# 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", "GOOG","MSFT", "AAPL")
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] "AAPL" "GOOG" "MSFT" "TSLA"
# weights
weights <- c(0.25, 0.25, 0.25, 0.25)
weights
## [1] 0.25 0.25 0.25 0.25
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 4 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AAPL       0.25
## 2 GOOG       0.25
## 3 MSFT       0.25
## 4 TSLA       0.25

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")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2013-01-31           0.00997
##  2 2013-02-28          -0.00509
##  3 2013-03-28           0.0267 
##  4 2013-04-30           0.134  
##  5 2013-05-31           0.183  
##  6 2013-06-28          -0.00804
##  7 2013-07-31           0.0707 
##  8 2013-08-30           0.0795 
##  9 2013-09-30           0.0358 
## 10 2013-10-31           0.0317 
## # … with 50 more rows

5 Calculate CAPM Beta

market_returns_tbl <- tq_get(x    = "^IXIC",
                 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)

5.1 Get market returns

5.2 Join returns

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        1.06

6 Plot: Scatter with regression line

portfolio_market_returns_tbl %>%
    
    ggplot(aes(x = market_returns,
               y = portfolio_returns)) +
    geom_point(color = "violet") +
    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?

I used the NASDAQ Index “^IXIC” as my market index because my portfolio are all within the technology/communication services sectors of the market. My portfolios beta is 1.06 which translates to 6% more volatilie than that of the market average of 1.0 . This is not surprising given the stocks in my portfolio align with that of the NASDAQ and how it moves. The plot proves my beta calculations as the data points to conform to that of the regression line especially considering their trend. Given there are some outliers to the generally positive trend, which easily can explain the 6% more volatility, as the tech sectors tend to be more volatile and can produce larger swings in returns. Overall, my portfolio tends to follow the market, but sees a little more volatility and is more sensitive because of specific stocks in my portfolio, such as TSLA.