Apply it to your data 6
How does your portfolio expect perform regarding expected returns and risk?
Cameron Whitty
# Load packages
# Core
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(ggrepel)Goal
Visualize expected returns and risk to make it easier to compare the performance of multiple assets and portfolios.
Choose your stocks.
from 2012-12-31 to 2017-12-31
1 Import stock prices
symbols <- c("XOM", "CVX", "COP", "BP", "SHEL")
# Using tq_get() ----
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 %>%
# Calculate monthly returns
group_by(symbol) %>%
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
period = "monthly",
type = "log") %>%
slice(-1) %>%
ungroup() %>%
# remane
set_names(c("asset", "date", "returns"))
# period_returns = c("yearly", "quarterly", "monthly", "weekly")3 Assign a weight to each asset
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
w <- c(0.25,
0.25,
0.20,
0.20,
0.10)
w_tbl <- tibble(symbols, w)4 Build a portfolio
portfolio_returns_rebalanced_monthly_tbl <- asset_returns_tbl %>%
tq_portfolio(assets_col = asset,
returns_col = returns,
weights = w_tbl,
col_rename = "returns",
rebalance_on = "months")## Warning in check_weights(weights, assets_col, map, x): Sum of weights does not
## equal 1.portfolio_returns_rebalanced_monthly_tbl## # A tibble: 60 × 2
## date returns
## <date> <dbl>
## 1 2013-01-31 0.0377
## 2 2013-02-28 -0.0251
## 3 2013-03-28 0.0228
## 4 2013-04-30 0.0212
## 5 2013-05-31 0.00831
## 6 2013-06-28 -0.0257
## 7 2013-07-31 0.0481
## 8 2013-08-30 -0.0144
## 9 2013-09-30 0.0200
## 10 2013-10-31 0.0457
## # ℹ 50 more rows# write_rds(portfolio_returns_rebalanced_monthly_tbl,
# "00_data/Ch03_portfolio_returns_rebalanced_monthly_tbl.rds")5 Compute Standard Deviation
portfolio_sd_tidyquant_builtin_percent <- portfolio_returns_rebalanced_monthly_tbl %>%
tq_performance(Ra = returns,
Rb = NULL,
performance_fun = table.Stats) %>%
select(Stdev) %>%
mutate(tq_sd = round(Stdev, 4) * 100)
portfolio_sd_tidyquant_builtin_percent## # A tibble: 1 × 2
## Stdev tq_sd
## <dbl> <dbl>
## 1 0.0507 5.076 Plot
portfolio_returns_rebalanced_monthly_tbl %>%
ggplot(aes(date, returns)) +
geom_point(color = "cornflowerblue", size = 2) +
labs(title = "Scatterplot of Returns by Date") +
theme(plot.title = element_text(hjust = 0.5))
sd_plot <- sd(portfolio_returns_rebalanced_monthly_tbl$returns)
mean_plot <- mean(portfolio_returns_rebalanced_monthly_tbl$returns)
portfolio_returns_rebalanced_monthly_tbl %>%
mutate(hist_col = case_when(
returns > mean_plot + sd_plot ~ "high",
returns < mean_plot - sd_plot ~ "middle",
TRUE ~ "low"
)) %>%
ggplot(aes(date, returns, col = hist_col)) +
geom_point(size = 2) +
labs(title = "Colored Scatter") +
theme(plot.title = element_text(hjust = 0.5))
sd_plot <- sd(portfolio_returns_rebalanced_monthly_tbl$returns)
mean_plot <- mean(portfolio_returns_rebalanced_monthly_tbl$returns)
portfolio_returns_rebalanced_monthly_tbl %>%
mutate(hist_col = case_when(
returns > mean_plot + sd_plot ~ "high",
returns < mean_plot - sd_plot ~ "middle",
TRUE ~ "low"
)) %>%
ggplot(aes(date, returns, col = hist_col)) +
geom_point(size = 2) +
labs(title = "Colored Scatter with Line") +
theme(plot.title = element_text(hjust = 0.5)) +
geom_hline(yintercept = mean_plot + sd_plot, linetype = "dotted", color = "purple") +
geom_hline(yintercept = mean_plot - sd_plot, linetype = "dotted", color = "purple")
portfolio_returns_rebalanced_monthly_tbl## # A tibble: 60 × 2
## date returns
## <date> <dbl>
## 1 2013-01-31 0.0377
## 2 2013-02-28 -0.0251
## 3 2013-03-28 0.0228
## 4 2013-04-30 0.0212
## 5 2013-05-31 0.00831
## 6 2013-06-28 -0.0257
## 7 2013-07-31 0.0481
## 8 2013-08-30 -0.0144
## 9 2013-09-30 0.0200
## 10 2013-10-31 0.0457
## # ℹ 50 more rowsasset_returns_sd_tbl <- asset_returns_tbl %>%
group_by(asset) %>%
tq_performance(Ra = returns,
Rb = NULL,
performance_fun = table.Stats) %>%
select(asset, Stdev) %>%
ungroup() %>%
add_row(tibble(asset = "Portfolio",
Stdev = sd(portfolio_returns_rebalanced_monthly_tbl$returns)))
asset_returns_sd_tbl %>%
# Plot
ggplot(aes(asset, Stdev, col = asset)) +
geom_point() +
ggrepel::geom_text_repel(aes(label = asset),
data = asset_returns_sd_tbl %>%
filter(asset == "Portfolio")) +
labs(title = "")
asset_returns_sd_mean_tbl <- asset_returns_tbl %>%
group_by(asset) %>%
tq_performance(Ra = returns,
Rb = NULL,
performance_fun = table.Stats) %>%
select(asset, Mean = ArithmeticMean, Stdev) %>%
ungroup() %>%
add_row(tibble(asset = "Portfolio",
Mean = mean(portfolio_returns_rebalanced_monthly_tbl$returns),
Stdev = sd(portfolio_returns_rebalanced_monthly_tbl$returns)))
asset_returns_sd_mean_tbl %>%
ggplot(aes(Stdev, Mean, col = asset)) +
geom_point() +
ggrepel::geom_text_repel(aes(label = asset))
window <- 24
port_rolling_sd_tbl <- portfolio_returns_rebalanced_monthly_tbl %>%
tq_mutate(select = returns,
mutate_fun = rollapply,
width = window,
FUN = sd,
col_rename = "rolling_sd") %>%
select(date, rolling_sd) %>%
na.omit()
port_rolling_sd_tbl %>%
ggplot(aes(date, rolling_sd)) +
geom_line(color = "cornflowerblue") +
scale_y_continuous(labels = scales::percent) +
scale_x_date(breaks = scales::breaks_pretty(n = 7))+
labs(title = "24-Month Rolling Volatility",
x = NULL,
y = NULL) +
theme(plot.title = element_text(hjust = 0.5))
How should you expect your portfolio to perform relative to its assets in the portfolio? Would you invest all your money in any of the individual stocks instead of the portfolio? Discuss both in terms of expected return and risk.
I would invest my money in Shell because, based on the data, it offers the strongest potential for profit among the companies I evaluated. It has the highest expected return while also carrying the lowest level of risk, making it the most attractive option overall.