# 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("COST", "LOW", "HD", "KR")

prices <- tq_get(x    = symbols,
                 get  = "stock.prices",
                 from = "2012-12-31")

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"))

asset_returns_tbl
## # A tibble: 668 × 3
##    asset date        returns
##    <chr> <date>        <dbl>
##  1 COST  2013-01-31  0.0359 
##  2 COST  2013-02-28 -0.00765
##  3 COST  2013-03-28  0.0465 
##  4 COST  2013-04-30  0.0216 
##  5 COST  2013-05-31  0.0138 
##  6 COST  2013-06-28  0.00854
##  7 COST  2013-07-31  0.0601 
##  8 COST  2013-08-30 -0.0458 
##  9 COST  2013-09-30  0.0291 
## 10 COST  2013-10-31  0.0243 
## # ℹ 658 more rows

3 Assign a weight to each asset (change the weighting scheme)

# symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "COST" "HD"   "KR"   "LOW"
# weights
weights <- c(0.35, 0.25, 0.25, 0.15)
weights
## [1] 0.35 0.25 0.25 0.15
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 4 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 COST       0.35
## 2 HD         0.25
## 3 KR         0.25
## 4 LOW        0.15

4 Build a portfolio

portfolio_retuns_tbl <- asset_returns_tbl %>%
    
    tq_portfolio(assets_col   = asset,
                 returns_col  = returns,
                 weights      = w_tbl,
                 rebalance_on = "months",
                 col_rename = "returns")

portfolio_retuns_tbl
## # A tibble: 167 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31  0.0594 
##  2 2013-02-28  0.0176 
##  3 2013-03-28  0.0529 
##  4 2013-04-30  0.0318 
##  5 2013-05-31  0.0319 
##  6 2013-06-28  0.00242
##  7 2013-07-31  0.0716 
##  8 2013-08-30 -0.0434 
##  9 2013-09-30  0.0461 
## 10 2013-10-31  0.0374 
## # ℹ 157 more rows

5 Calculate CAPM Beta

5.1 Get market returns

market_returns_tbl <- tq_get(x    = "SPY",
                 get  = "stock.prices",
                 from = "2012-12-31") %>%
    
    # 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_retuns_tbl, "date") %>%
    
    set_names("date", "market_returns", "portfolio_returns")

portfolio_market_returns_tbl
## # A tibble: 167 × 3
##    date       market_returns portfolio_returns
##    <date>              <dbl>             <dbl>
##  1 2013-01-31         0.0499           0.0594 
##  2 2013-02-28         0.0127           0.0176 
##  3 2013-03-28         0.0373           0.0529 
##  4 2013-04-30         0.0190           0.0318 
##  5 2013-05-31         0.0233           0.0319 
##  6 2013-06-28        -0.0134           0.00242
##  7 2013-07-31         0.0504           0.0716 
##  8 2013-08-30        -0.0305          -0.0434 
##  9 2013-09-30         0.0312           0.0461 
## 10 2013-10-31         0.0453           0.0374 
## # ℹ 157 more rows

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       0.796

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?