# Load packages

# Core
library(tidyverse)
library(tidyquant)
library(scales)
library(ggrepel)

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("WMT", "MSFT", "GE")
prices <- tq_get(x    = symbols, 
                 get = "stock.prices",
                 from = "2012-12-31",
                 to   = "2017-12-31")
symbols
## [1] "WMT"  "MSFT" "GE"

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"))

3 Assign a weight to each asset

symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "GE"   "MSFT" "WMT"
#Weights
weights <- c(0.25, 0.25, 0.5)
weights
## [1] 0.25 0.25 0.50
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 3 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 GE         0.25
## 2 MSFT       0.25
## 3 WMT        0.5

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           0.0342 
##  2 2013-02-28           0.0235 
##  3 2013-03-28           0.0371 
##  4 2013-04-30           0.0463 
##  5 2013-05-31           0.0104 
##  6 2013-06-28          -0.00434
##  7 2013-07-31           0.0147 
##  8 2013-08-30          -0.0291 
##  9 2013-09-30           0.0157 
## 10 2013-10-31           0.0565 
## # … with 50 more rows

5 Calculate 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 0.0313 0.0313
# Mean of Portfolio Returns
portfolio_mean_tidyquant_builtin_percent <-  mean(portfolio_returns_tbl$portfolio.returns)

portfolio_mean_tidyquant_builtin_percent
## [1] 0.009488039

6 Plot

Expected Returns VS Risk

# Expected Returns VS Risk
Sd_Mean_tbl <- asset_returns_tbl %>%
    
    group_by(asset) %>%
    tq_performance(Ra = returns, 
                   performance_fun = table.Stats) %>%
    select(Stdev, Mean = ArithmeticMean) %>%
    ungroup() %>%
    
    # Add Portfolio Standard Deviation
    
    add_row(tibble(asset = "portfolio",
    Mean = portfolio_mean_tidyquant_builtin_percent,
    Stdev = portfolio_sd_tidyquant_builtin_percent$tq_sd))
Sd_Mean_tbl
## # A tibble: 4 × 3
##   asset      Stdev     Mean
##   <chr>      <dbl>    <dbl>
## 1 GE        0.0544 -0.0003 
## 2 MSFT      0.0589  0.0216 
## 3 WMT       0.0471  0.0083 
## 4 portfolio 0.0313  0.00949
Sd_Mean_tbl %>%
    
    ggplot(aes(x = Stdev, y = Mean, color = asset)) +
    geom_point() +
    ggrepel::geom_text_repel(aes(label = asset))

24 Months Rolling Volitility

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     0.0304
##  2 2015-01-30     0.0331
##  3 2015-02-27     0.0336
##  4 2015-03-31     0.0346
##  5 2015-04-30     0.0343
##  6 2015-05-29     0.0351
##  7 2015-06-30     0.0364
##  8 2015-07-31     0.0365
##  9 2015-08-31     0.0397
## 10 2015-09-30     0.0397
## # … with 27 more rows
rolling_sd_tbl %>%
    ggplot(aes(x = date, y = rolling_sd)) +
    geom_line(color = "orange") +

# Formating
   scale_y_continuous(labels = scales::percent_format()) +
    labs(x = NULL, y = NULL, title = "24 Month Rolling Volitility") +
    theme(plot.title = element_text(hjust = .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.

I would expect the portfolio to have a less volatile return than any of the individual stocks within it. This is indicated by the lower standard deviation of the portfolio, meaning the return range is going to be much smaller. At the same time the average return is competitive or better than two of the three stocks within the portfolio. I would not likely invest my money in one individual stock over the portfolio. Even the stock that returns the most within my portfolio has a high standard deviation, and therefore has more variance than the other companies. The portfolio has the benefit to have dropped in rolling volitility within the years that are being measured.