# 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("MTN", "AAPL", "NFLX", "DIS", "GE")

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] "AAPL" "DIS"  "GE"   "MTN"  "NFLX"
#weights
weights <- c(0.25,0.25,0.2,0.2,0.1)
weights
## [1] 0.25 0.25 0.20 0.20 0.10
w_tbl <- tibble(symbols,weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AAPL       0.25
## 2 DIS        0.25
## 3 GE         0.2 
## 4 MTN        0.2 
## 5 NFLX       0.1

4 Build a portfolio

# ?tq_portfolio

portfolio_returns_tbl <- asset_returns_tbl %>% 
    
    tq_portfolio(assets_col = asset, 
                returns_col = returns,
                weights = w_tbl,
                replace_on = "months")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2013-01-31            0.0461
##  2 2013-02-28            0.0360
##  3 2013-03-28            0.0340
##  4 2013-04-30            0.0329
##  5 2013-05-31            0.0330
##  6 2013-06-28           -0.0411
##  7 2013-07-31            0.0795
##  8 2013-08-30            0.0183
##  9 2013-09-30            0.0387
## 10 2013-10-31            0.0591
## # 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 0.042 0.042
# Mean of Portfolio Returns
portfolio_mean_tidyquant_builtin_percent <-
mean(portfolio_returns_tbl$portfolio.returns)

portfolio_mean_tidyquant_builtin_percent
## [1] 0.01875382

6 Plot: Expected Returns versus Risk

# Expected Return vs Risk
sd_mean_tbl <- asset_returns_tbl %>%
    
    group_by(asset) %>%
    tq_performance(Ra = returns,
                   performance_fun = table.Stats) %>%
    select(Mean = ArithmeticMean, Stdev) %>%
    ungroup() %>%
    # mutate(Stdev = portfolio_sd_tidyquant_builtin_percent$Stdev * 100) %>%         

# 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: 6 × 3
##   asset        Mean  Stdev
##   <chr>       <dbl>  <dbl>
## 1 AAPL       0.015  0.0695
## 2 DIS        0.014  0.0528
## 3 GE        -0.0003 0.0544
## 4 MTN        0.0245 0.0493
## 5 NFLX       0.0446 0.133 
## 6 Portfolio  0.0188 0.042
sd_mean_tbl %>%
    ggplot(aes(x = Stdev, y = Mean, color = asset)) +
    geom_point() +
    ggrepel::geom_text_repel(aes(label = asset))

###24 Months Rolling Volitility

rolling_sd_tbl <- portfolio_returns_tbl %>%
    tq_mutate(select = portfolio.returns,
                       mutate_fun = rollapply,
                       width      = 24,
                       FUN        = sd,
                       col_rename = "rolling_sd") %>%
    na.omit() %>%
    select(date,rolling_sd)
rolling_sd_tbl
## # A tibble: 37 × 2
##    date       rolling_sd
##    <date>          <dbl>
##  1 2014-12-31     0.0392
##  2 2015-01-30     0.0389
##  3 2015-02-27     0.0407
##  4 2015-03-31     0.0412
##  5 2015-04-30     0.0421
##  6 2015-05-29     0.0423
##  7 2015-06-30     0.0400
##  8 2015-07-31     0.0388
##  9 2015-08-31     0.0428
## 10 2015-09-30     0.0442
## # ℹ 27 more rows
rolling_sd_tbl %>%
    
    ggplot(aes(x = date, y = rolling_sd)) +
    geom_line(color = "cornflowerblue") +
    
    #Formatting
    scale_y_continuous(labels = scales::percent_format()) +
    
    #Labeling
    labs(x = NULL,
         y = NULL,
         title = "24-Month Rolling Volitility") +
         theme(plot.title = element_text(hjust = 0.5))

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 portfolio is a good balance between risk and return. It has lower risk than most individual stocks while still offering decent returns. For example, Netflix has the highest return but also the highest risk, while General Electric has low risk but almost no return. The portfolio spreads out the risk, so it avoids the big ups and downs of single stocks.So No, the portfolio is a better choice because it balances risk and return, unlike individual stocks that are either too risky or offer low returns