# 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", "MSFT", "ABT", "NVDA", "JPM")

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] "ABT"  "JPM"  "MSFT" "NVDA" "XOM"
# weights
weights <- c(0.35, 0.15, 0.25, 0.15, 0.1)
weights
## [1] 0.35 0.15 0.25 0.15 0.10
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 ABT        0.35
## 2 JPM        0.15
## 3 MSFT       0.25
## 4 NVDA       0.15
## 5 XOM        0.1

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           0.0496 
##  2 2013-02-28           0.0160 
##  3 2013-03-28           0.0208 
##  4 2013-04-30           0.0684 
##  5 2013-05-31           0.0393 
##  6 2013-06-28          -0.0298 
##  7 2013-07-31           0.0153 
##  8 2013-08-30          -0.0367 
##  9 2013-09-30           0.00776
## 10 2013-10-31           0.0519 
## # 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 0.0405 0.0405
# Mean of portfolio returns
portfolio_mean_tidyquant_builtin_percent <- 
mean(portfolio_returns_tbl$portfolio.returns)

portfolio_mean_tidyquant_builtin_percent
## [1] 0.01934292

6 Plot

Expected Returns vs 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: 6 × 3
##   asset       Mean  Stdev
##   <chr>      <dbl>  <dbl>
## 1 ABT       0.0117 0.0558
## 2 JPM       0.017  0.0556
## 3 MSFT      0.0216 0.0589
## 4 NVDA      0.0471 0.0881
## 5 XOM       0.0021 0.0405
## 6 Portfolio 0.0193 0.0405
sd_mean_tbl %>%
    
    ggplot(aes(x = Stdev, y = Mean, color = 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(date, rolling_sd)

rolling_sd_tbl
## # A tibble: 37 × 2
##    date       rolling_sd
##    <date>          <dbl>
##  1 2014-12-31     0.0292
##  2 2015-01-30     0.0332
##  3 2015-02-27     0.0362
##  4 2015-03-31     0.0379
##  5 2015-04-30     0.0378
##  6 2015-05-29     0.0374
##  7 2015-06-30     0.0371
##  8 2015-07-31     0.0372
##  9 2015-08-31     0.0382
## 10 2015-09-30     0.0392
## # ℹ 27 more rows
rolling_sd_tbl %>%
    
    ggplot(aes(x = date, y = rolling_sd)) +
    geom_line(color = "blue") +
    
    # 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.

Based on the chart I expect high volatility with a moderate to high gains on my NVDIA stock, based on my portfolio it is low risk and medium reward. MSFT and JPM are medium reward medium risk. On the other hand XOM is low risk low reward which displaces the risk within my portfolio.