# Load packages

# Core
library(tidyverse)
library(tidyquant)

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( "AMZN", "LOW", "TJX", "CMG", "CVS")

prices <- tq_get(x = symbols,
                 get = "stock.prices",
                 from = "2012-12-31",
                 to = "2020-12-31")

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 a weight to each asset

# symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "AMZN" "CMG"  "CVS"  "LOW"  "TJX"
#weights
weights <- c(0.25, 0.25, 0.2, 0.2, 0.1)
weights
## [1] 0.25 0.25 0.20 0.20 0.10
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AMZN       0.25
## 2 CMG        0.25
## 3 CVS        0.2 
## 4 LOW        0.2 
## 5 TJX        0.1

4 Build a portfolio

# ?tq_portfolio()
portfolio_returns_tbl <- asset_returns_tbl %>% 
  
  tq_portfolio(assets_col = asset,returns_col = returns, 
               weights = w_tbl,
               rebalance_on = "months")
portfolio_returns_tbl
## # A tibble: 96 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2013-01-31           0.0560 
##  2 2013-02-28           0.00594
##  3 2013-03-28           0.0264 
##  4 2013-04-30           0.0347 
##  5 2013-05-31           0.0335 
##  6 2013-06-28           0.00175
##  7 2013-07-31           0.0884 
##  8 2013-08-30          -0.0243 
##  9 2013-09-30           0.0489 
## 10 2013-10-31           0.126  
## # ℹ 86 more rows

5 Calculate Standard Deviation

portfolio_sd_tidyquant_builtin_percent <- portfolio_returns_tbl %>%

tq_performance(Ra = portfolio.returns, 
                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 0.0548  5.48
# Mean of portfolio returns
portfolio_mean_tidyquant_builtin_percent <- 
mean(portfolio_returns_tbl$portfolio.returns)

portfolio_mean_tidyquant_builtin_percent
## [1] 0.01651923

6 Plot

sd_mean_tbl <- asset_returns_tbl %>%
  
  group_by(asset) %>%
  tq_performance(Ra = returns, 
                performance_fun = table.Stats) %>%
  select(Mean = ArithmeticMean, Stdev) %>%
  ungroup() %>%
  mutate(Stdev = Stdev * 100,
         Mean = Mean   * 100 ) %>%
  
  # Add Portfolio sd
  add_row(tibble(asset ="Portfolio",
                 Mean = portfolio_mean_tidyquant_builtin_percent * 100,
                 Stdev = portfolio_sd_tidyquant_builtin_percent$tq_sd))

sd_mean_tbl
## # A tibble: 6 × 3
##   asset      Mean Stdev
##   <chr>     <dbl> <dbl>
## 1 AMZN       2.68  8.02
## 2 CMG        1.59  9.34
## 3 CVS        0.54  6.87
## 4 LOW        1.72  7.31
## 5 TJX        1.32  5.72
## 6 Portfolio  1.65  5.48

Expected returns vs Risk

sd_mean_tbl %>%
  
  ggplot(aes(x = Stdev, y = Mean, color = asset)) +
  geom_point() + 
  ggrepel::geom_text_repel(aes(label = asset))

### 24 Monlths Rolling Volatility

rolling_sd_tbl <- portfolio_returns_tbl %>%
  tq_mutate(select = portfolio.returns,
            mutate_fun = rollapply,
            width = 24,
            FUN = sd,
            col_rename = "rolling_sd") %>%
  
  na.omit() %>% 
  select(date, rolling_sd)

rolling_sd_tbl
## # A tibble: 73 × 2
##    date       rolling_sd
##    <date>          <dbl>
##  1 2014-12-31     0.0438
##  2 2015-01-30     0.0435
##  3 2015-02-27     0.0434
##  4 2015-03-31     0.0439
##  5 2015-04-30     0.0444
##  6 2015-05-29     0.0444
##  7 2015-06-30     0.0445
##  8 2015-07-31     0.0481
##  9 2015-08-31     0.0489
## 10 2015-09-30     0.0489
## # ℹ 63 more rows
rolling_sd_tbl  %>%
  
  ggplot(aes(x = date, y = rolling_sd)) + 
  geom_line(color = "cornflowerblue") +

  # Formatting
  scale_y_continuous(labels = scales::percent_format()) + 

  # Labeling
  labs(x = NULL, 
       y = NULL, 
       title = "24-Month Rolling Volatility") + 
  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.