# 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

# Stocks
symbols <- c("TSLA", "NVDA", "AAPL", "MSFT", "AMZN")

# Prices
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"))

asset_returns_tbl
## # A tibble: 300 × 3
##    asset date         returns
##    <chr> <date>         <dbl>
##  1 AAPL  2013-01-31 -0.156   
##  2 AAPL  2013-02-28 -0.0256  
##  3 AAPL  2013-03-28  0.00285 
##  4 AAPL  2013-04-30  0.000271
##  5 AAPL  2013-05-31  0.0222  
##  6 AAPL  2013-06-28 -0.126   
##  7 AAPL  2013-07-31  0.132   
##  8 AAPL  2013-08-30  0.0804  
##  9 AAPL  2013-09-30 -0.0217  
## 10 AAPL  2013-10-31  0.0920  
## # ℹ 290 more rows

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

symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols 
## [1] "AAPL" "AMZN" "MSFT" "NVDA" "TSLA"
weights <- c(0.25, 0.25, 0.2, 0.2, 0.1)
weights
## [1] 0.25 0.25 0.20 0.20 0.10
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AAPL       0.25
## 2 AMZN       0.25
## 3 MSFT       0.2 
## 4 NVDA       0.2 
## 5 TSLA       0.1

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          -0.00905
##  2 2013-02-28          -0.00315
##  3 2013-03-28           0.0196 
##  4 2013-04-30           0.0666 
##  5 2013-05-31           0.103  
##  6 2013-06-28          -0.0225 
##  7 2013-07-31           0.0651 
##  8 2013-08-30           0.0419 
##  9 2013-09-30           0.0447 
## 10 2013-10-31           0.0497 
## # 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 0.0493 0.0493
# Mean of Portfolio 
portfolio_mean_tidyquant_builtin_percent <- mean(portfolio_returns_tbl$portfolio.returns)

portfolio_mean_tidyquant_builtin_percent
## [1] 0.02761418
# 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 AMZN      0.0257 0.0739
## 3 MSFT      0.0216 0.0589
## 4 NVDA      0.0471 0.0881
## 5 TSLA      0.037  0.145 
## 6 Portfolio 0.0276 0.0493
sd_mean_tbl %>%
    
    ggplot(aes(x = Stdev, y = Mean, color = asset)) +
    geom_point() +
    ggrepel::geom_text_repel(aes(label = asset))

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     0.0441
##  2 2015-01-30     0.0438
##  3 2015-02-27     0.0452
##  4 2015-03-31     0.0477
##  5 2015-04-30     0.0493
##  6 2015-05-29     0.0466
##  7 2015-06-30     0.0470
##  8 2015-07-31     0.0466
##  9 2015-08-31     0.0473
## 10 2015-09-30     0.0470
## # 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-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.

The portfolio as a whole will perform lower than top performing stock within the portfolio. Although the portfolio will maintian less risk and smoother overall performance compared the stocks as indivuals.