# 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("SPY", "EFA", "IJS", "EEM", "AAPL")

prices <- tq_get(x = symbols,
                 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] "AAPL" "EEM"  "EFA"  "IJS"  "SPY"
#weights
weights <- c(0.25, 0.25, 0.2, 0.2, 0.1)
weights
## [1] 0.25 0.25 0.20 0.20 0.10
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AAPL       0.25
## 2 EEM        0.25
## 3 EFA        0.2 
## 4 IJS        0.2 
## 5 SPY        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,
               rebalence_on = "months")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2013-01-31          -0.0169 
##  2 2013-02-28          -0.00928
##  3 2013-03-28           0.0137 
##  4 2013-04-30           0.0157 
##  5 2013-05-31          -0.00218
##  6 2013-06-28          -0.0468 
##  7 2013-07-31           0.0611 
##  8 2013-08-30          -0.00531
##  9 2013-09-30           0.0446 
## 10 2013-10-31           0.0486 
## # 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))

portfolio_sd_tidyquant_builtin_percent
## # A tibble: 1 × 2
##    Stdev  tq_sd
##    <dbl>  <dbl>
## 1 0.0348 0.0348
# Mean of portfolio returns
portfolio_mean_tidyquant_builitin_percent <- mean(portfolio_returns_tbl$portfolio.returns)

portfolio_mean_tidyquant_builitin_percent
## [1] 0.009252301

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() %>%
  
  # Add portfolio sd
  add_row(tibble(asset = "Portfolio",
                 Mean  = portfolio_mean_tidyquant_builitin_percent,
                 Stdev = portfolio_sd_tidyquant_builtin_percent$tq_sd))
sd_mean_tbl
## # A tibble: 6 × 3
##   asset        Mean  Stdev
##   <chr>       <dbl>  <dbl>
## 1 AAPL      0.015   0.0695
## 2 EEM       0.0028  0.0419
## 3 EFA       0.006   0.0326
## 4 IJS       0.0119  0.0396
## 5 SPY       0.0121  0.0272
## 6 Portfolio 0.00925 0.0348
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.

It would appear that the portfolio is performing relatively well compared to the assets that are in said portfolio. In terms of relative return and risk I would say the portfolio is doing well, the SPY stock however, is also performing over the portfolio with a little more risk and I would heavily consider investing into that over the portfolio