# 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("CRM", "CVX", "GOLD", "MSFT", "VZ")

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     = "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] "CRM"  "CVX"  "GOLD" "MSFT" "VZ"
# weights
weights <- c(0.25, 0.25, 0.2, 0.2, 0.1)
weights
## [1] 0.25 0.25 0.20 0.20 0.10
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 CRM        0.25
## 2 CVX        0.25
## 3 GOLD       0.2 
## 4 MSFT       0.2 
## 5 VZ         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 = "quarters")

portfolio_returns_tbl
## # A tibble: 20 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2013-03-28          0.0366  
##  2 2013-06-28         -0.119   
##  3 2013-09-30          0.107   
##  4 2013-12-31          0.0452  
##  5 2014-03-31          0.0187  
##  6 2014-06-30          0.0444  
##  7 2014-09-30         -0.0409  
##  8 2014-12-31         -0.0706  
##  9 2015-03-31          0.000142
## 10 2015-06-30          0.00163 
## 11 2015-09-30         -0.154   
## 12 2015-12-31          0.150   
## 13 2016-03-31          0.143   
## 14 2016-06-30          0.126   
## 15 2016-09-30         -0.0471  
## 16 2016-12-30          0.0257  
## 17 2017-03-31          0.0657  
## 18 2017-06-30         -0.0248  
## 19 2017-09-29          0.0819  
## 20 2017-12-29          0.0565

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 0.0822 0.0822
# Mean of portfolio returns 
portfolio_mean_tidyquant_builtin_percent <-
mean(portfolio_returns_tbl$portfolio.returns)

portfolio_sd_tidyquant_builtin_percent
## # A tibble: 1 × 2
##    Stdev  tq_sd
##    <dbl>  <dbl>
## 1 0.0822 0.0822

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: 6 × 3
##   asset        Mean  Stdev
##   <chr>       <dbl>  <dbl>
## 1 CRM        0.0444 0.101 
## 2 CVX        0.017  0.0916
## 3 GOLD      -0.0411 0.284 
## 4 MSFT       0.0648 0.0855
## 5 VZ         0.0214 0.0745
## 6 Portfolio  0.0223 0.0822
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.

I would expect my perform of this portfolio to have relative low risk and high returns, almost all of my stocks have very low risk besides one with high risk. I would not invest in any just one asset because that incurs more risk, but if I had to I would invest in MSFT because it is lower risk with the highest return.