# 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("NVDA", "AAPL", "NFLX", "MSFT", "TSLA")
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
symbol <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "NVDA" "AAPL" "NFLX" "MSFT" "TSLA"
# Weights
weights <- c(0.3, 0.3, 0.25, 0.25, 0.2)
weights
## [1] 0.30 0.30 0.25 0.25 0.20
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 NVDA       0.3 
## 2 AAPL       0.3 
## 3 NFLX       0.25
## 4 MSFT       0.25
## 5 TSLA       0.2

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           0.125  
##  2 2013-02-28           0.0266 
##  3 2013-03-28           0.0305 
##  4 2013-04-30           0.162  
##  5 2013-05-31           0.168  
##  6 2013-06-28          -0.0480 
##  7 2013-07-31           0.109  
##  8 2013-08-30           0.129  
##  9 2013-09-30           0.0569 
## 10 2013-10-31           0.00844
## # … with 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 0.0748 0.0748
# Mean of Portfolio Returns 
portfolio_mean_tidyquant_builtin_percent <- mean(portfolio_returns_tbl$portfolio.returns)

portfolio_mean_tidyquant_builtin_percent
## [1] 0.04258824

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: 6 × 3
##   asset       Mean  Stdev
##   <chr>      <dbl>  <dbl>
## 1 AAPL      0.015  0.0695
## 2 MSFT      0.0216 0.0589
## 3 NFLX      0.0446 0.133 
## 4 NVDA      0.0471 0.0881
## 5 TSLA      0.037  0.145 
## 6 Portfolio 0.0426 0.0748
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     0.0736
##  2 2015-01-30     0.0720
##  3 2015-02-27     0.0732
##  4 2015-03-31     0.0784
##  5 2015-04-30     0.0792
##  6 2015-05-29     0.0746
##  7 2015-06-30     0.0737
##  8 2015-07-31     0.0722
##  9 2015-08-31     0.0699
## 10 2015-09-30     0.0700
## # … with 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-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.

Firstly looking at my scatter plot you can see that my chosen stocks all have high returns for the most part, only exemption is Apple. Netflix and Tesla are in the quadrant best described as “high risk, high reward” which can be a dangerous game to play. Overall my portfolio has a Standard Deviation a little above 0.75 and a mean average if 0.04 which is pretty good for both. I would personally invest all of my assets into the portfolio which seems to have good returns and is a safer option than betting on the risky game to go my way. All around my portfolio has a good mix of high return, but also high risk at certain points.