# 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("X", "CMC", "ZEUS", "GOOG", "TSLA")
prices <- tq_get(x  = symbols,
    get = "stock.prices", 
     from = "2012-12-31")

2 Convert prices to returns (monthly)

asset_return_tbl <- prices %>%
    
    group_by(symbol) %>%
    
    tq_transmute(select     = adjusted, 
                 mutate_fun = periodReturn, 
                 period     = "monthly", 
                 type       = "log",
                 col_rename = "returns") %>%
    
    slice(-1) %>%
    
    ungroup() %>%

set_names(c("asset", "date", "returns"))

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

symbols <- asset_return_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "CMC"  "GOOG" "TSLA" "X"    "ZEUS"
weights <- c(0.20, 0.30, 0.25, 0.15, 0.1)
weights
## [1] 0.20 0.30 0.25 0.15 0.10
w_tbl <- tibble(symbols, weights)
w_tbl 
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 CMC        0.2 
## 2 GOOG       0.3 
## 3 TSLA       0.25
## 4 X          0.15
## 5 ZEUS       0.1

4 Build a portfolio

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

portfolio_returns_tbl
## # A tibble: 143 × 2
##    date       returns
##    <date>       <dbl>
##  1 2013-01-31  0.0547
##  2 2013-02-28 -0.0168
##  3 2013-03-28  0.0172
##  4 2013-04-30  0.0538
##  5 2013-05-31  0.199 
##  6 2013-06-28  0.0132
##  7 2013-07-31  0.0809
##  8 2013-08-30  0.0335
##  9 2013-09-30  0.0975
## 10 2013-10-31  0.0443
## # ℹ 133 more rows

5 Calculate CAPM Beta

5.1 Get market returns

market_returns_tbl <- tq_get(x  = "DJI",
            get = "stock.prices", 
           from = "2012-12-31") %>%

 tq_transmute(select     = adjusted, 
                 mutate_fun = periodReturn, 
                 period     = "monthly", 
                 type       = "log",
                 col_rename = "returns") %>%
    
    slice(-1)
    market_returns_tbl
## # A tibble: 105 × 2
##    date       returns
##    <date>       <dbl>
##  1 2013-01-31  0.0561
##  2 2013-02-28  0.0139
##  3 2013-03-28  0.0366
##  4 2013-04-30  0.0178
##  5 2013-05-31  0.0184
##  6 2013-06-28 -0.0137
##  7 2013-07-31  0.0388
##  8 2013-08-30 -0.0455
##  9 2013-09-30  0.0213
## 10 2013-10-31  0.0271
## # ℹ 95 more rows

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")
portfolio_market_returns_tbl
## # A tibble: 105 × 3
##    date       market_returns portfolio_returns
##    <date>              <dbl>             <dbl>
##  1 2013-01-31         0.0561            0.0547
##  2 2013-02-28         0.0139           -0.0168
##  3 2013-03-28         0.0366            0.0172
##  4 2013-04-30         0.0178            0.0538
##  5 2013-05-31         0.0184            0.199 
##  6 2013-06-28        -0.0137            0.0132
##  7 2013-07-31         0.0388            0.0809
##  8 2013-08-30        -0.0455            0.0335
##  9 2013-09-30         0.0213            0.0975
## 10 2013-10-31         0.0271            0.0443
## # ℹ 95 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        1.31

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?

The CAPM beta of this portfolio is 1.14 meaning it is slightly more volatile than the merket. The graph shows this as the regression line is slighltly more weighted to the portfolio returns over the market returns.