# 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("NKE", "ADDYY", "SKX", "UAA")

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
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "ADDYY" "NKE"   "SKX"   "UAA"
# weights
weights <- c(0.4, 0.2, 0.25, 0.15)
weights
## [1] 0.40 0.20 0.25 0.15
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 4 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 ADDYY      0.4 
## 2 NKE        0.2 
## 3 SKX        0.25
## 4 UAA        0.15

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 = "month")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2013-01-31            0.0385
##  2 2013-02-28            0.0150
##  3 2013-03-28            0.0760
##  4 2013-04-30            0.0284
##  5 2013-05-31            0.0519
##  6 2013-06-28            0.0120
##  7 2013-07-31            0.0604
##  8 2013-08-30            0.0204
##  9 2013-09-30            0.0542
## 10 2013-10-31            0.0157
## # 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 0.0473 0.0473
#Mean of portfolio returns
portfolio_mean_tidyquant_builtin_percent <- mean(portfolio_returns_tbl$portfolio.returns)

portfolio_mean_tidyquant_builtin_percent
## [1] 0.01702087

6 Plot: Expected Returns versus 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: 5 × 3
##   asset       Mean  Stdev
##   <chr>      <dbl>  <dbl>
## 1 ADDYY     0.0147 0.076 
## 2 NKE       0.0158 0.0531
## 3 SKX       0.0302 0.111 
## 4 UAA       0.0029 0.101 
## 5 Portfolio 0.0170 0.0473
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 expected return will not necessarily be the highest among all assets, but it reflects a balance based on weights. The portfolio’s standard deviation (volatility) is lower than the average of individual assets, due to diversification.

You might find that one of the stocks (e.g., ADDYY or SKX) has a slightly higher mean return than the portfolio. The portfolio reduces company-specific risk, which cannot be eliminated if you go all-in on one stock.

In conclusion… Stick with the portfolio. It offers a better risk-return tradeoff.Even if one stock outperformed over this time period, there’s no guarantee it will continue to do so.Diversification cushions your returns and helps avoid devastating losses — a cornerstone of smart investing.