# 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("MSFT", "DPZ", "AAPL")

prices <- tq_get(x = symbols,
                 get = "stock.prices",
                 from = "2020-12-31",
                 to = "2025-12-31")

2 Convert prices to returns (monthly)

asset_returns_tbl <- prices %>%
    
    group_by(symbol) %>%
    
    tq_transmute(select = adjusted,
                 mutate_fun = periodReturn,
                 period = "quarterly",
                 type = "log") %>%
    
    slice(-1) %>%
    
    ungroup() %>%
    
    set_names(c("asset", "date", "returns"))

3 Assign a weight to each asset (change the weigting scheme)

# symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "AAPL" "DPZ"  "MSFT"
#weights
weights <- c(0.34, 0.33, 0.33)
weights
## [1] 0.34 0.33 0.33
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 3 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AAPL       0.34
## 2 DPZ        0.33
## 3 MSFT       0.33

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 = "quarters")

portfolio_returns_tbl
## # A tibble: 18 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2021-03-31           -0.0205
##  2 2021-06-30            0.165 
##  3 2021-09-30            0.0333
##  4 2021-12-31            0.193 
##  5 2022-03-31           -0.140 
##  6 2022-06-30           -0.156 
##  7 2022-09-30           -0.102 
##  8 2022-12-30            0.0276
##  9 2023-03-31            0.128 
## 10 2023-06-30            0.120 
## 11 2023-09-29           -0.0266
## 12 2023-12-29            0.128 
## 13 2024-03-28            0.0615
## 14 2024-06-28            0.105 
## 15 2024-09-30           -0.0362
## 16 2024-12-31            0.0117
## 17 2025-03-31           -0.0468
## 18 2025-06-18            0.0328

5 Compute 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, 3)*100)

portfolio_sd_tidyquant_builtin_percent
## # A tibble: 1 × 2
##   Stdev tq_sd
##   <dbl> <dbl>
## 1 0.102  10.2
#Mean of portfolio returns
portfolio_mean_tidyquant_builtin_percent <- mean(portfolio_returns_tbl$portfolio.returns)

portfolio_mean_tidyquant_builtin_percent
## [1] 0.02651494

6 Plot: Expected Returns versus Risk

# Expected Returns vs Risk
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: 4 × 3
##   asset      Mean Stdev
##   <chr>     <dbl> <dbl>
## 1 AAPL       2.32  14.2
## 2 DPZ        1.16  14.6
## 3 MSFT       4.49  12.5
## 4 Portfolio  2.65  10.2
sd_mean_tbl %>%
    ggplot(aes(x = Stdev, y = Mean, color = asset)) +
    geom_point() +
    ggrepel::geom_text_repel(aes(label = asset))

Quarterly Rolling Volatility

rolling_sd_tbl <- portfolio_returns_tbl %>%
    
    tq_mutate(select = portfolio.returns,
              mutate_fun = rollapply,
              width = 4,
              FUN = sd,
              col_rename = "rolling_sd") %>%
    
    na.omit() %>%
    select(date, rolling_sd)

rolling_sd_tbl
## # A tibble: 15 × 2
##    date       rolling_sd
##    <date>          <dbl>
##  1 2021-12-31     0.103 
##  2 2022-03-31     0.152 
##  3 2022-06-30     0.164 
##  4 2022-09-30     0.164 
##  5 2022-12-30     0.0832
##  6 2023-03-31     0.128 
##  7 2023-06-30     0.107 
##  8 2023-09-29     0.0748
##  9 2023-12-29     0.0760
## 10 2024-03-28     0.0712
## 11 2024-06-28     0.0680
## 12 2024-09-30     0.0724
## 13 2024-12-31     0.0610
## 14 2025-03-31     0.0690
## 15 2025-06-18     0.0381

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.

In terms of positioning the Portfolio I’ve put together is not very substantial by itself sitting far behind the stocks itself that said unlike DPZ the risk is rather middle of the road making it a more gradual expansion of wealth.

I honestly think that I would invest my money into individual stocks, most likely MSFT and AAPL, largely because MSFT is like the portfolio but more valued and AAPL is more stable than DPZ alone.