# 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

# Choose stocks

symbols <- c("AMZN", "NVDA", "WMT")

# Using tq_get() ----
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 %>%

    # Calculate monthly returns
    group_by(symbol) %>%
    tq_transmute(select = adjusted,
                 mutate_fun = periodReturn,
                 period = "monthly",
                 type = "log") %>%
    slice(-1) %>%
    ungroup() %>%

    # remane
    set_names(c("asset", "date", "returns"))

# period_returns = c("yearly", "quarterly", "monthly", "weekly")

3 Assign a weight to each asset (change the weigting scheme)

symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()

w <- c(0.25,
       0.25,
       0.20)

w_tbl <- tibble(symbols, w)

4 Build a portfolio

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

portfolio_returns_rebalanced_monthly_tbl
## # A tibble: 60 × 2
##    date         returns
##    <date>         <dbl>
##  1 2013-01-31  0.0191  
##  2 2013-02-28  0.0108  
##  3 2013-03-28  0.0178  
##  4 2013-04-30  0.0131  
##  5 2013-05-31  0.0220  
##  6 2013-06-28 -0.000717
##  7 2013-07-31  0.0364  
##  8 2013-08-30 -0.0228  
##  9 2013-09-30  0.0427  
## 10 2013-10-31  0.0394  
## # ℹ 50 more rows

5 Compute Standard Deviation

portfolio_sd_tidyquant_builtin_percent <- portfolio_returns_rebalanced_monthly_tbl %>%
    
    tq_performance(Ra = returns,
                   Rb = NULL, 
                   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 0.0342 0.0342
# Mean of portfolio returns
portfolio_mean_tidyquant_builtin_percent <- 
mean(portfolio_returns_rebalanced_monthly_tbl)

portfolio_mean_tidyquant_builtin_percent
## [1] NA

6 Plot: Expected Returns versus 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 AMZN       0.0257 0.0739
## 2 NVDA       0.0471 0.0881
## 3 WMT        0.0083 0.0471
## 4 Portfolio NA      0.0342

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.

Some single stocks have the potential to give you a larger return compared to your portfolio, in this case NVDA has the highest standard deviation making them the best stock to choose from the ones selected. Sticks with higher expected returns had the potential for more risk, as NVDA with 0.0471 with the highest and WMT with the lowest at 0.0083. The safest choice is up to personal prefferance, in this case I would choose AMZN where it sits right in the middle between risk and return, giving you a strong return potential with minimal risk taken.