Health_Care
Mandar
2022-08-26
knitr::opts_chunk$set(echo = TRUE,fig.align = "center",
message = FALSE,
warning = FALSE)
options(tinytex.verbose = TRUE)#The healthcare sector consists of businesses that provide medical services, manufacture medical equipment or drugs, provide medical insurance, or otherwise facilitate the provision of healthcare to patients. The healthcare sector is one of the largest and most complex in the U.S. economy, accounting for 18% of gross domestic product (GDP) in 2020. The U.S. healthcare sector benefits from a strong system of medical research and development, in cooperation with the higher education system and the technology industry. The aging U.S. population and the advancing senescence of the Baby Boomer generation are driving ongoing strong demand in the healthcare sector.
1 loading the libraries
library(tidymodels)
library(tidyquant)
library(tidyverse)
library(scales)
library(kableExtra)
library(lubridate)
library(ggplot2)
library(plotly)
library(timetk)
library(modeltime)
library(DataExplorer)
library(correlationfunnel)2 Data for Health Sector
# tickers
symbols <- c("JNJ","PFE","CVS","UNH","AMGN") %>% sort()
# Stock prices
prices <- symbols %>% tq_get(get = "stock.prices",from = "1990-01-01")
# monthly Returns
returns <-prices %>%
group_by(symbol) %>%
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
period = "monthly") %>%
ungroup() %>%
mutate(date = rollback(date, roll_to_first = TRUE))
#visualiaztion
returns %>%
group_by(symbol) %>%
ggplot(aes(date, monthly.returns, color = symbol)) +
geom_line() + scale_y_continuous(labels = scales::percent) +
theme_tq() + facet_wrap(~symbol) + scale_color_tq(theme = "light") +
labs(
title = "Stocks Returns",
subtitle = "Health Sector",
caption = "Stocks(JNJ,PFE,CVS,UNH,AMGN)",
x = "",
y = "Returns"
) + theme(legend.position = "none")
# Portfolio Creation
#Weights Allocation
w <- rep(1/5, 5)
# Portfolio Creation
port_ret_tbl <- returns %>%
tq_portfolio(assets_col = symbol,
returns_col = monthly.returns,
weights = w) %>%
add_column(symbol = "Portfolio", .before = 1)
# Visualization
## ggplot
port_ret_tbl %>%
ggplot(aes(date, portfolio.returns)) +
geom_line() + scale_y_continuous(labels = scales::percent) + geom_smooth(method = "loess") +
labs(title = "Portfolio Returns",
subtitle = "Healthcare Sector",
x = "",
y = "Returns")
## timetk
port_ret_tbl %>%
plot_time_series(
.date_var = date,
.value = portfolio.returns,
.title = "Portfolio Returns",
.x_lab = "",
.y_lab = "Returns"
)3 S&P 500
market_prices <- tq_get("^GSPC", get = "stock.prices",from = "1990-01-01")
market_returns <- market_prices %>%
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
period = "monthly",
col_rename = "market_ret") %>%
mutate(date = rollback(date, roll_to_first = TRUE))4 Fama-French Data
library(frenchdata)
factors_ff_monthly_raw <- download_french_data("Fama/French 3 Factors")
factors_ff_monthly <- factors_ff_monthly_raw$subsets$data[[1]] |>
transmute(
date = floor_date(ymd(str_c(date,"01")),"month"),
rf = as.numeric(RF) / 100,
mkt_excess = as.numeric(`Mkt-RF`) / 100,
smb = as.numeric(SMB) / 100,
hml = as.numeric(HML) / 100
) %>%
# filter the dates
filter(date >= "1990-01-01")
factors_ff_monthly 5 Combining all the data
port_risk_market_tbl <- port_ret_tbl %>%
left_join(factors_ff_monthly, by = "date") %>%
left_join(market_returns, by = "date") %>%
mutate(excess_returns = portfolio.returns - rf) %>%
mutate(market_excess_returns = market_ret - rf) %>%
select(symbol, date, mkt_excess, smb, hml, excess_returns, market_excess_returns)6 Beta Estimation
6.1 Step 1 : CAPM Function
library(slider)
# estimate capm
estimate_capm <- function(data, min_obs = 1) {
if (nrow(data) < min_obs) {
beta <- as.numeric(NA)
} else {
fit <- lm(excess_returns ~ mkt_excess + smb+hml+ market_excess_returns, data = data)
beta <- as.numeric(fit$coefficients[2])
}
return(beta)
}
# rolling capm estimation
roll_capm_estimation <- function(data, months, min_obs) {
data <- bind_rows(data) |>
arrange(date)
betas <- slide_period_vec(
.x = data,
.i = data$date,
.period = "month",
.f = ~estimate_capm(., min_obs),
.before = months - 1,
.complete = FALSE
)
tibble(
month = unique(data$date),
beta = betas
)
}6.2 Step 2 : Estimation
beta_port_tbl <- port_risk_market_tbl %>%
mutate(roll_capm_estimation(cur_data(), months = 60, min_obs = 48)) %>%
select(symbol, date, beta) %>%
drop_na() 6.3 Step 3 : Visualization
beta_port_tbl %>%
plot_time_series(
.date_var = date,
.value = beta,
.title = "Rolling Beta ",
.x_lab = "",
.y_lab = "Beta"
)6.4 ggplot
beta_port_tbl %>%
ggplot(aes(date, beta))+
geom_line() + geom_smooth(method = "loess") +
scale_y_continuous() + theme_tq() + scale_color_tq(theme = "light") +
labs(
title = "Rolling Beta ",
subtitle = "Healthcare Sector",
caption = "Stocks(JNJ,PFE,CVS,UNH,AMGN",
x = "",
y = "Beta"
)
## Step 4 : Beta stocks ### step 1: combining the returns, market index
and fama-french
returns_risk_market_tbl <- returns %>%
group_by(symbol) %>%
left_join(factors_ff_monthly, by = "date") %>%
left_join(market_returns, by = "date") %>%
mutate(excess_returns = monthly.returns -rf) %>%
mutate(market_excess_returns = market_ret - rf) %>%
ungroup() %>%
select(symbol, date, excess_returns, market_excess_returns, mkt_excess, smb, hml)6.4.1 step 2 : estimation
beta_stocks_tbl <- returns_risk_market_tbl %>%
group_by(symbol) %>%
mutate(roll_capm_estimation(cur_data(), months = 60, min_obs = 48)) %>%
ungroup() %>%
select(symbol, date, beta) %>%
drop_na()6.4.2 step 3 : visualization
beta_stocks_tbl %>%
group_by(symbol) %>%
ggplot(aes(date, beta, color = symbol)) +
geom_line() + scale_y_continuous() +
theme_tq() +
labs(
title = "Rolling Beta Estimates(PFE,JNJ,CVS,UNH,AMGN)",
subtitle = "Healthcare Sector Stocks",
x = "",
y = "Beta"
)
#### timetk
beta_stocks_tbl %>%
plot_time_series(
.value = beta,
.date_var = date,
.color_var = symbol,
.smooth = FALSE,
.title = "Rolling Beta estimates",
.x_lab = "",
.y_lab = "Beta",
.legend_show = FALSE
)