# 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("JPM", "GOOG")
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" "JPM"
# weights
weights <- c(0.5, 0.5)
weights
## [1] 0.5 0.5
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 2 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 GOOG        0.5
## 2 JPM         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            0.0703
##  2 2013-02-28            0.0487
##  3 2013-03-28           -0.0195
##  4 2013-04-30            0.0380
##  5 2013-05-31            0.0814
##  6 2013-06-28           -0.0115
##  7 2013-07-31            0.0349
##  8 2013-08-30           -0.0725
##  9 2013-09-30            0.0282
## 10 2013-10-31            0.0835
## # … with 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)*100)

portfolio_sd_tidyquant_builtin_percent
## # A tibble: 1 × 2
##    Stdev tq_sd
##    <dbl> <dbl>
## 1 0.0413  4.13
# Mean of Portfolio Returns
portfolio_mean_tidyquant_builtin_percent <- mean(portfolio_returns_tbl$portfolio.returns)

portfolio_mean_tidyquant_builtin_percent
## [1] 0.01758102

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() %>%
    mutate(Stdev = Stdev * 100,
           Mean = Mean * 100 ) %>%
    
    # Add portfolio sd
    add_row(tibble(asset = "Portfolio", 
                   Mean = portfolio_mean_tidyquant_builtin_percent * 100, 
                   Stdev = portfolio_sd_tidyquant_builtin_percent$tq_sd))

sd_mean_tbl
## # A tibble: 3 × 3
##   asset      Mean Stdev
##   <chr>     <dbl> <dbl>
## 1 GOOG       1.81  5.35
## 2 JPM        1.7   5.56
## 3 Portfolio  1.76  4.13
sd_mean_tbl %>%
    
    ggplot(aes(x = Stdev, y = Mean, color = asset)) +
    geom_point() +
    ggrepel::geom_text_repel(aes(label = asset))

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     0.0401
##  2 2015-01-30     0.0413
##  3 2015-02-27     0.0431
##  4 2015-03-31     0.0430
##  5 2015-04-30     0.0426
##  6 2015-05-29     0.0400
##  7 2015-06-30     0.0398
##  8 2015-07-31     0.0437
##  9 2015-08-31     0.0413
## 10 2015-09-30     0.0423
## # … 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.

My portfolio assumes some risk, but I am comfortable with standard devations just above five for both my stocks, as well as a portfolio st. deviation of 4.13. My portfolio has two stocks that can be considered good buys especially for more conservative investors. The mean returns for my portfolio is 1.76 compared to 1.7 for JPM and 1.81 for GOOG. The risk for my portfolio is lower than that of the two Individual stocks as well. With an average return around 1.76 and standard deviation of 4.13 compared to higher standard deviations over five for both individual stocks, I would not recommend investing all my money an one of the individual stocks. A conservative investor should feel comfortable with little risk and solid returns. I would not recommend investing all your money in one of these stocks, but rather look to diversify even beyond these two stocks. Given the two stocks are apart of different economic sectors, they are more protected if one crashes. It would be smart for an investor to further diversify with other sectors of the market to protect their money even more in the future. GOOG and JPM are relatively safe investments with a possibility for good returns, especially for long-term investors.