# 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("GOOG", "GME", "NVDA", "V")

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] "GME"  "GOOG" "NVDA" "V"
# weights
weights <- c(0.25, 0.25, 0.25, 0.25)
weights
## [1] 0.25 0.25 0.25 0.25
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 4 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 GME        0.25
## 2 GOOG       0.25
## 3 NVDA       0.25
## 4 V          0.25

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           0.00716
##  2 2013-02-28           0.0451 
##  3 2013-03-28           0.0484 
##  4 2013-04-30           0.0804 
##  5 2013-05-31           0.0311 
##  6 2013-06-28           0.0607 
##  7 2013-07-31           0.0398 
##  8 2013-08-30          -0.00126
##  9 2013-09-30           0.0418 
## 10 2013-10-31           0.0666 
## # 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 0.0441 0.0441
# Mean of Portfolio Return
portfolio_mean_tidyquant_builtin_percent <- mean(portfolio_returns_tbl$portfolio.returns)

portfolio_mean_tidyquant_builtin_percent
## [1] 0.0205988

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 %>%
    
    ggplot(aes(x = Stdev, y = mean, color = 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.

Based on the graph above, I would expect my portfolio to perform with a low risk and a moderate return. If you look at the individual stocks like V (Visa) and GOOG (Google), they are getting relatively the same return but Google has a slightly higher risk, therefore V is in “better” position. GME (GameStop) is not in a very good place because they are performing high risk with very little in return. Lastly is NVDA (NVIDIA), this stock is at a high risk but also very high return. The only reason this is not the best place to be in is because the risk is extremely high. The best place for one of these stocks to be in is the upper left quadrant, low risk and high return, none of them are in this place. The portfolio itself is closets to this position, even though they are in the best quadrant, they are in the lower end of it so they are not getting the highest return.