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

2 Convert prices to returns

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

#Symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "AAPL" "AMZN" "MSFT" "NVDA" "TSLA"
#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 AMZN       0.25
## 3 MSFT       0.2 
## 4 NVDA       0.2 
## 5 TSLA       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, 
                 reabalance_on = "months",
                 col_rename = "returns")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31 -0.00905
##  2 2013-02-28 -0.00289
##  3 2013-03-28  0.0204 
##  4 2013-04-30  0.0720 
##  5 2013-05-31  0.125  
##  6 2013-06-28 -0.00475
##  7 2013-07-31  0.0748 
##  8 2013-08-30  0.0683 
##  9 2013-09-30  0.0645 
## 10 2013-10-31 -0.00562
## # 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 return
    
    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        1.09

6 Plot

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") +
    
    # To set the limits (zoom window) for both the X and Y axes, 
    # forcing the plot to display only the range from 0 to 0.1 (or 0% to 10%) on both axes
    coord_cartesian(xlim = c(0,0.1), ylim = c(0,0.1))

How sensitive is your portfolio to the market? Discuss in terms of the beta coefficient. Does the plot confirm the beta coefficient you calculated?

According to my calculated beta coefficient of 1.09, my portfolio is slightly more sensitive to market movements than the overall market. On average, for every 1% change in the market my portfolio is expected to move 1.9%. This shows that while my portfolio is slightly riskier than the S&P500 but not excessively. The line chart supports my beta because the slope of the line is slightly higher than 45 degrees which means that the beta is >1, confirming the value from before.