# 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", "F", "WMT", "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" "F"    "MU"   "NVDA" "WMT"
# 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 F           0.2
## 3 MU          0.2
## 4 NVDA        0.2
## 5 WMT         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          0.0897  
##  2 2013-06-28          0.105   
##  3 2013-09-30          0.118   
##  4 2013-12-31          0.0813  
##  5 2014-03-31          0.0316  
##  6 2014-06-30          0.133   
##  7 2014-09-30          0.000639
##  8 2014-12-31          0.0764  
##  9 2015-03-31         -0.0142  
## 10 2015-06-30         -0.119   
## 11 2015-09-30         -0.0638  
## 12 2015-12-31          0.0391  
## 13 2016-03-31         -0.0143  
## 14 2016-06-30          0.0878  
## 15 2016-09-30          0.155   
## 16 2016-12-30          0.133   
## 17 2017-03-31          0.108   
## 18 2017-06-30          0.0705  
## 19 2017-09-29          0.136   
## 20 2017-12-29          0.103

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 0.0726 0.0726
# Mean of portfolio returns
portfolio_mean_tidyquant_builtin_percent <- mean(portfolio_returns_tbl$portfolio.returns)

portfolio_mean_tidyquant_builtin_percent
## [1] 0.06282355

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 F         0.0092 0.0769
## 3 MU        0.0935 0.235 
## 4 NVDA      0.141  0.134 
## 5 WMT       0.025  0.0837
## 6 Portfolio 0.0628 0.0726
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 of Apple, NVIDIA, Ford, Walmart, and Micron to provide growth while balancing risk across different industries. NVIDIA and Micron offer strong growth potential but can be volatile, while Walmart may provide more stability. I would not invest all my money in one stock because the portfolio spreads risk across multiple companies and balances potential returns with risk.