# 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("AAPL", "MSFT", "META", "TSLA", "NFLX")

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" "META" "MSFT" "NFLX" "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 META       0.25
## 3 MSFT       0.2 
## 4 NFLX       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, 
                 rebalance_on = "months", 
                 col_rename = "returns")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date         returns
##    <date>         <dbl>
##  1 2013-01-31  0.131   
##  2 2013-02-28 -0.0158  
##  3 2013-03-28  0.000339
##  4 2013-04-30  0.112   
##  5 2013-05-31  0.0533  
##  6 2013-06-28 -0.0327  
##  7 2013-07-31  0.166   
##  8 2013-08-30  0.113   
##  9 2013-09-30  0.0734  
## 10 2013-10-31  0.0247  
## # … with 50 more rows

5 Calculate CAPM Beta

5.1 Get market returns

market_returns_tbl <- tq_get(x = "NDX", 
                  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        1.14

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?

My portfolio shows a Beta of 1.14, which indicated slightly more volatility to be expected than the NASDAQ 100. So when the market goes up 3% my portfolio generally speaking goes up 3% x 1.14, which is 3.42%. Vice versa when the market goes down. The NASDAQ index has returned an average annual return of 15.59% between July 2007 to September 2022 and proves great returns over time. By just looking at the graph, It is a bit difficult to identify my beta coefficient of the portfolio. From was I can observe, I see a lot more dots/returns under the regression which shows lower portfolio returns. On the other hand, some portfolio returns above the regression line shows very good returns which evens out the average and therefore I believe the plot confirms the beta coefficient.