# Load packages

# Core
library(tidyverse)
library(tidyquant)

Goal

Calculate and visualize your portfolio’s beta.

Choose your stocks and the baseline market.

from 2012-12-31 to present

1 Import stock prices

#choose stocks 
symbols <- c("NFLX", "TSLA", "GOOG", "COST")

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

2 Convert prices to returns

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

symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols 
## [1] "COST" "GOOG" "NFLX" "TSLA"
# weights 
weights <- c(0.25, 0.25, 0.25, 0.25)
weights
## [1] 0.25 0.25 0.25 0.25
w_tbl <- tibble(symbols, weights)
w_tbl 
## # A tibble: 4 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 COST       0.25
## 2 GOOG       0.25
## 3 NFLX       0.25
## 4 TSLA       0.25

4 Build a portfolio

# ?tq_portfolio 

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

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date       returns
##    <date>       <dbl>
##  1 2013-01-31 0.196  
##  2 2013-02-28 0.0265 
##  3 2013-03-28 0.0321 
##  4 2013-04-30 0.136  
##  5 2013-05-31 0.177  
##  6 2013-06-28 0.0108 
##  7 2013-07-31 0.110  
##  8 2013-08-30 0.0716 
##  9 2013-09-30 0.0707 
## 10 2013-10-31 0.00978
## # ℹ 50 more rows

5 Calculate CAPM Beta

5.1 Get market returns

market_returns_tbl <- tq_get(x     = "SPY", 
                 from  = "2012-12-31", 
                 to    = "2017-12-31") %>%  
   
    # Convert Prices to Returns  
    tq_transmute(select     = adjusted, 
                 mutate_fun = periodReturn, 
                 period     = "monthly", 
                 type      = "log", 
                 col_rename = "returns") %>% 
    
    slice(-1) 

market_returns_tbl
## # A tibble: 60 × 2
##    date       returns
##    <date>       <dbl>
##  1 2013-01-31  0.0499
##  2 2013-02-28  0.0127
##  3 2013-03-28  0.0373
##  4 2013-04-30  0.0190
##  5 2013-05-31  0.0233
##  6 2013-06-28 -0.0134
##  7 2013-07-31  0.0504
##  8 2013-08-30 -0.0305
##  9 2013-09-30  0.0312
## 10 2013-10-31  0.0453
## # ℹ 50 more rows

5.2 Join Returns

portfolio_market_returns_tbl <- left_join(market_returns_tbl, portfolio_returns_tbl, by = "date") %>% 
    
    set_names("date", "market_returns", "portfolio_returns")

portfolio_market_returns_tbl
## # A tibble: 60 × 3
##    date       market_returns portfolio_returns
##    <date>              <dbl>             <dbl>
##  1 2013-01-31         0.0499           0.196  
##  2 2013-02-28         0.0127           0.0265 
##  3 2013-03-28         0.0373           0.0321 
##  4 2013-04-30         0.0190           0.136  
##  5 2013-05-31         0.0233           0.177  
##  6 2013-06-28        -0.0134           0.0108 
##  7 2013-07-31         0.0504           0.110  
##  8 2013-08-30        -0.0305           0.0716 
##  9 2013-09-30         0.0312           0.0707 
## 10 2013-10-31         0.0453           0.00978
## # ℹ 50 more rows

5.3 CAPM Beta

portfolio_market_returns_tbl %>% 
    
    tq_performance(Ra = portfolio_returns, 
                   Rb = market_returns, 
                   performance_fun = CAPM.beta )
## # A tibble: 1 × 1
##   CAPM.beta.1
##         <dbl>
## 1       0.981

6 Plot: Scatter with regression line

portfolio_market_returns_tbl %>%
    
    ggplot(aes(x = market_returns, 
               y = portfolio_returns)) +
    geom_point(color = "cornflowerblue") +
    geom_smooth(method = "lm", se = FALSE, size = 1.5, color = tidyquant::palette_light()[3]) + 
    
    labs(y = "Portfolio Returns", 
         x = "Market Returns")

How sensitive is your portfolio to the market? Discuss in terms of the beta coefficient. Does the plot confirm the beta coefficient you calculated?

My portfolio, as shown by the graph, is not that sensitive to the market, due to the points not being that clustered around the line. Although it has a beta of .981, which means we should see the portfolio volatility be more in line with the market volatility