# 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("TSLA", "META", "XOM", "AAPL", "PG", "AMZN")

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] "AAPL" "AMZN" "META" "PG"   "TSLA" "XOM"
# weights
weights <- c(0.2, 0.2, 0.2, 0.1, 0.2, 0.1)
weights
## [1] 0.2 0.2 0.2 0.1 0.2 0.1
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 6 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AAPL        0.2
## 2 AMZN        0.2
## 3 META        0.2
## 4 PG          0.1
## 5 TSLA        0.2
## 6 XOM         0.1

4 Build a 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           0.0458 
##  2 2013-02-28          -0.0450 
##  3 2013-03-28           0.00821
##  4 2013-04-30           0.0767 
##  5 2013-05-31           0.111  
##  6 2013-06-28           0.00424
##  7 2013-07-31           0.174  
##  8 2013-08-30           0.0615 
##  9 2013-09-30           0.0789 
## 10 2013-10-31           0.0223 
## # 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 0.0465 0.0465
# Mean of portfolio returns
portfolio_mean_tidyquant_builtin_percent <- mean(portfolio_returns_tbl$portfolio.returns)

portfolio_mean_tidyquant_builtin_percent
## [1] 0.02280989

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 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: 7 × 3
##   asset       Mean  Stdev
##   <chr>      <dbl>  <dbl>
## 1 AAPL      0.015  0.0695
## 2 AMZN      0.0257 0.0739
## 3 META      0.0315 0.0838
## 4 PG        0.0077 0.0372
## 5 TSLA      0.037  0.145 
## 6 XOM       0.0021 0.0405
## 7 Portfolio 0.0228 0.0465
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     0.0568
##  2 2015-01-30     0.0571
##  3 2015-02-27     0.0548
##  4 2015-03-31     0.0559
##  5 2015-04-30     0.0553
##  6 2015-05-29     0.0527
##  7 2015-06-30     0.0525
##  8 2015-07-31     0.0426
##  9 2015-08-31     0.0449
## 10 2015-09-30     0.0432
## # 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.

In terms of expected returns, TSLA has the highest mean return, suggesting that it offers the potential for the highest returns among the individual stocks. However, the portfolio’s mean return is also quite competitive compared to the individual stocks.

Considering risk, TSLA has the highest standard deviation, indicating that it is the riskiest among the individual stocks. On the other hand, the portfolio has a lower standard deviation, suggesting that it has a relatively lower level of risk compared to TSLA and some of the other individual stocks.

In result of these finding, I am able to conclude the following. Investing all my money in any individual stock, such as TSLA, would potentially yield higher returns but at the cost of significantly higher risk. Conversely, by investing in the portfolio, I can achieve a balance between risk and return. The portfolio’s mean return, although significantly lower than TSLA’s, is still competitive, and it offers the advantage of diversification and lower risk compared to any individual stock, as evidenced by its standard deviation.