# 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("MSFT", "NVDA", "JPM")

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()
symbols
## [1] "JPM"  "MSFT" "NVDA"
# weights
weights <- c(0.4, 0.3, 0.3)
weights
## [1] 0.4 0.3 0.3
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 3 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 JPM         0.4
## 2 MSFT        0.3
## 3 NVDA        0.3

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          0.0380  
##  2 2013-02-28          0.0333  
##  3 2013-03-28          0.000498
##  4 2013-04-30          0.0803  
##  5 2013-05-31          0.0775  
##  6 2013-06-28         -0.0256  
##  7 2013-07-31          0.00859 
##  8 2013-08-30         -0.0148  
##  9 2013-09-30          0.0240  
## 10 2013-10-31          0.0132  
## # 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 0.0485 0.0485
# Mean of portfolio returns
portfolio_mean_tidyquant_builtin_percent <- mean(portfolio_returns_tbl$portfolio.returns)

portfolio_mean_tidyquant_builtin_percent
## [1] 0.02743348

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
## # A tibble: 4 × 3
##   asset       Mean  Stdev
##   <chr>      <dbl>  <dbl>
## 1 JPM       0.017  0.0556
## 2 MSFT      0.0216 0.0589
## 3 NVDA      0.0471 0.0881
## 4 Portfolio 0.0274 0.0485
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.

My overall portfolio outperforms both JPM and MSFT, while also maintaining a lower volatility. NVDA has a significantly higher return but is much more volatile. For this reason I would probably stay with my whole portfolio and not invest in just 1 stock.