R_pdfetch_S&P500_Rolling_CAGR_Analysis

Cleaning Environment

rm(list=ls())

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(pdfetch)

## Warning: package 'pdfetch' was built under R version 4.2.2

library(ggplot2)

Fetching S&P500 data from Yahoo

SnP_500 = pdfetch_YAHOO("^GSPC", from = "2013-01-01", to = "2022-12-31") %>% data.frame() %>% tibble::rownames_to_column("date") %>% select(date,contains(".open"),contains(".close"))

SnP_500 = SnP_500 %>% mutate(Year = format(as.Date(SnP_500$date, format="%Y-%m-%d"),"%Y"))

SnP_500_2013 = SnP_500 %>% filter(Year == "2013") %>% na.omit()
SnP_500_2014 = SnP_500 %>% filter(Year == "2014") %>% na.omit()
SnP_500_2015 = SnP_500 %>% filter(Year == "2015") %>% na.omit()
SnP_500_2016 = SnP_500 %>% filter(Year == "2016") %>% na.omit()
SnP_500_2017 = SnP_500 %>% filter(Year == "2017") %>% na.omit()
SnP_500_2018 = SnP_500 %>% filter(Year == "2018") %>% na.omit()
SnP_500_2019 = SnP_500 %>% filter(Year == "2019") %>% na.omit()
SnP_500_2020 = SnP_500 %>% filter(Year == "2020") %>% na.omit()
SnP_500_2021 = SnP_500 %>% filter(Year == "2021") %>% na.omit()
SnP_500_2022 = SnP_500 %>% filter(Year == "2022") %>% na.omit()

Creating CAGR Function to use later

CAGR = function(EV,BV,n){
  
  Rate = ((EV/BV)^(1/n) - 1) * 100
  return(Rate)
  
}

Applying CAGR Function to our data

SnP_500_2013_2015_CAGR = CAGR(SnP_500_2015[nrow(SnP_500_2015),3],SnP_500_2013[1,2],3) %>% round(2)

SnP_500_2014_2016_CAGR = CAGR(SnP_500_2016[nrow(SnP_500_2016),3],SnP_500_2014[1,2],3) %>% round(2)

SnP_500_2015_2017_CAGR = CAGR(SnP_500_2017[nrow(SnP_500_2017),3],SnP_500_2015[1,2],3) %>% round(2)

SnP_500_2016_2018_CAGR = CAGR(SnP_500_2018[nrow(SnP_500_2018),3],SnP_500_2016[1,2],3) %>% round(2)

SnP_500_2017_2019_CAGR = CAGR(SnP_500_2019[nrow(SnP_500_2019),3],SnP_500_2017[1,2],3) %>% round(2)

SnP_500_2018_2020_CAGR = CAGR(SnP_500_2020[nrow(SnP_500_2020),3],SnP_500_2018[1,2],3) %>% round(2)

SnP_500_2019_2021_CAGR = CAGR(SnP_500_2021[nrow(SnP_500_2021),3],SnP_500_2019[1,2],3) %>% round(2)

SnP_500_2020_2022_CAGR = CAGR(SnP_500_2022[nrow(SnP_500_2022),3],SnP_500_2020[1,2],3) %>% round(2)

Creating df to visualize returns

df = data.frame(rbind(SnP_500_2013_2015_CAGR,
                      SnP_500_2014_2016_CAGR,
                      SnP_500_2015_2017_CAGR,
                      SnP_500_2016_2018_CAGR,
                      SnP_500_2017_2019_CAGR,
                      SnP_500_2018_2020_CAGR,
                      SnP_500_2019_2021_CAGR,
                      SnP_500_2020_2022_CAGR)) %>% tibble::rownames_to_column("Index")
names(df)[1] = "Index"
names(df)[2] = "Percent_Change"

df$Year = c("1","2","3","4","5","6","7","8")

Using ggplot2 package to create graphical visualization

ggplot(df, aes(x = Year, y = Percent_Change)) +
  geom_bar(stat = "identity", color = "blue", fill=rgb(0.1,0.4,0.5,0.7)) +
  geom_text(label = df$Percent_Change, nudge_x = 0.0, nudge_y = -0.6) + 
  labs(x = "Index", y = "CAGR") + 
  scale_x_discrete(labels=c('SnP500_2013-15', 'SnP500_2014-16','SnP500_2015-17', 'SnP500_2016-18','SnP500_2017-19', 'SnP500_2018-20', 'SnP500_2019-21', 'SnP500_2019-22'))