# 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("XOM", "SHEL", "BP", "CVX")

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] "BP"   "CVX"  "SHEL" "XOM"
#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 BP         0.25
## 2 CVX        0.25
## 3 SHEL       0.25
## 4 XOM        0.25

4 Build a 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  0.0477 
##  2 2013-02-28 -0.0292 
##  3 2013-03-28  0.0150 
##  4 2013-04-30  0.0213 
##  5 2013-05-31  0.00575
##  6 2013-06-28 -0.0263 
##  7 2013-07-31  0.0401 
##  8 2013-08-30 -0.0338 
##  9 2013-09-30  0.00754
## 10 2013-10-31  0.0360 
## # ℹ 50 more rows

5 Calculate CAPM Beta

5.1 Get market returns

market_returns_tbl <- tq_get(x    = "XLE",
                 get  = "stock.prices",
                 from = "2012-12-31", 
                 to   = "2017-12-31") %>%
    
    # Convert prices to monthly 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, "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       0.806

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?

With a beta of 0.806 this portfolio is only about 80% as sensitive to variation as its overall industry. Meaning if the oil-based energy market should gain 100 basis points within a month, this portfolio would be expected to gain 80.6 basis points within the same period of time.