# 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("AMZN", "HD", "TSLA", "NFLX", "GOOG")
prices <- tq_get(x = symbols,
                 get = "stock.prices",
                 from = "2012-12-31",
                 to = "2017-12-31")

2 Convert prices to returns (monthly)

asset_return_tbl <- prices %>%
    
    group_by(symbol) %>%
    
    tq_transmute(select     = adjusted, 
                 mutate_fun = periodReturn, 
                 period     = "quarterly",
                 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_return_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "AMZN" "GOOG" "HD"   "NFLX" "TSLA"
#weights
weights <- c(0.25, 0.20, 0.20, 0.20, 0.15)
weights
## [1] 0.25 0.20 0.20 0.20 0.15
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AMZN       0.25
## 2 GOOG       0.2 
## 3 HD         0.2 
## 4 NFLX       0.2 
## 5 TSLA       0.15

4 Build a portfolio

portfolio_returns_tbl <- asset_return_tbl %>%
    
    tq_portfolio(assets_col  = asset, 
                 returns_col = returns,
                 weights     = w_tbl,
                 rebalance_on = "months")

portfolio_returns_tbl
## # A tibble: 20 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2013-03-28           0.223  
##  2 2013-06-28           0.231  
##  3 2013-09-30           0.190  
##  4 2013-12-31           0.125  
##  5 2014-03-31          -0.0104 
##  6 2014-06-30           0.0695 
##  7 2014-09-30           0.0313 
##  8 2014-12-31          -0.0689 
##  9 2015-03-31           0.0854 
## 10 2015-06-30           0.169  
## 11 2015-09-30           0.0887 
## 12 2015-12-31           0.157  
## 13 2016-03-31          -0.0623 
## 14 2016-06-30          -0.00986
## 15 2016-09-30           0.0740 
## 16 2016-12-30           0.0329 
## 17 2017-03-31           0.151  
## 18 2017-06-30           0.0915 
## 19 2017-09-29           0.0531 
## 20 2017-12-29           0.0946

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 0.0859 0.0859
#Mean of portfolio returns
portfolio_mean_tidyquant_builtin_percent <- mean(portfolio_returns_tbl$portfolio.returns)

portfolio_mean_tidyquant_builtin_percent
## [1] 0.08577

6 Plot: Expected Returns versus Risk

# Expected Returns vs Risk
sd_mean_tbl <- asset_return_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 AMZN      0.077  0.129 
## 2 GOOG      0.0544 0.0905
## 3 HD        0.0613 0.0615
## 4 NFLX      0.134  0.217 
## 5 TSLA      0.111  0.300 
## 6 Portfolio 0.0858 0.0859
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.

Looking at the plot, it appears to be best to invest in either Netflix or Tesla. Netflix has a higher mean return meaning on average your return will be higher. On the other hand Telsa has a slightly lower mean but a higher standard deviation. This means Tesla stock is more volatile and can increase and decrease by a larger margin, but the mean is still very high at just over 11%. None of the stocks that I chose to look at had a negative average return. However, the average of my portfolio had a higher mean than Home Depot, Google, and Amazon. This does not mean these three stocks performed poorly, it just means I would be better off investing in the entire portfolio rather than just those three stocks.