# 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", "GOOG", "WMT", "TGT", "LOW")

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] "GOOG" "LOW"  "NKE"  "TGT"  "WMT"
# 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 GOOG       0.25
## 2 LOW        0.25
## 3 NKE        0.2 
## 4 TGT        0.2 
## 5 WMT        0.1

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          0.0516  
##  2 2013-02-28          0.0272  
##  3 2013-03-28          0.0353  
##  4 2013-04-30          0.0386  
##  5 2013-05-31          0.0260  
##  6 2013-06-28         -0.000549
##  7 2013-07-31          0.0336  
##  8 2013-08-30         -0.0329  
##  9 2013-09-30          0.0505  
## 10 2013-10-31          0.0674  
## # 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 0.0326 0.0326
# 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 0.0326 0.0326

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 GOOG      0.0181 0.0535
## 2 LOW       0.0174 0.0535
## 3 NKE       0.0158 0.0531
## 4 TGT       0.0043 0.0609
## 5 WMT       0.0083 0.0471
## 6 Portfolio 0.0137 0.0326
sd_mean_tbl %>%
    
    ggplot(aes(x = Stdev, y = Mean, color = asset)) +
    geom_point() +
    ggrepel::geom_text_repel(aes(label = asset))

24 Months Rolling Volatility

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     0.0333
##  2 2015-01-30     0.0334
##  3 2015-02-27     0.0341
##  4 2015-03-31     0.0339
##  5 2015-04-30     0.0355
##  6 2015-05-29     0.0355
##  7 2015-06-30     0.0355
##  8 2015-07-31     0.0372
##  9 2015-08-31     0.0370
## 10 2015-09-30     0.0362
## # ℹ 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.

The monthly return is expected to be 1.37%, and the standard deviation is expected to be 3.26%. GOOG and LOW both have a standard deviation of 5.35%, then NKE at 5.31%, TGT at 6.09%, and WMT at 4.71%. Because of the diversification of stocks, I would not choose to invest all of my money into any of just one stock (WMT, TGT, LOW, GOOG, or NKE). This would give more of a chance of risk rather than return on this portfolio. This particular portfolio has a lower level of risk. GOOG has the highest level of risk (1.81%), then LOW (1.74%), NKE (1.58%), WMT (0.83%), and TGT at 0.43%. The return depends on each of the weights of the stocks, meaning a stock may have a higher expected return, but it also comes with a higher level of risk. For example, GOOG has the highest return at 1.81%, but a very high risk at 5.35%. Compared to the return, and the portfolios risk of 3.26%, this is something to think about. The stocks given the diversification provide a balance between risk and return. It gives up potential return with LOW, GOOG, and NKE, and risk is lowered.