# 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("MTN", "AAPL", "NFLX", "DIS", "GE")

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 <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "AAPL" "DIS"  "GE"   "MTN"  "NFLX"
weight  <- c(0.25, 0.25, 0.2, 0.2, 0.1)
weight
## [1] 0.25 0.25 0.20 0.20 0.10
w_tbl <- tibble(symbols, weight)
w_tbl
## # A tibble: 5 × 2
##   symbols weight
##   <chr>    <dbl>
## 1 AAPL      0.25
## 2 DIS       0.25
## 3 GE        0.2 
## 4 MTN       0.2 
## 5 NFLX      0.1

4 Build a 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            0.0461
##  2 2013-02-28            0.0286
##  3 2013-03-28            0.0352
##  4 2013-04-30            0.0247
##  5 2013-05-31            0.0322
##  6 2013-06-28           -0.0451
##  7 2013-07-31            0.0805
##  8 2013-08-30            0.0125
##  9 2013-09-30            0.0296
## 10 2013-10-31            0.0643
## # ℹ 50 more rows

5 Compute Standard Deviation

portfolio_sd_tidyquant_builtin_percent <- portfolio_returns_tbl %>%
    
    tq_performance(Ra = portfolio.returns,
                   Rb = NULL,
                   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 0.0375 0.0375
portfolio_sd_tidyquant_builtin_percent <- mean(portfolio_returns_tbl$portfolio.returns)

portfolio_sd_tidyquant_builtin_percent
## [1] 0.01652315

6 Plot: Expected Returns versus Risk

sd_mean_tbl <- asset_returns_tbl %>%
    
    group_by(asset) %>%
    tq_performance(Ra = returns,
                   Rb = NULL,
                   performance_fun = table.Stats) %>%
    select(Mean = ArithmeticMean, Stdev) %>%
    ungroup() %>% 
 
    
    add_row(tibble(asset = "Portfolio",
                   Mean = portfolio_sd_tidyquant_builtin_percent, 
                   Stdev = portfolio_sd_tidyquant_builtin_percent))
sd_mean_tbl
## # A tibble: 6 × 3
##   asset        Mean  Stdev
##   <chr>       <dbl>  <dbl>
## 1 AAPL       0.015  0.0695
## 2 DIS        0.014  0.0528
## 3 GE        -0.0003 0.0544
## 4 MTN        0.0245 0.0493
## 5 NFLX       0.0446 0.133 
## 6 Portfolio  0.0165 0.0165
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.

The portfolio has less overall risk due to diversification. I would not invest all my money into one of these assets due to the overall risk being higher.