# 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("MSFT", "DPZ", "AAPL")

prices <- tq_get(x = symbols,
                 get = "stock.prices",
                 from = "2020-12-31",
                 to = "2024-12-31")

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" "DPZ"  "MSFT"
#weights
weights <- c(0.34, 0.33, 0.33)
weights
## [1] 0.34 0.33 0.33
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 3 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AAPL       0.34
## 2 DPZ        0.33
## 3 MSFT       0.33

4 Build a portfolio

# ?tq_portfolio

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

portfolio_returns_tbl
## # A tibble: 48 × 2
##    date         returns
##    <date>         <dbl>
##  1 2021-01-29  0.000869
##  2 2021-02-26 -0.0492  
##  3 2021-03-31  0.0278  
##  4 2021-04-30  0.0928  
##  5 2021-05-28 -0.0166  
##  6 2021-06-30  0.0890  
##  7 2021-07-30  0.0773  
##  8 2021-08-31  0.0284  
##  9 2021-09-30 -0.0725  
## 10 2021-10-29  0.0812  
## # ℹ 38 more rows

5 Calculate CAPM Beta

5.1 Get market returns

market_returns_tbl <- tq_get(x   = "SPY",
       get = "stock.prices",
       from = "2020-12-31",
       to = "2024-12-31") %>%
    
    # Conver 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_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        1.17

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")

Line plot of fitted vs actual returns

actual_fitted_long_tbl <- portfolio_market_returns_tbl %>%
    
    # Linear Regression Model
    lm(portfolio_returns ~ market_returns, data = .) %>%
    
    # Get fitted and actual returns
    broom::augment() %>%
    
    # Add date
    mutate(date = portfolio_market_returns_tbl$date) %>%
    select(date, portfolio_returns, .fitted) %>%
    
    # Transform data to long
    pivot_longer(cols = c(portfolio_returns, .fitted),
                 names_to = "type", 
                 values_to = "returns")

actual_fitted_long_tbl %>%
    ggplot(aes(x = date, y = returns, color = type)) +
    geom_line()

How sensitive is your portfolio to the market? Discuss in terms of the beta coefficient. Does the plot confirm the beta coefficient you calculated?

I would say that my portfolio is very sensitive to the market, in terms of the beta coefficient it is far more volatile than the market sitting at 1.17 and that is reflected in some locations throughout the graph. Across multiple points the portfolio drops strictly on its own whilst other times it is in line with the market.