# Load packages

# Core
library(tidyverse)
library(tidyquant)

Goal

Collect individual returns into a portfolio by assigning a weight to each stock

Choose your stocks.

from 2021-01-21 to 2025-01-21

1 Import stock prices

symbols <- c("UNH", "LLY", "JNJ", "PFE", "MRK")

prices <- tq_get(x = symbols, 
                 get = "stock.prices", 
                 from = "2010-01-01", 
                 to = "2025-01-01")

2 Convert prices to returns (quarterly)

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 (change the weigting scheme)

# Symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "JNJ" "LLY" "MRK" "PFE" "UNH"
# weights
weights <- c(0.3, 0.25, 0.20, 0.13, 0.12)
weights
## [1] 0.30 0.25 0.20 0.13 0.12
w_tbl <-tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 JNJ        0.3 
## 2 LLY        0.25
## 3 MRK        0.2 
## 4 PFE        0.13
## 5 UNH        0.12

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: 179 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2010-02-26          -0.0103 
##  2 2010-03-31           0.0209 
##  3 2010-04-30          -0.0379 
##  4 2010-05-28          -0.0629 
##  5 2010-06-30           0.00805
##  6 2010-07-30           0.0222 
##  7 2010-08-31           0.00400
##  8 2010-09-30           0.0797 
##  9 2010-10-29           0.00154
## 10 2010-11-30          -0.0318 
## # ℹ 169 more rows

5 Calculate STD. DEV.

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))

portfolio_sd_tidyquant_builtin_percent
## # A tibble: 1 × 2
##    Stdev  tq_sd
##    <dbl>  <dbl>
## 1 0.0379 0.0379
# Mean of portfolio returns

portfolio_mean_tidyquant_builtin_percent <- mean(portfolio_returns_tbl$portfolio.returns)

portfolio_mean_tidyquant_builtin_percent
## [1] 0.01140527

6 Plot

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() %>%
    
    # Add portfolio sd
    add_row(tibble(asset = "Portfolio"),
            Mean = portfolio_mean_tidyquant_builtin_percent,
            Stdev = portfolio_sd_tidyquant_builtin_percent$tq_sd)

sd_mean_tbl
## # A tibble: 6 × 3
##   asset       Mean  Stdev
##   <chr>      <dbl>  <dbl>
## 1 JNJ       0.0071 0.0431
## 2 LLY       0.0196 0.0624
## 3 MRK       0.0084 0.0522
## 4 PFE       0.0055 0.0592
## 5 UNH       0.0165 0.0562
## 6 Portfolio 0.0114 0.0379
sd_mean_tbl %>%
    
    ggplot(aes(x = Stdev, y = Mean, colour = asset)) +
    geom_point() +
    ggrepel::geom_text_repel(aes(label = asset))

24 Months 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(-portfolio.returns)

rolling_sd_tbl
## # A tibble: 156 × 2
##    date       rolling_sd
##    <date>          <dbl>
##  1 2012-01-31     0.0341
##  2 2012-02-29     0.0339
##  3 2012-03-30     0.0341
##  4 2012-04-30     0.0326
##  5 2012-05-31     0.0296
##  6 2012-06-29     0.0319
##  7 2012-07-31     0.0319
##  8 2012-08-31     0.0319
##  9 2012-09-28     0.0293
## 10 2012-10-31     0.0292
## # ℹ 146 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.

The portfolio has an expected return of approximately 0.0115, which is lower than LLY and UNH but higher than JNJ, MRK, and PFE. Among the individual stocks, LLY has the highest expected return at around 0.020, followed by UNH at about 0.017. In terms of risk, the portfolio stands out with the lowest standard deviation—approximately 0.038—indicating that it is the least risky investment shown on the chart.LLY and UNH offer higher expected returns, but with significantly greater risk with standard deviations around 0.060 and 0.057 respectively. It would not be advisable to invest all your money in any single stock instead of the portfolio. The portfolio benefits from diversification, which reduces overall volatility. While LLY and UNH might seem attractive due to their higher expected returns, they carry considerably more risk. The portfolio offers a better balance of return and stability, making it the better choice for most investors seeking consistent performance and downside protection.