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("TSLA", "MSFT", "GOOG")

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

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 <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "GOOG" "MSFT" "TSLA"
weights <- c(0.35, 0.35, 0.30)
weights
## [1] 0.35 0.35 0.30
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 3 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 GOOG       0.35
## 2 MSFT       0.35
## 3 TSLA       0.3

4 Build a portfolio

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

portfolio_returns_tbl
## # A tibble: 143 × 2
##    date       returns
##    <date>       <dbl>
##  1 2013-01-31 0.0633 
##  2 2013-02-28 0.00555
##  3 2013-03-28 0.0322 
##  4 2013-04-30 0.170  
##  5 2013-05-31 0.218  
##  6 2013-06-28 0.0281 
##  7 2013-07-31 0.0416 
##  8 2013-08-30 0.0717 
##  9 2013-09-30 0.0509 
## 10 2013-10-31 0.0217 
## # ℹ 133 more rows

5 Calculate CAPM Beta

5.1 Get market returns

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

   
    tq_transmute(select     = adjusted, 
                 mutate_fun = periodReturn, 
                 period     = "monthly",
                 type       = "log", 
                 col_rename = "returns") %>%
    
    slice(-1)

5.2 Join returns

portfolio_market_returns_tbl <- portfolio_returns_tbl %>%

    mutate(market_returns = market_returns_tbl %>% pull(returns))

5.3 CAPM Beta

portfolio_market_returns_tbl %>%

    tq_performance(Ra = returns,
                   Rb = market_returns,
                   performance_fun = CAPM.beta)
## # A tibble: 1 × 1
##   CAPM.beta.1
##         <dbl>
## 1        1.17

6 Plot: Scatter with regression line

portfolio_market_returns_tbl %>%

    ggplot(aes(market_returns, returns)) +
    geom_point(color = "magenta") +

    geom_smooth(method = "lm", se = FALSE,
                size = 1.5, color = tidyquant::palette_light()[3]) +

    labs(x = "market returns",
         y = "portfolio 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?

My portfolio moves in the same direction as the market. The beta is 1.17 which means it is slightly more volatile than the market, so when the market is up, my portfolio also goes up but by around 20% more.The plot confirms my beta because it is upward sloping. My data points are are a little further from my line indicating that the relationship between the two may not be as close as it could be.