# 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("MSFT", "NFLX", "META", "AMZN", "AAPL")
prices <- tq_get(x    = symbols,
                 get  = "stock.prices", 
                 from = "2012-12-31",
                 to   = "2017-12-31")

2 Convert prices to returns (monthly)

asset_returns_table <- 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_table %>% distinct(asset) %>% pull()
symbols
## [1] "AAPL" "AMZN" "META" "MSFT" "NFLX"
# weights
weights <- c(0.15, 0.25, 0.2, 0.3, 0.1)
weights
## [1] 0.15 0.25 0.20 0.30 0.10
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AAPL       0.15
## 2 AMZN       0.25
## 3 META       0.2 
## 4 MSFT       0.3 
## 5 NFLX       0.1

4 Build a portfolio

portfolio_returns_tbl <- asset_returns_table %>%
    
    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          0.0873  
##  2 2013-02-28         -0.0114  
##  3 2013-03-28         -0.000878
##  4 2013-04-30          0.0613  
##  5 2013-05-31          0.0143  
##  6 2013-06-28         -0.0169  
##  7 2013-07-31          0.109   
##  8 2013-08-30          0.0491  
##  9 2013-09-30          0.0701  
## 10 2013-10-31          0.0746  
## # 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 0.0472 0.0472
# Mean of portfolio returns
portfolio_mean_tidyquant_builtin_percent <- 
    mean(portfolio_returns_tbl$portfolio.returns)

portfolio_mean_tidyquant_builtin_percent
## [1] 0.02591284

6 Plot: Expected Returns versus Risk

sd_mean_tbl <- asset_returns_table %>%
    
    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      0.015  0.0695
## 2 AMZN      0.0257 0.0739
## 3 META      0.0315 0.0838
## 4 MSFT      0.0216 0.0589
## 5 NFLX      0.0446 0.133 
## 6 Portfolio 0.0259 0.0472
sd_mean_tbl %>%
    
    ggplot(aes(x = Stdev, y = Mean, color = asset)) +
    geom_point() +
    geom_text(aes(label = asset),
              vjust = 1.5,   # Nudges labels down
              hjust = 0.5,   # Centers labels horizontally
              size = 4)      # Sets text size

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.

Answer: My portfolio’s average monthly return is sitting at around 2.6%, only losing to META and NFLX with returns of 3.2% and 4.45% respectively. AMZN’s monthly return was similar to the portfolio’s at roughly 2.6% as well. This is most likely due to the portfolio’s weight being comprised of 30% AMZN stock. MSFT had about a 2.2% return and AAPL came in last with an estimated 1.5% monthly return. Given the high volatility of NFLX (Stdev: 0.134), I would consider investing a heavier portion of my funds into it if I were able to catch the stock in a downturn, but META overall has the best balance of monthly return (3.2%) + standard deviation (0.084) and would probably be the safest bet if I wanted to invest 100% of my money in that one stock.