# Load packages

# Core
library(tidyverse)
library(tidyquant)
library(ggrepel)

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

#choose stocks 
symbols <- c("NFLX", "TSLA", "COST", "GOOG")

prices <- tq_get(x     = symbols, 
                 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] "COST" "GOOG" "NFLX" "TSLA"
# weights 
weights <- c(0.25, 0.25, 0.25, 0.25)
weights
## [1] 0.25 0.25 0.25 0.25
w_tbl <- tibble(symbols, weights)
w_tbl 
## # A tibble: 4 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 COST       0.25
## 2 GOOG       0.25
## 3 NFLX       0.25
## 4 TSLA       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 = "months") 

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2013-01-31           0.196  
##  2 2013-02-28           0.0265 
##  3 2013-03-28           0.0321 
##  4 2013-04-30           0.136  
##  5 2013-05-31           0.177  
##  6 2013-06-28           0.0108 
##  7 2013-07-31           0.110  
##  8 2013-08-30           0.0716 
##  9 2013-09-30           0.0707 
## 10 2013-10-31           0.00978
## # 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 0.0628 0.0628
# Mean of portfolio returns 
portfolio_mean_tidyquant_builtin_percent <- mean(portfolio_returns_tbl$portfolio.returns) 
     
portfolio_mean_tidyquant_builtin_percent
## [1] 0.02809847

6 Plot: Expected Returns versus Risk

Expected Return 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 
    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 COST      0.0127 0.0478
## 2 GOOG      0.0181 0.0535
## 3 NFLX      0.0446 0.133 
## 4 TSLA      0.037  0.145 
## 5 Portfolio 0.0281 0.0628
sd_mean_tbl %>% 
    
    ggplot(aes(x = Stdev, y = Mean, color = asset)) + 
    geom_point() +
    ggrepel::geom_label_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     0.0753
##  2 2015-01-30     0.0678
##  3 2015-02-27     0.0678
##  4 2015-03-31     0.0700
##  5 2015-04-30     0.0680
##  6 2015-05-29     0.0607
##  7 2015-06-30     0.0607
##  8 2015-07-31     0.0608
##  9 2015-08-31     0.0609
## 10 2015-09-30     0.0606
## # ℹ 27 more rows
rolling_sd_tbl %>% 
    
    ggplot(aes(x = date, y = rolling_sd)) + 
    geom_line(color = "cornflowerblue") + 
    
    # Formatting 
    scale_y_continuous(labels = scales::percent_format()) + 
    
    # Labeling 
    labs(x = NULL, 
         y = NULL, 
         title = "24 Months Rolling Volatility") + 
    theme(plot.title = element_text(hjust = .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.

I would excpect the portfolio to be towards a less risker side, as compared to NFLX and TSLA which are the assets with the highest risks and returns in my portfolio. However, the portfolio carries more risk and returns, than my other assets COST and GOOG. I would invest my money in the portfolio, instead of just a singular asset. However I would be changing the weights of my assets, in order to invest in the higher risk and returns stocks.