# 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("AAPL", "MSFT", "AMZN", "NVDA", "GOOG")
prices <- tq_get(x    = symbols, 
                 get  = "stock.prices",
                 from = "2012-12-31", 
                 to   = "2017-12-31")

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" "AMZN" "GOOG" "MSFT" "NVDA"
# weights
weights <- c(0.25, 0.20, 0.20, 0.20, 0.15)
weights
## [1] 0.25 0.20 0.20 0.20 0.15
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AAPL       0.25
## 2 AMZN       0.2 
## 3 GOOG       0.2 
## 4 MSFT       0.2 
## 5 NVDA       0.15

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: 60 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31 -0.00888
##  2 2013-02-28  0.0143 
##  3 2013-03-28  0.00837
##  4 2013-04-30  0.0376 
##  5 2013-05-31  0.0485 
##  6 2013-06-28 -0.0298 
##  7 2013-07-31  0.0389 
##  8 2013-08-30  0.0117 
##  9 2013-09-30  0.0300 
## 10 2013-10-31  0.0948 
## # ℹ 50 more rows

5 Calculate CAPM Beta

5.1 Get Market Returns

market_returns_tbl <- tq_get(x    = "^IXIC", 
                 get  = "stock.prices",
                 from = "2012-12-31", 
                 to   = Sys.Date()) %>% 
  # 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        1.11

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 has a positive beta coefficient of 1.11, meaning it generally moves in the same direction as the NASDAQ market. Since the beta is slightly greater than 1, my portfolio is about 11% more sensitive to market movements. For example, a 1% increase in the NASDAQ would be associated with approximately a 1.11% increase in my portfolio, while a 1% decrease would be associated with approximately a 1.11% decrease. This means my portfolio has slightly higher market-related risk than the NASDAQ. The scatterplot supports my calculated beta because the regression line slopes upward, showing a positive linear relationship between market returns and portfolio returns. Most of the data points follow the upward trend, although there is some spread around the line. This suggests a reasonably strong positive linear relationship, but the market does not explain every change in my portfolio’s returns. Overall, my portfolio moves with the market but tends to experience slightly larger movements.