# 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("AAPL", "MSFT", "META")

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     = "quarterly", 
                 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" "META" "MSFT"
#weights
weights <- c(0.30, 0.30, 0.40)
weights
## [1] 0.3 0.3 0.4
w_tbl <-  tibble(symbols, weights)
w_tbl
## # A tibble: 3 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AAPL        0.3
## 2 META        0.3
## 3 MSFT        0.4

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: 20 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2013-03-28          -0.0347 
##  2 2013-06-28           0.0388 
##  3 2013-09-30           0.256  
##  4 2013-12-31           0.126  
##  5 2014-03-31           0.0573 
##  6 2014-06-30           0.102  
##  7 2014-09-30           0.119  
##  8 2014-12-31           0.0281 
##  9 2015-03-31           0.00244
## 10 2015-06-30           0.0519 
## 11 2015-09-30          -0.0195 
## 12 2015-12-31           0.126  
## 13 2016-03-31           0.0390 
## 14 2016-06-30          -0.0648 
## 15 2016-09-30           0.136  
## 16 2016-12-30           0.00921
## 17 2017-03-31           0.155  
## 18 2017-06-30           0.0408 
## 19 2017-09-29           0.0918 
## 20 2017-12-29           0.0961

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 0.0752 0.0752
# Mean of Portfolio Returns
portfolio_mean_tidyquant_builtin_percent <- mean(portfolio_returns_tbl$portfolio.returns)

portfolio_mean_tidyquant_builtin_percent
## [1] 0.0678176

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 AAPL      0.045  0.119 
## 2 META      0.0946 0.162 
## 3 MSFT      0.0648 0.0855
## 4 portfolio 0.0678 0.0752
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.

As I can identify in the graph, I see that the portfolio I have chosen, proves relatively low risk compared towards it high returns. An expected return of almost 7% with a standard deviation of 7.5% as we can see in the graph. That is a result I am very happy with relative to every separate asset in the portfolio.

If I got the opportunity to invest all my money into one of these stocks, I would choose META due to its high risk and high return. Why I would do that is because I am young, with my whole life ahead of me, I can afford to take on more risk since I am saving long-term. Saving my money in Meta will generate more profit over time through compounding but I need to be able to tolerate the higher risk as well. That is a strategy I am using in my own portfolio, accumulating through DCA towards companies I find great towards a long term investment. Although, in the current market, I am more tilted towards the energy sectors and larger less volatile companies that has no debt or very little of it.