title: “Apply it to your data 10” subtitle: “How sensitive is your portfolio to the market?”
# Core
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsGoal
Calculate and visualize your portfolio’s beta.
Choose your stocks and the baseline market.
from 2012-12-31 to present
1 Input Stock Prices
symbols <- c("NVDA", "AAPL", "AMD", "INTC", "GOOG")
prices <- tq_get(x = symbols,
get = "stock.prices",
from = "2012-12-31",
to = "2017-12-31")2 Convert prices to returns
asset_returns_tbl <- prices %>%
group_by(symbol) %>%
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
period = "monthly",
type = "log") %>%
slice(-1) %>%
ungroup() %>%
set_names(c("asset", "date", "returns"))3 Assign Weight To Each Asset
# symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols## [1] "AAPL" "AMD" "GOOG" "INTC" "NVDA"# weights
weights <- c(0.25, 0.25, 0.2, 0.2, 0.1)
weights## [1] 0.25 0.25 0.20 0.20 0.10w_tbl <- tibble(symbols, weights)
w_tbl ## # A tibble: 5 × 2
## symbols weights
## <chr> <dbl>
## 1 AAPL 0.25
## 2 AMD 0.25
## 3 GOOG 0.2
## 4 INTC 0.2
## 5 NVDA 0.14 Build a portfolio
portfolio_returns_tbl <- asset_returns_tbl %>%
tq_portfolio(assets_col = asset,
returns_col = returns,
weights = w_tbl,
rebalance_on = "months",
col_rename = "returns")
portfolio_returns_tbl## # A tibble: 60 × 2
## date returns
## <date> <dbl>
## 1 2013-01-31 -0.00164
## 2 2013-02-28 -0.00108
## 3 2013-03-28 0.0152
## 4 2013-04-30 0.0583
## 5 2013-05-31 0.114
## 6 2013-06-28 -0.0279
## 7 2013-07-31 0.0103
## 8 2013-08-30 -0.0323
## 9 2013-09-30 0.0532
## 10 2013-10-31 0.0333
## # ℹ 50 more rows5 Calculate CAPM Beta
5.1 Get Market Returns
market_returns_tbl <- tq_get(x = "VOO",
get = "stock.prices",
from = "2012-12-31",
to = "2017-12-31") %>%
# Convert prices to returns
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
period = "monthly",
type = "log",
col_rename = "returns") %>%
slice(-1) 5.2 Join Returns
portfolio_market_returns_tbl <- left_join(market_returns_tbl,
portfolio_returns_tbl,
by = "date") %>%
set_names("date", "market_returns", "portfolio_returns")5.3 CAPM Beta
portfolio_market_returns_tbl %>%
tq_performance(Ra = portfolio_returns,
Rb = market_returns,
performance_fun = CAPM.beta)## # A tibble: 1 × 1
## CAPM.beta.1
## <dbl>
## 1 1.386 Plot
Scatterplot of returns with regression line
portfolio_market_returns_tbl %>%
ggplot(aes(x = market_returns,
y = portfolio_returns)) +
geom_point(color = "cornflowerblue") +
geom_smooth(method = "lm", se = FALSE,
size = 1.5, color =
tidyquant::palette_light()[3]) +
labs(y = "Potfolio Returns",
x = "Market Returns")## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## `geom_smooth()` using formula = 'y ~ x'
How sensitive is your portfolio to the market? Discuss in terms of the
beta coefficient. Does the plot confirm the beta coefficient you
calculated?
My portfolio is more sensitive than market. This confirms my portfolio’s Beta at 1.38 the line trends slightly lower than a 45 degree angle. As a result of this beta my portfolio follows the trendline but has more differentiation than the trend line of the market. When looking at the scatterplot by portfolio see’s returns lower than the low end of the plot line but higher than the high end of the plot line. Along with seeing a slight higher concentration whenn the market is at 0% growth my portfolio however is generally performing at 0.02% growth.
Line plot of fitted vs actual returns
actual_fitted_long_tbl <- portfolio_market_returns_tbl %>%
# Linear Regression Model
lm(portfolio_returns ~ market_returns, data = .) %>%
# . meaning: take the value that's been passed on to, here "portfolio_market_returns_tbl"
# Get fitted and actual returns
broom::augment() %>%
# Add date
mutate(date = portfolio_market_returns_tbl$date) %>%
# $: Adds a column
select(date, portfolio_returns, .fitted) %>%
# Transform data to long
pivot_longer(cols = c(portfolio_returns, .fitted),
names_to = "type",
values_to = "returns")
actual_fitted_long_tbl %>%
ggplot(aes(x = date, y = returns, color = type)) +
geom_line()