# Load packages

# Core
library(tidyverse)
library(tidyquant)
library(plotly)

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("FNV", "WPM", "NEM", "CCJ", "BWXT", "NVDA", "TSM", "ASML", "CAT", "ITW", "HON", "PLD", "EQIX", "PSA", "TPL", "XOM", "CVX")

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"))

asset_returns_tbl
## # A tibble: 1,020 × 3
##    asset date       returns
##    <chr> <date>       <dbl>
##  1 ASML  2013-01-31  0.154 
##  2 ASML  2013-02-28 -0.0563
##  3 ASML  2013-03-28 -0.0430
##  4 ASML  2013-04-30  0.0992
##  5 ASML  2013-05-31  0.0887
##  6 ASML  2013-06-28 -0.0269
##  7 ASML  2013-07-31  0.128 
##  8 ASML  2013-08-30 -0.0327
##  9 ASML  2013-09-30  0.126 
## 10 ASML  2013-10-31 -0.0420
## # ℹ 1,010 more rows

3 Assign a Weight to Each Asset (Change the Weighting Scheme)

symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
##  [1] "ASML" "BWXT" "CAT"  "CCJ"  "CVX"  "EQIX" "FNV"  "HON"  "ITW"  "NEM" 
## [11] "NVDA" "PLD"  "PSA"  "TPL"  "TSM"  "WPM"  "XOM"
weights <- c(0.10, 0.10, 0.10, 0.05, 0.05, 0.04, 0.03, 0.03, 0.04, 0.03, 0.03, 0.07, 0.07, 0.06, 0.07, 0.07, 0.06)
weights
##  [1] 0.10 0.10 0.10 0.05 0.05 0.04 0.03 0.03 0.04 0.03 0.03 0.07 0.07 0.06 0.07
## [16] 0.07 0.06
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 17 × 2
##    symbols weights
##    <chr>     <dbl>
##  1 ASML       0.1 
##  2 BWXT       0.1 
##  3 CAT        0.1 
##  4 CCJ        0.05
##  5 CVX        0.05
##  6 EQIX       0.04
##  7 FNV        0.03
##  8 HON        0.03
##  9 ITW        0.04
## 10 NEM        0.03
## 11 NVDA       0.03
## 12 PLD        0.07
## 13 PSA        0.07
## 14 TPL        0.06
## 15 TSM        0.07
## 16 WPM        0.07
## 17 XOM        0.06

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           0.0457 
##  2 2013-02-28          -0.0170 
##  3 2013-03-28           0.0106 
##  4 2013-04-30          -0.00468
##  5 2013-05-31           0.0276 
##  6 2013-06-28          -0.0378 
##  7 2013-07-31           0.0389 
##  8 2013-08-30          -0.00842
##  9 2013-09-30           0.0294 
## 10 2013-10-31           0.00176
## # ℹ 50 more rows

5 Compute Standard Deviation

portfoltio_sd_tidyquant_builtin_percent <- portfolio_returns_tbl %>%
    
    tq_performance(Ra = portfolio.returns, 
                   performance_fun = table.Stats) %>%
    
    select(Stdev) %>%
    mutate(tq_sd = round (Stdev, 4))

portfoltio_sd_tidyquant_builtin_percent
## # A tibble: 1 × 2
##    Stdev  tq_sd
##    <dbl>  <dbl>
## 1 0.0326 0.0326
# Mean of Portfolio Returns
portfoltio_mean_tidyquant_builtin_percent <- mean(portfolio_returns_tbl$portfolio.returns)

portfoltio_mean_tidyquant_builtin_percent
## [1] 0.01253176

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_row(tibble(asset = "Portfolio",
                    Mean = portfoltio_mean_tidyquant_builtin_percent,
                   Stdev = portfoltio_sd_tidyquant_builtin_percent$tq_sd))

sd_mean_tbl
## # A tibble: 18 × 3
##    asset        Mean  Stdev
##    <chr>       <dbl>  <dbl>
##  1 ASML       0.0174 0.0625
##  2 BWXT       0.0204 0.0458
##  3 CAT        0.012  0.0656
##  4 CCJ       -0.0106 0.0949
##  5 CVX        0.0057 0.0524
##  6 EQIX       0.0155 0.0505
##  7 FNV        0.0069 0.0951
##  8 HON        0.0165 0.034 
##  9 ITW        0.0186 0.043 
## 10 NEM       -0.0025 0.120 
## 11 NVDA       0.0471 0.0881
## 12 PLD        0.0122 0.0518
## 13 PSA        0.0089 0.0501
## 14 TPL        0.0356 0.105 
## 15 TSM        0.0165 0.0523
## 16 WPM       -0.007  0.125 
## 17 XOM        0.0021 0.0405
## 18 Portfolio  0.0125 0.0326
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.

My current portfolio would have an expected return (mean) of 1.25% and expected risk (stdev) of 3.26%. Through diversification, my portfolio holds the least risk among all of the individual stocks. That said, there is an argument for increasing exposure in HON, ITW, EQIX, TSM, ASML, or BWXT due to their similar (if greater) risk because of their greater expected returns. I still value the importance and safety of diversification so I wouldn’t invest everything into one stock, but with this chart, I can see the benefit of removing some stocks from the portfolio (or altering my weights to each stock) to enhance not only my expected returns, but my risk as well. As shown in the plot above, not only do FNV, CCJ, NEM, and WPM hold greater risk, they have lower than my portfolio returns as well. That is my review over the portfolio.