# 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 ("DLR", "NVDA", "GOOG", "MSFT", "AMZN")

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] "AMZN" "DLR"  "GOOG" "MSFT" "NVDA"
# weights
weights <- c(0.2, 0.2, 0.1, 0.3, 0.2)
weights 
## [1] 0.2 0.2 0.1 0.3 0.2
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AMZN        0.2
## 2 DLR         0.2
## 3 GOOG        0.1
## 4 MSFT        0.3
## 5 NVDA        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            0.0564
##  2 2013-06-28            0.0803
##  3 2013-09-30            0.0109
##  4 2013-12-31            0.105 
##  5 2014-03-31            0.0370
##  6 2014-06-30            0.0330
##  7 2014-09-30            0.0484
##  8 2014-12-31            0.0177
##  9 2015-03-31            0.0135
## 10 2015-06-30            0.0503
## 11 2015-09-30            0.0915
## 12 2015-12-31            0.238 
## 13 2016-03-31            0.0228
## 14 2016-06-30            0.109 
## 15 2016-09-30            0.135 
## 16 2016-12-30            0.0951
## 17 2017-03-31            0.0820
## 18 2017-06-30            0.112 
## 19 2017-09-29            0.0824
## 20 2017-12-29            0.101

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 0.0532 0.0532
# Mean of portfolio returns
portfolio_mean_tidyquant_builtin_percent <- mean(portfolio_returns_tbl$portfolio.returns)

portfolio_mean_tidyquant_builtin_percent
## [1] 0.0760774

6 Plot: Expected Returns versus Risk

Expected Return vs 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 AMZN      0.077  0.129 
## 2 DLR       0.0375 0.0913
## 3 GOOG      0.0544 0.0905
## 4 MSFT      0.0648 0.0855
## 5 NVDA      0.141  0.134 
## 6 Portfolio 0.0761 0.0532
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. Looking at the chart, I expect my portfolio to perform very well compared to the individual assets. My portfolio has an expected return of over 7% with a relatively small standard deviation of 0.04. All individual stocks have a much higher standard deviation, while some of them (MSFT, GOOG, DLR, and AMZN) offer an even lower or the same expected return. Because of this, I would prefer to invest in the portfolio rather than in any single stock, since it offers a relatively good return with lower risk. This shows why diversification is important in investing, because spreading your money across different assets can reduce risk while still providing a good return.