# 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", "AMD", "AMZN", "INTC", "AAPL")

prices <- tq_get(x = symbols,
                 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" "AMD"  "AMZN" "INTC" "NVDA"
#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 AMD        0.25
## 3 AMZN       0.2 
## 4 INTC       0.2 
## 5 NVDA       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,
               rebalence_on = "months",
               col_rename = "returns")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31 -0.00352
##  2 2013-02-28 -0.0137 
##  3 2013-03-28  0.0194 
##  4 2013-04-30  0.0434 
##  5 2013-05-31  0.124  
##  6 2013-06-28 -0.0138 
##  7 2013-07-31  0.00425
##  8 2013-08-30 -0.0522 
##  9 2013-09-30  0.0750 
## 10 2013-10-31  0.0180 
## # ℹ 50 more rows

5 Calculate CAPM Beta

5.1 Get Market Returns

market_returns_tbl <- tq_get(x     = "NVDA",
                  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       0.445

6 Plot: Scatter with regression line

Scatterplot of returns 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")

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

There is not a strong linear relationship with the beta coefficient. The scatter plot shows that the dots are very spread out and do not really confer with the line, meaning that the beta coefficient is not confirmed by the plot