# 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("X", "ZEUS", "CMC", "GOOG", "TSLA")
prices <- tq_get(x  = symbols,
    get = "stock.prices", 
     from = "2012-12-31",
      to  = "2017-12-31")

2 Convert prices to returns (monthly)

asset_return_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_return_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "CMC"  "GOOG" "TSLA" "X"    "ZEUS"
weights <- c(0.3, 0.2, 0.2, 0.15, 0.15)
weights
## [1] 0.30 0.20 0.20 0.15 0.15
w_tbl <- tibble(symbols, weights)
w_tbl 
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 CMC        0.3 
## 2 GOOG       0.2 
## 3 TSLA       0.2 
## 4 X          0.15
## 5 ZEUS       0.15

4 Build a portfolio

portfolio_returns_tbl <- asset_return_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           0.0525 
##  2 2013-02-28          -0.0218 
##  3 2013-03-28           0.0183 
##  4 2013-04-30           0.0162 
##  5 2013-05-31           0.181  
##  6 2013-06-28           0.00154
##  7 2013-07-31           0.0809 
##  8 2013-08-30           0.0193 
##  9 2013-09-30           0.104  
## 10 2013-10-31           0.0448 
## # ℹ 50 more rows

5 Compute Standard Deviation

portfolio_sd_tidyquant_builitin_percent <- portfolio_returns_tbl %>%

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

   portfolio_sd_tidyquant_builitin_percent
## # A tibble: 1 × 2
##    Stdev  tq_sd
##    <dbl>  <dbl>
## 1 0.0715 0.0715
   portfolio_mean_tidyquant_builitin_percent <- 
    mean(portfolio_returns_tbl$portfolio.returns)

portfolio_mean_tidyquant_builitin_percent
## [1] 0.01462342

6 Plot: Expected Returns versus Risk

# Expected Returns vs Risk
sd_mean_tbl <- asset_return_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_builitin_percent, 
                   Stdev = portfolio_sd_tidyquant_builitin_percent$tq_sd))

sd_mean_tbl
## # A tibble: 6 × 3
##   asset        Mean  Stdev
##   <chr>       <dbl>  <dbl>
## 1 CMC        0.0084 0.0763
## 2 GOOG       0.0181 0.0535
## 3 TSLA       0.037  0.145 
## 4 X          0.0073 0.194 
## 5 ZEUS      -0.0001 0.173 
## 6 Portfolio  0.0146 0.0715
sd_mean_tbl %>%
    
    ggplot(aes(x = Stdev, y = Mean, color = asset)) + 
    geom_point() + 
    ggrepel::geom_label_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.

Based on the graph is has a low standard deviation meaning it is less volatile and less risky of an investment, however is also has a lower return than 2 of the assets google and tesla. Google even having a lower standard deviation while still having a higher return.