# 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

# Choose stocks

symbols <- c("AAPL", "ROKU", "CL=F")

# Using tq_get() ----
prices <- tq_get(x    = symbols,
                 get  = "stock.prices",
                 from = "2012-12-31",
                 to   = "2023-11-07")

2 Convert prices to returns (monthly)

asset_returns_tbl <- prices %>%
  
  # Calculate monthly returns
  group_by(symbol) %>%
  tq_transmute(select     = adjusted,
               mutate_fun = periodReturn,
               period     = "monthly",
               type       = "log") %>%
  slice (-1) %>%
  ungroup() %>%
  
  # rename
  set_names(c("asset", "date", "returns"))

# period_returns = c("yearly", "quarterly", "monthly", "weekly")

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

symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()

w <- c(0.45,
       0.35,
       0.20)

w_tbl <- tibble(symbols, w)

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: 131 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31 -0.0490 
##  2 2013-02-28 -0.0316 
##  3 2013-03-28  0.0204 
##  4 2013-04-30 -0.0137 
##  5 2013-05-31  0.00435
##  6 2013-06-28 -0.0396 
##  7 2013-07-31  0.0889 
##  8 2013-08-30  0.0448 
##  9 2013-09-30 -0.0275 
## 10 2013-10-31  0.0204 
## # ℹ 121 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",
                 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, 
                                  by = "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       0.834

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? My portfolio’s beta is 0.834, which means that it correlates well with the market, and my portfolio moves with the market, but just a little bit less. This also means that my portfolio is a bit less volatile than the market, but only slightly. The linear relationship between my portfolio and the market is a little strong, but not very much, which we can see by studying the plot. If the dots were a bit closer to the regression line, the relationship would be stronger. The plot confirms the beta coefficient I calculated, because we can see that the market returns are a little bit higher than the portfolio returns.