# Load packages
 
# Core
library(tidyverse)
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library(tidyquant)
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Goal

Calculate and visualize your portfolio’s beta.

Choose your stocks and the baseline market.

I chose the stocks XOM, CVX, COP, SLB and EOG. I chose them because they are all competitors within the aame industry.

from 2012-12-31 to present

1 Import stock prices

symbols <- c("XOM", "CVX", "COP", "SLB", "EOG")
 
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] "COP" "CVX" "EOG" "SLB" "XOM"
# 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 COP        0.25
## 2 CVX        0.25
## 3 EOG        0.2 
## 4 SLB        0.2 
## 5 XOM        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")
## Warning in check_weights(weights, assets_col, map, x): Sum of weights does not
## equal 1.
portfolio_returns_tbl
## # A tibble: 60 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31  0.0505 
##  2 2013-02-28  0.0104 
##  3 2013-03-28  0.00924
##  4 2013-04-30 -0.00521
##  5 2013-05-31  0.0210 
##  6 2013-06-28 -0.0117 
##  7 2013-07-31  0.0847 
##  8 2013-08-30  0.00496
##  9 2013-09-30  0.0453 
## 10 2013-10-31  0.0392 
## # ℹ 50 more rows

5 Calculate CAPM Beta

5.1 Get market returns

# Get market returns
market_returns_tbl <- tq_get("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

# Combine market returns with portfolio returns
portfolio_market_returns_tbl <- portfolio_returns_tbl %>%
 
    # Add market returns
    mutate(market_returns = market_returns_tbl %>% pull(returns))

5.3 CAPM Beta

# 3 Calculating CAPM Beta ----
 
# A complete list of functions for performance_fun()
# tq_performance_fun_options()
 
portfolio_market_returns_tbl %>%
 
    tq_performance(Ra = 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

# Figure 8.2 Scatter with regression line from ggplot ----
 
portfolio_market_returns_tbl %>%
 
    ggplot(aes(market_returns, returns)) +
    geom_point(color = "cornflowerblue") +
 
    geom_smooth(method = "lm", se = FALSE,
                size = 1.5, color = tidyquant::palette_light()[3]) +
 
    labs(x = "market returns",
         y = "portfolio returns")
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## `geom_smooth()` using formula = 'y ~ x'

Actual versus fitted returns

# Figure 8.5 Actual versus fitted returns ----
 
portfolio_market_returns_tbl %>%
 
    # Run regression
    lm(returns ~ market_returns, data = .) %>%
 
    # Get fitted
    broom::augment() %>%
 
    # Add date %>%
    mutate(date = portfolio_market_returns_tbl$date) %>%
 
    # Transform data to long format
    pivot_longer(cols = c(returns, .fitted),
                 names_to = "type",
                 values_to = "returns") %>%
 
    # Plot
    ggplot(aes(date, returns, color = type)) +
    geom_line()

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