# 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("TGT", "WMT", "AMZN")

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
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "AMZN" "TGT"  "WMT"
# weights
weights <- c(0.3, 0.2, 0.1)
weights
## [1] 0.3 0.2 0.1
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 3 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AMZN        0.3
## 2 TGT         0.2
## 3 WMT         0.1

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 = "months")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2013-01-31           0.0236 
##  2 2013-02-28           0.00920
##  3 2013-03-28           0.0254 
##  4 2013-04-30          -0.00476
##  5 2013-05-31           0.0125 
##  6 2013-06-28           0.00700
##  7 2013-07-31           0.0358 
##  8 2013-08-30          -0.0492 
##  9 2013-09-30           0.0355 
## 10 2013-10-31           0.0519 
## # … with 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 0.0264 0.0264
# Mean of portfolio returns
portfolio_mean_tidyquant_builtin_percent <- mean(portfolio_returns_tbl$portfolio.returns)

portfolio_mean_tidyquant_builtin_percent
## [1] 0.009381122

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: 4 × 3
##   asset        Mean  Stdev
##   <chr>       <dbl>  <dbl>
## 1 AMZN      0.0257  0.0739
## 2 TGT       0.0043  0.0609
## 3 WMT       0.0083  0.0471
## 4 Portfolio 0.00938 0.0264
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.

All three of the companies would be performing better than the portfolio with the lowest standard deviation being a .02 difference with Walmart, and the highest being a .05 difference with Amazon. I would invest my money into Amazon. It has a much larger percentage change with its mean and standard deviation in comparison to the Portfolio so I would have a much higher chance of profiting by investing into Amazon as a company.