# 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 2019-12-31 to 2024-12-31

1 Import stock prices

symbols <- c("SPY", "EFA", "IJS", "EEM", "AGG")

# Using tq_get() ----
prices <- tq_get(x = symbols,
                 get = "stock.prices",
                 from = "2019-12-31",
                 to = "2024-12-31")

2 Convert prices to returns (monthly)

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"))

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

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")

portfolio_returns_rebalanced_monthly_tbl
## # A tibble: 60 × 2
##    date         returns
##    <date>         <dbl>
##  1 2020-01-31 -0.0296  
##  2 2020-02-28 -0.0515  
##  3 2020-03-31 -0.148   
##  4 2020-04-30  0.0713  
##  5 2020-05-29  0.0293  
##  6 2020-06-30  0.0333  
##  7 2020-07-31  0.0378  
##  8 2020-08-31  0.0309  
##  9 2020-09-30 -0.0214  
## 10 2020-10-30 -0.000476
## # ℹ 50 more rows

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 0.0436  4.36

6 Plot: Expected Returns versus Risk

portfolio_returns_rebalanced_monthly_tbl %>%
    
    ggplot(aes(x = date, y = returns)) +
    geom_point(color = "cornflower blue") +
    
    # Formatting
    scale_x_date(breaks = scales::breaks_pretty(n = 6)) +
    
    labs(title = "Portfolio Returns Scatter",
         y = "monthly return")

portfolio_returns_rebalanced_monthly_tbl %>%
    
    ggplot(aes(returns)) +
    geom_histogram(fill = "cornflower blue",
                   binwidth = 0.005) +
    
    labs(title = "Portfolio Returns Distribution",
         y = "count",
         x = "returns")

portfolio_returns_rebalanced_monthly_tbl %>%
    
    ggplot(aes(returns)) +
    geom_histogram(fill = "cornflower blue",
                   binwidth = 0.01) +
    geom_density(aes(returns)) +
    
    labs(title = "Portfolio Histogram and Density",
         y = "distribution",
         x = "monthly returns")

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.