# 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", "AAPL", "NFLX", "MSFT", "TSLA")
prices <- tq_get (x = symbols,
                  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
symbol <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "NVDA" "AAPL" "NFLX" "MSFT" "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 NVDA       0.25
## 2 AAPL       0.25
## 3 NFLX       0.2 
## 4 MSFT       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  0.0926
##  2 2013-02-28  0.0258
##  3 2013-03-28  0.0195
##  4 2013-04-30  0.109 
##  5 2013-05-31  0.0998
##  6 2013-06-28 -0.0456
##  7 2013-07-31  0.0755
##  8 2013-08-30  0.0906
##  9 2013-09-30  0.0378
## 10 2013-10-31  0.0188
## # 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        1.10

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?

My Beta is 1.10 when compared to SPY. My portfolio doesn’t have a strong correlation with the market. The purple dots would be closer to the green line if they had a strong correlation with the market, instead they are very scattered. The plot does confirm the beta coefficient I found. The upward 45 degree slope is a good indicator for that.