# 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", "MSFT", "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 <- asset_returns_tbl %>% distinct(asset) %>% pull()

w <- c(0.35,
       0.35,
       0.30)

w_tbl <- tibble(symbols, w)

4 Build a portfolio

portfolio_returns_tbl <- asset_returns_tbl %>%
    
    tq_portfolio(assets_col   = asset,
                 returns_col  = returns,
                 weights      = w_tbl,
                 col_rename   = "returns",
                 rebalance_on = "months")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31 -0.0231 
##  2 2013-02-28  0.0178 
##  3 2013-03-28  0.00654
##  4 2013-04-30  0.0570 
##  5 2013-05-31  0.0450 
##  6 2013-06-28 -0.0435 
##  7 2013-07-31  0.0247 
##  8 2013-08-30  0.0281 
##  9 2013-09-30  0.00310
## 10 2013-10-31  0.108  
## # ℹ 50 more rows

5 Compute Standard Deviation

portfolio_sd_tidyquant_builtin_percent <- portfolio_returns_tbl %>%
    
    tq_performance(Ra = returns,
                   Rb = NULL, 
                   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 0.0449  4.49

6 Plot: Expected Returns versus Risk

asset_returns_sd_mean_tbl <- asset_returns_tbl %>%

    group_by(asset) %>%
    tq_performance(Ra = returns,
                   Rb = NULL,
                   performance_fun = table.Stats) %>%

    select(asset, Mean = ArithmeticMean, Stdev) %>%
    ungroup() %>%

    add_row(tibble(asset = "Portfolio",
                   Mean  = mean(portfolio_returns_tbl$returns),
                   Stdev = sd(portfolio_returns_tbl$returns)))


asset_returns_sd_mean_tbl %>%

    ggplot(aes(Stdev, Mean, col = asset)) +
    geom_point() +
    ggrepel::geom_text_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.
My portfolio should perform relatively well relative to my assets within the portfolio. The portfolio has a lower standard deviation than any of the assets, which translates to lower risk. It falls around the middle of my distribution in terms of returns. I would not invest in any single stock instead of my portfolio because my portfolio has much lower risk than any of the stocks and a very middle-ground return. If anything I would consider giving MSFT and GOOG more weight in my portfolio and taking some from AAPL because of how it is high risk and low return.