# 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("NFLX", "AAPL", "JPM", "NVDA", "WMT")

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 <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "AAPL" "JPM"  "NFLX" "NVDA" "WMT"
weights <- c(0.3, 0.2, 0.2, 0.15, 0.15)
weights
## [1] 0.30 0.20 0.20 0.15 0.15
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AAPL       0.3 
## 2 JPM        0.2 
## 3 NFLX       0.2 
## 4 NVDA       0.15
## 5 WMT        0.15

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           0.0878 
##  2 2013-02-28           0.0335 
##  3 2013-03-28           0.00738
##  4 2013-04-30           0.0505 
##  5 2013-05-31           0.0409 
##  6 2013-06-28          -0.0636 
##  7 2013-07-31           0.0923 
##  8 2013-08-30           0.0294 
##  9 2013-09-30           0.0251 
## 10 2013-10-31           0.0388 
## # 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)*100)

portfolio_sd_tidyquant_builtin_percent
## # A tibble: 1 × 2
##   Stdev tq_sd
##   <dbl> <dbl>
## 1 0.045   4.5
portfolio_mean_tidyquant_builtin_percent <- mean(portfolio_returns_tbl$portfolio.returns)

portfolio_mean_tidyquant_builtin_percent
## [1] 0.02514571

6 Plot: Expected Returns versus Risk

sd_mean_tbl <- asset_returns_tbl %>%
  
  group_by(asset) %>%
  tq_performance(Ra = returns,
                 performance_fun = table.Stats) %>%
  select(Mean = ArithmeticMean, Stdev) %>%
  ungroup() %>%
  mutate(Stdev = Stdev *100,
         Mean = Mean * 100) %>%
  
  add_row(tibble(asset = "Portfolio",
                 Mean = portfolio_mean_tidyquant_builtin_percent * 100,
                 Stdev = portfolio_sd_tidyquant_builtin_percent$tq_sd))

sd_mean_tbl
## # A tibble: 6 × 3
##   asset      Mean Stdev
##   <chr>     <dbl> <dbl>
## 1 AAPL       1.5   6.95
## 2 JPM        1.7   5.56
## 3 NFLX       4.46 13.3 
## 4 NVDA       4.71  8.81
## 5 WMT        0.83  4.71
## 6 Portfolio  2.51  4.5
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.

The portfolio should have a lower risk than most of the stocks because it spreads the money across several companies. NVDA and NFLX have higher expected returns, but they also have much higher risk. WMT has the lowest risk, but it also has the lowest expected return. I would not invest all my money in one individual stock because the portfolio provides reduced risk.