# 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("RTX", "GD", "LMT", "BA")
prices <- tq_get(x    = symbols, 
                 get  = "stock.prices", 
                 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
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "BA"  "GD"  "LMT" "RTX"
# weights
weights <- c(0.35, 0.30, 0.20, 0.15)
weights
## [1] 0.35 0.30 0.20 0.15
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 4 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 BA         0.35
## 2 GD         0.3 
## 3 LMT        0.2 
## 4 RTX        0.15

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 -0.0224
##  2 2013-02-28  0.0349
##  3 2013-03-28  0.0727
##  4 2013-04-30  0.0405
##  5 2013-05-31  0.0642
##  6 2013-06-28  0.0184
##  7 2013-07-31  0.0764
##  8 2013-08-30 -0.0114
##  9 2013-09-30  0.0773
## 10 2013-10-31  0.0423
## # … with 50 more rows

5 Calculate CAPM Beta

5.1 Get Market Returns

market_returns_tbl <- tq_get(x    = "NQ=F", 
                 get  = "stock.prices", 
                 from = "2012-12-31", 
                 to   = "2017-12-31") %>% 
    
    # Convert prices
    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       0.653

6 Plot

Scatterplot of returns 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?

I would say that this portfolio is not very sensitive to the market. I used NASDAQ. The beta is 0.653. This means that is not super sensitive but also will follow the market slightly. From the graph, you can see that while the portfolio is wrapped around the market line, in the sense that it follows the same direction, the plots are far from the line itself.