# 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("META", "AAPL", "NFLX", "MSFT", "GOOG")
prices <- tq_get (x = symbols,
                  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
symbol <- asset_returns_tbl %>% 
    distinct(asset) %>% 
    pull()
symbols
## [1] "META" "AAPL" "NFLX" "MSFT" "GOOG"
# 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 META       0.25
## 2 AAPL       0.25
## 3 NFLX       0.2 
## 4 MSFT       0.2 
## 5 GOOG       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  0.127   
##  2 2013-02-28 -0.00255 
##  3 2013-03-28 -0.00896 
##  4 2013-04-30  0.0800  
##  5 2013-05-31 -0.000614
##  6 2013-06-28 -0.0410  
##  7 2013-07-31  0.145   
##  8 2013-08-30  0.0851  
##  9 2013-09-30  0.0633  
## 10 2013-10-31  0.0600  
## # … with 50 more rows

5 Calculate CAPM Beta

5.1 Get market returns

market_returns_tbl <- tq_get (x = "^IXIC",
                  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_returns_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        1.08

6 Plot: Scatter with regression line

portfolio_market_returns_tbl %>%
    
    ggplot(aes(x = market_returns,
               y = portfolio_returns)) +
    geom_point(color = "violet") +
    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?

I used the NASDAQ Index for the market as my portfolio is full of technology based companies. My portfolio’s beta coefficient is 0.968 which means it is less volatile than the market of 1. The plot does confirm my beta calculations as my portfolio for the most part follows the market. However, there are many outliers in my portfolio as the tech industry is a very volatile industry.