-' 
                                  | is.na(INDUSTRY_BSE), 'UNK')) %>%
    as.data.frame()

sectorAlloc2 <- mfi$Portfolio() %>%
    filter(SCHEME_CODE == schemeCode 
           & INSTRUMENT == 'EQUITY' 
           & !is.null(WEIGHTAGE)
           & WEIGHTAGE > 0
           & PORTFOLIO_DATE == date2
           & (INSTRUMENT_TYPE == 'STOCK' 
              | INSTRUMENT_TYPE == 'E' 
              | INSTRUMENT_TYPE == '' 
              | is.null(INSTRUMENT_TYPE))) %>%
    group_by(INDUSTRY_BSE) %>%
    summarize(ALLOC = sum(WEIGHTAGE)) %>%
    arrange(desc(ALLOC)) %>%
    collect()

sectorAlloc2Df <- sectorAlloc2 %>%
    mutate(INDUSTRY_BSE = replace(INDUSTRY_BSE, INDUSTRY_BSE == '-' 
                                  | is.na(INDUSTRY_BSE), 'UNK')) %>%
    as.data.frame()

Warning message:
“Missing values are always removed in SQL.
Use `SUM(x, na.rm = TRUE)` to silence this warning
This warning is displayed only once per session.”

pie(sectorAlloc1Df$ALLOC, 
    labels=sprintf("%s (%.2f%%)", sectorAlloc1Df$INDUSTRY, sectorAlloc1Df$ALLOC), 
    col=economist_pal()(9), 
    main=sprintf("%s Sector Allocations (%s)", schemeName, date1), 
    family='Segoe UI', 
    init.angle=45)
mtext("@StockViz", side=1)

pie(sectorAlloc2Df$ALLOC, 
    labels=sprintf("%s (%.2f%%)", sectorAlloc2Df$INDUSTRY, sectorAlloc2Df$ALLOC), 
    col=economist_pal()(9), 
    main=sprintf("%s Sector Allocations (%s)", schemeName, date2), 
    family='Segoe UI', 
    init.angle=45)
mtext("@StockViz", side=1)

# zoom into allocations that are more than 2%

sectorAlloc1Df2 <- sectorAlloc1Df[sectorAlloc1Df$ALLOC > 2, ]
sectorAlloc2Df2 <- sectorAlloc2Df[sectorAlloc2Df$ALLOC > 2, ]

pie(sectorAlloc1Df2$ALLOC, 
    labels=sprintf("%s (%.2f%%)", sectorAlloc1Df2$INDUSTRY, sectorAlloc1Df2$ALLOC), 
    col=economist_pal()(9), 
    main=sprintf("%s Sector Allocations (%s) > 2%%", schemeName, date1), 
    family='Segoe UI', 
    init.angle=45)
mtext("@StockViz", side=1)

pie(sectorAlloc2Df2$ALLOC, 
    labels=sprintf("%s (%.2f%%)", sectorAlloc2Df2$INDUSTRY, sectorAlloc2Df2$ALLOC), 
    col=economist_pal()(9), 
    main=sprintf("%s Sector Allocations (%s) > 2%%", schemeName, date2), 
    family='Segoe UI', 
    init.angle=45)
mtext("@StockViz", side=1)

This notebook was created using pluto. Learn more here