# 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("TSLA", "NVDA", "LLY", "HD", "UAA")

prices <- tq_get(x    = symbols, 
                 get. = "stock.prices",
                 from = "2012-12-31", 
                 to   = Sys.Date()) # Fetches data until present

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] "HD"   "LLY"  "NVDA" "TSLA" "UAA"
# 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 HD         0.25
## 2 LLY        0.25
## 3 NVDA       0.2 
## 4 TSLA       0.2 
## 5 UAA        0.1

4 Build a 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: 155 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31  0.0660 
##  2 2013-02-28  0.00224
##  3 2013-03-28  0.0389 
##  4 2013-04-30  0.102  
##  5 2013-05-31  0.147  
##  6 2013-06-28 -0.0134 
##  7 2013-07-31  0.0866 
##  8 2013-08-30  0.0384 
##  9 2013-09-30  0.0472 
## 10 2013-10-31 -0.0366 
## # ℹ 145 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 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       0.755

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]) +
    
    # Labeling
    labs(x = "Portfolio Returns", 
         y = "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?

Beta Coefficient

The calculated Beta coefficient of .755, this shows that my portfolio is slightly less volatile than the market, in this case, the S&P500. This means that when the market is moving up or down, my portfolio will receive a little less in returns than what the market is generating.

Scatterplot & Regression Line

The slope of the regression line in the graph represents my Beta coefficient and the sensitivity of the portfolio’s returns to the changing returns of the market. As my slope is a .755, this shows that my portfolio is going to move .755 times whatever the market is moving at, so slightly slower than the market.