# Load packages

# Core
library(tidyverse)
library(tidyquant)

Goal

Calculate and visualize your portfolio’s beta.

Choose your stocks and the baseline market.

“MSFT”, “AAPL”, “F”, “JPM”, “SBUX” Baseline “SPY” from 2012-12-31 to present

1 Import stock prices

symbols <- c("MSFT", "AAPL", "F", "JPM", "SBUX")

prices <- tq_get(x    = symbols,
                 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"))

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

# symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "AAPL" "F"    "JPM"  "MSFT" "SBUX"
# 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)

4 Build a portfolio

# ?tq_portfolio

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

portfolio_returns_tbl
## # A tibble: 143 × 2
##    date         returns
##    <date>         <dbl>
##  1 2013-01-31 -0.000198
##  2 2013-02-28 -0.00229 
##  3 2013-03-28  0.0162  
##  4 2013-04-30  0.0584  
##  5 2013-05-31  0.0744  
##  6 2013-06-28 -0.0293  
##  7 2013-07-31  0.0580  
##  8 2013-08-30 -0.00248 
##  9 2013-09-30  0.0252  
## 10 2013-10-31  0.0460  
## # ℹ 133 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 return
    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        1.08

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 portfolio’s sensitivity to the market, as represented by the beta coefficient, is 1.08. This indicates that the portfolio’s returns tend to move 1.08 times the magnitude of the market’s returns, suggesting the portfolio is highly correlated with the market. The plot confirms this high positive correlation, as the data points closely follow the upward-sloping trend line.