# 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("XOM", "SHEL", "BP", "CVX")

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 <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "BP"   "CVX"  "SHEL" "XOM"
weights <- c(0.30,0.10,0.20,0.40)
weights
## [1] 0.3 0.1 0.2 0.4
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 4 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 BP          0.3
## 2 CVX         0.1
## 3 SHEL        0.2
## 4 XOM         0.4

4 Build a 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           0.0463 
##  2 2013-02-28          -0.0340 
##  3 2013-03-28           0.0166 
##  4 2013-04-30           0.0148 
##  5 2013-05-31           0.00751
##  6 2013-06-28          -0.0204 
##  7 2013-07-31           0.0326 
##  8 2013-08-30          -0.0356 
##  9 2013-09-30           0.00431
## 10 2013-10-31           0.0484 
## # 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 0.0444 0.0444
portfolio_mean_tidyquant_builtin_percent <- mean(portfolio_returns_tbl$portfolio.returns)
portfolio_mean_tidyquant_builtin_percent
## [1] 0.003892862

6 Plot: Expected Returns versus Risk

sd_mean_tbl <- asset_returns_tbl %>% 
    group_by(asset) %>%
    tq_performance(Ra = returns, performance_fun = table.Stats) %>%
    select(Stdev, Mean = ArithmeticMean) %>%
    ungroup() %>%
    

    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      Stdev    Mean
##   <chr>      <dbl>   <dbl>
## 1 BP        0.0601 0.0052 
## 2 CVX       0.0524 0.0057 
## 3 SHEL      0.0551 0.0046 
## 4 XOM       0.0405 0.0021 
## 5 Portfolio 0.0444 0.00389
sd_mean_tbl %>%
    ggplot(aes(x = Stdev, y = Mean, color = asset)) + 
    geom_point() + 
    ggrepel::geom_text_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.

While the majority of the portfolio seems to be >0.05 Stdev, the mean Stdev seems to be lowered as the result of just one asset XOM. Since this stock specifically is skewing the average of the portfolio to be less volatile it may be worth considering purchasing this stock itself if a lack of volatility is what you’re looking for.