# 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

symbols <- c( "AMZN", "SKT", "BBWI", "TSLA", "JBLU")

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] "AMZN" "BBWI" "JBLU" "SKT"  "TSLA"
#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 AMZN       0.25
## 2 BBWI       0.25
## 3 JBLU       0.2 
## 4 SKT        0.2 
## 5 TSLA       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,
               rebalance_on = "months", 
               col_rename = "returns" )
portfolio_returns_tbl
## # A tibble: 60 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31  0.0407 
##  2 2013-02-28 -0.0129 
##  3 2013-03-28  0.0370 
##  4 2013-04-30  0.0596 
##  5 2013-05-31  0.0366 
##  6 2013-06-28  0.0113 
##  7 2013-07-31  0.0766 
##  8 2013-08-30 -0.00841
##  9 2013-09-30  0.0835 
## 10 2013-10-31  0.0517 
## # ℹ 50 more rows

5 Calculate CAPM Beta

5.1 Get market returns

market_rerturns_tbl <- tq_get(x = "SPY",
                 get = "stock.prices",
                 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)

5.2 Join returns

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

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       0.875

6 Plot: Scatter with regression line

Scatterplot of returns w/ 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. # A beta less than 1 tells us that the investment is less volatile than the market, while a beta greater than 1 indicates that the investment’s price will be more volatile than the market. So, this portfolio appears to be relatively stable in comparison. Does the plot confirm the beta coefficient you calculated? # The plot does confirm the beta coefficient, because the slope is lesser than a 45 degree angle.