# 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("UAL", "AAL", "LUV", "DAL")

prices <- tq_get(x    = symbols,
                 get  = "stock.prices",
                 from = "2012-12-31",
                 to   = "2017-12-31")

2 Convert prices to returns (monthly)

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] "AAL" "DAL" "LUV" "UAL"
# weights
weights <- c(0.3, 0.2, 0.25, 0.25)
weights
## [1] 0.30 0.20 0.25 0.25
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 4 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AAL        0.3 
## 2 DAL        0.2 
## 3 LUV        0.25
## 4 UAL        0.25

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: 60 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2013-01-31            0.0790
##  2 2013-02-28            0.0229
##  3 2013-03-28            0.180 
##  4 2013-04-30            0.0126
##  5 2013-05-31            0.0312
##  6 2013-06-28           -0.0448
##  7 2013-07-31            0.119 
##  8 2013-08-30           -0.137 
##  9 2013-09-30            0.135 
## 10 2013-10-31            0.134 
## # ℹ 50 more rows

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, 4))

portfolio_sd_tidyquant_builtin_percent
## # A tibble: 1 × 2
##    Stdev  tq_sd
##    <dbl>  <dbl>
## 1 0.0808 0.0808
# Mean of portfolio returns
portfolio_mean_tidyquant_builtin_percent <- mean(portfolio_returns_tbl$portfolio.returns)

portfolio_mean_tidyquant_builtin_percent
## [1] 0.02459114

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() %>%
    
    # 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: 5 × 3
##   asset       Mean  Stdev
##   <chr>      <dbl>  <dbl>
## 1 AAL       0.023  0.104 
## 2 DAL       0.0269 0.08  
## 3 LUV       0.0316 0.0783
## 4 UAL       0.0176 0.0982
## 5 Portfolio 0.0246 0.0808
sd_mean_tbl %>%
    
    ggplot(aes(x = Stdev, y = Mean, color = asset)) +
    geom_point() +
    ggrepel::geom_label_repel(aes(label = asset))

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 plot indicates that LUV is the least risky with the highest expected return, along with DAL following closely behind. UAL has the lowest expected return with a relatively high risk, and AAL is the most volatile and has mid-low expected returns. I would focus on maintaining diversification as any investor should, but putting significantly more money into LUV and DAL while avoiding UAL and AAL for the most part. The portfolio itself performs with less expected return than LUV or DAL, and is slightly more risky. It is close to AAL as far as expected returns go without being nearly as risky.