# 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("CRWD", "AMZN", "SHOP","TTD", "NVDA")

prices <- tq_get(x = symbols, 
                 get  = "stock.prices", 
                 from = "2021-01-01")

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
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "AMZN" "CRWD" "NVDA" "SHOP" "TTD"
# weights
weights <- c(0.25, 0.25, 0.2, 0.2, 0.1)

w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AMZN       0.25
## 2 CRWD       0.25
## 3 NVDA       0.2 
## 4 SHOP       0.2 
## 5 TTD        0.1

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: 46 × 2
##    date         returns
##    <date>         <dbl>
##  1 2021-02-26  0.0378  
##  2 2021-03-31 -0.0978  
##  3 2021-04-30  0.110   
##  4 2021-05-28  0.00183 
##  5 2021-06-30  0.149   
##  6 2021-07-30 -0.000161
##  7 2021-08-31  0.0648  
##  8 2021-09-30 -0.0992  
##  9 2021-10-29  0.105   
## 10 2021-11-30  0.0333  
## # ℹ 36 more rows

5 Calculate CAPM Beta

5.1 Get market returns

market_returns_tbl <- tq_get(x = "SPY", 
                 get  = "stock.prices", 
                 from = "2021-01-01") %>%
    
    # Convert prices to returns
    tq_transmute(select     = adjusted,
                 mutate_fun = periodReturn, 
                 period     = "monthly",
                 type       = "log", 
                 col_rename = "returns") %>%
    
    slice(-1)

5.2 Join retuens

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        1.66

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?

My portfolio does follow the market due to the positive beta, but it is more volatile than the market is. My portfolio is very sensitive to the market, my beta coefficient is 1.66, so when the market increases by 10% my portfolio would increases by 16.6%. The plot does confirm this, showing around a 16 percent loss and gain when the market would have a 10 percent loss or gain. My spread of points is not perfect, but is still pretty good. Most points are close to the line, and there are only a few big outliars.