# 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("RGR", "AMZN", "TSLA")
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] "AMZN" "RGR"  "TSLA"
# weights
weights <- c(0.25, 0.2, 0.1)
weights
## [1] 0.25 0.20 0.10
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 3 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AMZN       0.25
## 2 RGR        0.2 
## 3 TSLA       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           0.0467 
##  2 2013-02-28           0.00601
##  3 2013-03-28          -0.00280
##  4 2013-04-30           0.0253 
##  5 2013-05-31           0.0721 
##  6 2013-06-28           0.00802
##  7 2013-07-31           0.0542 
##  8 2013-08-30           0.0138 
##  9 2013-09-30           0.0759 
## 10 2013-10-31           0.0278 
## # 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 0.0331 0.0331
# Mean of portfolio returns

portfolio_sd_tidyquant_builtin_percent <- mean(portfolio_returns_tbl$portfolio.returns)

portfolio_sd_tidyquant_builtin_percent
## [1] 0.01125749

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_sd_tidyquant_builtin_percent,
                   Stdev = portfolio_sd_tidyquant_builtin_percent))

sd_mean_tbl
## # A tibble: 4 × 3
##   asset       Mean  Stdev
##   <chr>      <dbl>  <dbl>
## 1 AMZN      0.0257 0.0739
## 2 RGR       0.0057 0.0965
## 3 TSLA      0.037  0.145 
## 4 Portfolio 0.0113 0.0113
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.

I expect the assets of TSLA and AMZN will perform riskier compared to the portfolio, but highs will be higher and lows will be lower. RGR will perform worst than the portfolio and has a way bigger standard deviatio, similar to TSLA and AMZN.