# 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(0.60, 0.55, 0.50, 0.45)
weights
## [1] 0.60 0.55 0.50 0.45
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 4 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 GME        0.6 
## 2 GOOG       0.55
## 3 NVDA       0.5 
## 4 V          0.45

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           0.00775
##  2 2013-02-28           0.101  
##  3 2013-03-28           0.105  
##  4 2013-04-30           0.185  
##  5 2013-05-31           0.0578 
##  6 2013-06-28           0.144  
##  7 2013-07-31           0.0971 
##  8 2013-08-30          -0.00136
##  9 2013-09-30           0.0796 
## 10 2013-10-31           0.150  
## # 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 0.0956 0.0956
# Mean of Portfolio Return
portfolio_mean_tidyquant_builtin_percent <- mean(portfolio_returns_tbl$portfolio.returns)

portfolio_mean_tidyquant_builtin_percent
## [1] 0.04097018

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.

The graph suggests that my portfolio carries significant risk but also the potential for high returns. For example, both Visa (V) and Google (GOOG) offer similar returns, though Google has a slightly higher risk, making Visa the better option. GameStop (GME) is in a weak position, as it has high risk but minimal returns. NVIDIA (NVDA) offers both high risk and high return, but the extreme level of risk makes it less ideal. Ideally, a stock would be in the upper left quadrant, where risk is low and returns are high, but none of these stocks fit that profile.