# 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("NKE", "ADDYY", "SKX", "UAA")

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] "ADDYY" "NKE"   "SKX"   "UAA"
# weights
weights <- c(0.4, 0.2, 0.25, 0.15)
weights
## [1] 0.40 0.20 0.25 0.15
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 4 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 ADDYY      0.4 
## 2 NKE        0.2 
## 3 SKX        0.25
## 4 UAA        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  0.0385
##  2 2013-02-28  0.0150
##  3 2013-03-28  0.0760
##  4 2013-04-30  0.0284
##  5 2013-05-31  0.0519
##  6 2013-06-28  0.0120
##  7 2013-07-31  0.0604
##  8 2013-08-30  0.0204
##  9 2013-09-30  0.0542
## 10 2013-10-31  0.0157
## # ℹ 50 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 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       0.592

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 beta coefficient of the portfolio is 1.10, which means the portfolio is 10% more volatile than the overall market. For every 1% change in the market, the portfolio tends to change by 1.10%. The scatter plot of portfolio returns against market returns confirms this relationship: the regression line has a positive slope greater than 1, indicating a high sensitivity to market movements. Therefore, the plot supports the beta value and confirms that this portfolio is relatively aggressive in risk profile compared to the market.