# 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("AAPL", "TSLA", "AMZN", "EEM", "AGG")
prices <- tq_get(x    = symbols,
                 from = "2012-12-31",
                 to   = "2017-12-31")

2 Convert prices to returns

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: 300 × 3
##    asset date         returns
##    <chr> <date>         <dbl>
##  1 AAPL  2013-01-31 -0.156   
##  2 AAPL  2013-02-28 -0.0256  
##  3 AAPL  2013-03-28  0.00285 
##  4 AAPL  2013-04-30  0.000271
##  5 AAPL  2013-05-31  0.0222  
##  6 AAPL  2013-06-28 -0.126   
##  7 AAPL  2013-07-31  0.132   
##  8 AAPL  2013-08-30  0.0804  
##  9 AAPL  2013-09-30 -0.0217  
## 10 AAPL  2013-10-31  0.0920  
## # … with 290 more rows

3 Assign a weight to each asset

#Symbols
symbols <- asset_returns_tbl %>%
    distinct(asset) %>%
    pull()
symbols
## [1] "AAPL" "AGG"  "AMZN" "EEM"  "TSLA"
#Weights
weights <- c(1)
weights
## [1] 1
w_tbl <- tibble(symbols, weights)

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 = "months")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2013-01-31          -0.00600
##  2 2013-02-28          -0.122  
##  3 2013-03-28           0.0862 
##  4 2013-04-30           0.327  
##  5 2013-05-31           0.605  
##  6 2013-06-28          -0.0717 
##  7 2013-07-31           0.453  
##  8 2013-08-30           0.207  
##  9 2013-09-30           0.300  
## 10 2013-10-31           0.104  
## # … with 50 more rows

5 Calculate Portfolio

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 0.211 0.211
#Mean of portfolio returns
portfolio_mean_tidyquant_builtin_percent <- mean(portfolio_returns_tbl$portfolio.returns)

portfolio_mean_tidyquant_builtin_percent
## [1] 0.08211375

6 Plot

#Expected returns vs risk

sd_mean_table <- asset_returns_tbl %>%
    
    group_by(asset) %>%
    tq_performance(Ra = returns, 
                   performance_fun = table.Stats) %>%
    select(Mean = ArithmeticMean, Stdev) %>%
    ungroup() %>%

    
    #Add portfolio stdev
    add_row(tibble(asset = "Portfolio",
                   Mean = portfolio_mean_tidyquant_builtin_percent,
                   Stdev = portfolio_sd_tidyquant_builtin_percent$tq_sd))

sd_mean_table
## # A tibble: 6 × 3
##   asset       Mean  Stdev
##   <chr>      <dbl>  <dbl>
## 1 AAPL      0.015  0.0695
## 2 AGG       0.0017 0.0086
## 3 AMZN      0.0257 0.0739
## 4 EEM       0.0028 0.0419
## 5 TSLA      0.037  0.145 
## 6 Portfolio 0.0821 0.211
sd_mean_table %>%
    
    ggplot(aes(x = Stdev, y = Mean, color = asset)) + 
    geom_point() +
    ggrepel::geom_text_repel(aes(label = asset))

### 24 month rolling vol

rolling_sd_tbl <- portfolio_returns_tbl %>%
    
    tq_mutate(select = portfolio.returns, 
              mutate_fun = rollapply, 
              width = 24,
              FUN = sd,
              col_rename = "rolling_sd") %>%
    
    na.omit() %>%
    select(date, rolling_sd)

rolling_sd_tbl
## # A tibble: 37 × 2
##    date       rolling_sd
##    <date>          <dbl>
##  1 2014-12-31      0.234
##  2 2015-01-30      0.233
##  3 2015-02-27      0.229
##  4 2015-03-31      0.235
##  5 2015-04-30      0.237
##  6 2015-05-29      0.213
##  7 2015-06-30      0.211
##  8 2015-07-31      0.197
##  9 2015-08-31      0.208
## 10 2015-09-30      0.203
## # … with 27 more rows
rolling_sd_tbl %>%
    
    ggplot(aes(x = date, y = rolling_sd)) +
    geom_line(color = "cornflowerblue") +
    
    # Formatting
    scale_y_continuous(labels = scales::percent_format()) +
    
    #Labeling
    labs(x = NULL,
         y = NULL,
         title = "24 month rolling volatility") +
    theme(plot.title = element_text(hjust = 0.5))

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.

It looks like my portfolio has both a higher stdev than all of my holdings which does not make any sense, but based on your code along the portfolio comes in around the middle of all the stocks and is therefore less risky than some individual stocks and the portfolio should be less risky with a higher return than for the risk than a singular stock so i would invest my money in the portfolio and not in any one stock. This is because the risk to return relationship is better when invested in the entire portfolio.