# 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("WMT", "AAPL", "NKE", "NFLX", "MSFT")
prices <- tq_get(x    = symbols, 
                 from = "2012-12-31",
                 to   = "2024-11-24")

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" "MSFT" "NFLX" "NKE"  "WMT"
# weights
weights <- c(0.25, 0.25, 0.2, 0.2, 0.1)
weights
## [1] 0.25 0.25 0.20 0.20 0.10
w_tbl <- tibble(symbols,weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AAPL       0.25
## 2 MSFT       0.25
## 3 NFLX       0.2 
## 4 NKE        0.2 
## 5 WMT        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: 143 × 2
##    date       returns
##    <date>       <dbl>
##  1 2013-01-31  0.0955
##  2 2013-02-28  0.0282
##  3 2013-03-28  0.0314
##  4 2013-04-30  0.0818
##  5 2013-05-31  0.0210
##  6 2013-06-28 -0.0420
##  7 2013-07-31  0.0442
##  8 2013-08-30  0.0581
##  9 2013-09-30  0.0411
## 10 2013-10-31  0.0590
## # ℹ 133 more rows

5 Calculate CAPM Beta

5.1 Get market returns

market_returns_tbl <- tq_get(x    = "WMT", 
                 from = "2012-12-31",
                 to   = "2024-11-13") %>%
    
    # 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       0.382

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, 
                linewidth = 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?

Based off of the plot created, there is not a strong linear relationship between the two variables, we know this because the dots on the plot are all over the place and not showing any relationship to the line created on the plot (Beta coefficient). I find it a little suprising that there is not more of a linear relationship with the beta coeffiecient line, as “WMT” makes up 25% of the portfolio so I wouldve expected more points to be closer, but I’m assumming the outliers are from some of the other stocks returns.