# 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("AMZN", "MSFT", "TSLA")

prices <- tq_get(x = symbols,
                 get = "stock.prices",
                 from = "2012-12-31",
                 to = "2017-12-31")

2 Convert prices to returns (monthly)

asset_returnns_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)

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

4 Build a portfolio

portfolio_returns_tbl <- asset_returnns_tbl %>%
    
    tq_portfolio(assets_col = asset,
                 returns_col = returns,
                 weights = w_tbl, 
                 rebalance_on = "quarters")

portfolio_returns_tbl
## # A tibble: 20 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2013-03-28          0.0861  
##  2 2013-06-28          0.488   
##  3 2013-09-30          0.262   
##  4 2013-12-31          0.00993 
##  5 2014-03-31          0.109   
##  6 2014-06-30          0.0532  
##  7 2014-09-30          0.0358  
##  8 2014-12-31         -0.0439  
##  9 2015-03-31         -0.0490  
## 10 2015-06-30          0.213   
## 11 2015-09-30          0.0214  
## 12 2015-12-31          0.139   
## 13 2016-03-31         -0.0556  
## 14 2016-06-30          0.00358 
## 15 2016-09-30          0.0686  
## 16 2016-12-30          0.0102  
## 17 2017-03-31          0.175   
## 18 2017-06-30          0.147   
## 19 2017-09-29         -0.000558
## 20 2017-12-29          0.0653

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 0.128 0.128
portfolio_mean_tidyquant_builtin_percent <- mean(portfolio_returns_tbl$portfolio.returns)

portfolio_mean_tidyquant_builtin_percent
## [1] 0.08691008

6 Plot: Expected Returns versus Risk

sd_mean_tbl <- asset_returnns_tbl %>%
    
    group_by(asset) %>%
    tq_performance(Ra = returns,
                   performance_fun = table.Stats) %>%
    select(Mean = ArithmeticMean, Stdev) %>%
    ungroup() %>%
    mutate(Stdev = Stdev * 100,
           Mean = Mean * 100) %>%
    
    #Add portfolio sd
    add_row(tibble(asset = "Portfolio",
                   Mean = portfolio_mean_tidyquant_builtin_percent * 100,
                   Stdev = portfolio_sd_tidyquant_builtin_percent$tq_sd))

sd_mean_tbl
## # A tibble: 4 × 3
##   asset      Mean  Stdev
##   <chr>     <dbl>  <dbl>
## 1 AMZN       7.7  12.9  
## 2 MSFT       6.48  8.55 
## 3 TSLA      11.1  30.0  
## 4 Portfolio  8.69  0.128
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 performs better than the individual Amazon and Microsoft stocks. The standard deviation is lower than 1% which means the volatility is extremely low (risk is really low). I belive that investing in the portfolio would be better than investing in one specific stock.