# 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", "MSFT", "HD", "WMT")

prices <- tq_get(x    = symbols, 
                 get  = "stock.prices", 
                 from = "2012-12-31",
                 to   = "2017-12-31")
prices
## # A tibble: 5,040 × 8
##    symbol date        open  high   low close   volume adjusted
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl>
##  1 AMZN   2012-12-31  12.2  12.6  12.1  12.5 68380000     12.5
##  2 AMZN   2013-01-02  12.8  12.9  12.7  12.9 65420000     12.9
##  3 AMZN   2013-01-03  12.9  13.0  12.8  12.9 55018000     12.9
##  4 AMZN   2013-01-04  12.9  13.0  12.8  13.0 37484000     13.0
##  5 AMZN   2013-01-07  13.1  13.5  13.1  13.4 98200000     13.4
##  6 AMZN   2013-01-08  13.4  13.4  13.2  13.3 60214000     13.3
##  7 AMZN   2013-01-09  13.4  13.5  13.3  13.3 45312000     13.3
##  8 AMZN   2013-01-10  13.4  13.4  13.1  13.3 57268000     13.3
##  9 AMZN   2013-01-11  13.3  13.4  13.2  13.4 48266000     13.4
## 10 AMZN   2013-01-14  13.4  13.7  13.4  13.6 85500000     13.6
## # … with 5,030 more rows

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"))
asset_returns_tbl
## # A tibble: 80 × 3
##    asset date        returns
##    <chr> <date>        <dbl>
##  1 AMZN  2013-03-28  0.0604 
##  2 AMZN  2013-06-28  0.0412 
##  3 AMZN  2013-09-30  0.119  
##  4 AMZN  2013-12-31  0.243  
##  5 AMZN  2014-03-31 -0.170  
##  6 AMZN  2014-06-30 -0.0351 
##  7 AMZN  2014-09-30 -0.00723
##  8 AMZN  2014-12-31 -0.0382 
##  9 AMZN  2015-03-31  0.181  
## 10 AMZN  2015-06-30  0.154  
## # … with 70 more rows

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

# symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull() 
symbols
## [1] "AMZN" "HD"   "MSFT" "WMT"
# weights
weights <- c(0.30, 0.30, 0.15, 0.25)
weights
## [1] 0.30 0.30 0.15 0.25
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 4 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AMZN       0.3 
## 2 HD         0.3 
## 3 MSFT       0.15
## 4 WMT        0.25

4 Build a 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: 20 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2013-03-28           0.0922 
##  2 2013-06-28           0.0749 
##  3 2013-09-30           0.0260 
##  4 2013-12-31           0.135  
##  5 2014-03-31          -0.0521 
##  6 2014-06-30          -0.00123
##  7 2014-09-30           0.0599 
##  8 2014-12-31           0.0620 
##  9 2015-03-31           0.0515 
## 10 2015-06-30           0.0191 
## 11 2015-09-30           0.0431 
## 12 2015-12-31           0.148  
## 13 2016-03-31          -0.00465
## 14 2016-06-30           0.0518 
## 15 2016-09-30           0.0683 
## 16 2016-12-30          -0.0156 
## 17 2017-03-31           0.101  
## 18 2017-06-30           0.0628 
## 19 2017-09-29           0.0409 
## 20 2017-12-29           0.186

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)*100)

portfolio_sd_tidyquant_builtin_percent
## # A tibble: 1 × 2
##    Stdev tq_sd
##    <dbl> <dbl>
## 1 0.0571  5.71
# Mean of portfolio returns
portfolio_mean_tidyquant_builtin_percent <- mean(portfolio_returns_tbl$portfolio.returns)

portfolio_mean_tidyquant_builtin_percent
## [1] 0.05745327

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: 5 × 3
##   asset      Mean Stdev
##   <chr>     <dbl> <dbl>
## 1 AMZN       7.7  12.9 
## 2 HD         6.13  6.15
## 3 MSFT       6.48  8.55
## 4 WMT        2.5   8.37
## 5 Portfolio  5.75  5.71
sd_mean_tbl %>%
    
    ggplot(aes(x = Stdev, y = Mean)) +
    geom_point() + 
    ggrepel::geom_text_repel(aes(label = asset))

sd_mean_tbl
## # A tibble: 5 × 3
##   asset      Mean Stdev
##   <chr>     <dbl> <dbl>
## 1 AMZN       7.7  12.9 
## 2 HD         6.13  6.15
## 3 MSFT       6.48  8.55
## 4 WMT        2.5   8.37
## 5 Portfolio  5.75  5.71

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.

I can expect my portfolio to perform well. If I were to invest all of my money in one stock, I would choose Walmart because of its low volatility. Volatility is sometimes a good things, but I would prefer a stock that is less volatile because that means I have a lesser risk for losing money.