# 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 ("AAPL", "NVDA", "CAT", "PANW", "MU")

prices <- tq_get(x = symbols,
                 get = "stockprices",
                 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 = "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_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "AAPL" "CAT"  "MU"   "NVDA" "PANW"
# weights
weights <- c(0.2, 0.2, 0.2, 0.2, 0.2)
weights 
## [1] 0.2 0.2 0.2 0.2 0.2
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AAPL        0.2
## 2 CAT         0.2
## 3 MU          0.2
## 4 NVDA        0.2
## 5 PANW        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 = "quarters")

portfolio_returns_tbl
## # A tibble: 20 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2013-03-28           0.0706 
##  2 2013-06-28           0.00250
##  3 2013-09-30           0.120  
##  4 2013-12-31           0.148  
##  5 2014-03-31           0.0871 
##  6 2014-06-30           0.173  
##  7 2014-09-30           0.0389 
##  8 2014-12-31           0.0712 
##  9 2015-03-31          -0.00701
## 10 2015-06-30          -0.0285 
## 11 2015-09-30          -0.0825 
## 12 2015-12-31           0.0539 
## 13 2016-03-31          -0.0251 
## 14 2016-06-30           0.0286 
## 15 2016-09-30           0.247  
## 16 2016-12-30           0.0987 
## 17 2017-03-31           0.0844 
## 18 2017-06-30           0.130  
## 19 2017-09-29           0.158  
## 20 2017-12-29           0.0934

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 0.0792 0.0792
# Mean of portfolio returns
portfolio_mean_tidyquant_builtin_percent <- mean(portfolio_returns_tbl$portfolio.returns)

portfolio_mean_tidyquant_builtin_percent
## [1] 0.07312763

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      0.045  0.119 
## 2 CAT       0.0359 0.116 
## 3 MU        0.0935 0.235 
## 4 NVDA      0.141  0.134 
## 5 PANW      0.0498 0.173 
## 6 Portfolio 0.0731 0.0792
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.

I would expect my portfolio to perform well compared to most of the individual stocks because it provides a balance between expected return and risk. The portfolio has an average quarterly return of 7.31% and a standard deviation of 7.92%. NVIDIA (NVDA) has the highest expected return at 14.1%, but it also has a standard deviation of 13.4%, meaning its returns are more volatile than the overall portfolio. Micron (MU) has an expected return of 9.35%, but it has the highest risk, with a standard deviation of 23.5%. By investing equally across all five stocks, my portfolio spreads out the risk instead of relying on one company’s performance.