# 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("APPL", "NFLX", "GOOG", "NVDA", "GOLD")

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] "GOOG" "NFLX" "NVDA"
#weights
weights <- c(0.4, 0.35, 0.25)
weights
## [1] 0.40 0.35 0.25
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 3 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 GOOG       0.4 
## 2 NFLX       0.35
## 3 NVDA       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", 
                col_rename   = "returns")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31  0.229  
##  2 2013-02-28  0.0783 
##  3 2013-03-28  0.00205
##  4 2013-04-30  0.0790 
##  5 2013-05-31  0.0518 
##  6 2013-06-28 -0.0276 
##  7 2013-07-31  0.0618 
##  8 2013-08-30  0.0401 
##  9 2013-09-30  0.0567 
## 10 2013-10-31  0.0737 
## # 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 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        1.06

6 Plot: Scatter with regression line

portfolio_market_returns_tbl %>%
    
    ggplot(aes(x = market_returns, 
               y = portfolio_returns)) +
    geom_point() + 
    geom_smooth(method = "lm", se = FALSE,
                size = 1.5, color = tidyquant::palette_light()[3]) +
    
    labs(y = "Portfolio Returns",
         x = "Market Returns")

actual_fitted_long_tbl <- portfolio_market_returns_tbl %>%
    
    # Linear Regression Modle
    lm(portfolio_returns ~ market_returns, data = .) %>%
    
    # Get fitted and actual returns
    broom::augment() %>%
    
    # Add date
    mutate(date = portfolio_market_returns_tbl$date) %>%
    select(date, portfolio_returns, market_returns) %>%
    
    # Transform data to long
    pivot_longer(cols = c(portfolio_returns, market_returns), 
                 names_to = "type", 
                 values_to = "returns")

actual_fitted_long_tbl %>%
    
    ggplot(aes(x = date, y = returns, color = type)) +
    geom_line()

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

The beta coefficient shows that my portfolio is slightly more volatile than the market, meaning it reacts more to market changes. This fits with the mix of high-growth tech stocks and Gold for balance. The scatterplot confirms this, showing a strong positive relationship between market and portfolio returns. calculated.