# 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", "NVDA", "F", "WMT", "MU")

prices <- tq_get(x = symbols,
                 get = "stockprices",
                 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" "F"    "MU"   "NVDA" "WMT"
# weights
weights <- c(0.2, 0.2, 0.2, 0.2, 0.2)
weights 
## [1] 0.2 0.2 0.2 0.2 0.2
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AAPL        0.2
## 2 F           0.2
## 3 MU          0.2
## 4 NVDA        0.2
## 5 WMT         0.2

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 = "quarters",
               col_rename = "returns")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date       returns
##    <date>       <dbl>
##  1 2013-01-31 0.0105 
##  2 2013-02-28 0.0243 
##  3 2013-03-28 0.0673 
##  4 2013-04-30 0.0186 
##  5 2013-05-31 0.0781 
##  6 2013-06-28 0.00921
##  7 2013-07-31 0.0439 
##  8 2013-08-30 0.00627
##  9 2013-09-30 0.0618 
## 10 2013-10-31 0.0273 
## # ℹ 50 more rows

5 Calculate CAPM Beta

5.1 Get market returns

market_returns_tbl <- tq_get(x = "SPY",
                 get = "stockprices",
                 from = "2012-12-31",
                 to = "2017-12-31") %>%

  # Convert prices to returns
  tq_transmute(select = adjusted, 
               mutate_fun = periodReturn, 
               period = "monthly", 
               type = "log", 
               col_name = "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        1.16

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 is fairly sensitive to the market because it includes stocks like AAPL, NVDA, and MU. The plot shows that my portfolio generally moves in the same direction as the market, but sometimes has bigger swings. This supports the beta coefficient and shows that my portfolio has slightly higher market risk.