# 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", "PG", "MSFT", "SMCI", "LME")

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 weighting scheme)

# symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "LME"  "MSFT" "NVDA" "PG"   "SMCI"
# 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 LME        0.25
## 2 MSFT       0.25
## 3 NVDA       0.2 
## 4 PG         0.2 
## 5 SMCI       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: 63 × 2
##    date       returns
##    <date>       <dbl>
##  1 2013-01-31  0.132 
##  2 2013-02-28 -0.0286
##  3 2013-03-28 -0.0370
##  4 2013-04-30  0.0809
##  5 2013-05-31 -0.0685
##  6 2013-06-28  0.0509
##  7 2013-07-31  0.151 
##  8 2013-08-30 -0.0397
##  9 2013-09-30 -0.0294
## 10 2013-10-31 -0.0731
## # ℹ 53 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        1.93

6 Plot: Scatter with regression line

portfolio_market_returns_tbl %>%
    
    ggplot(aes(x = market_returns,
               y = portfolio_returns)) +
    geom_point(color = "magenta") +
    geom_smooth(method = "lm", se = FALSE, 
                size = 1, 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?

the graph looks like my portfolio is semi-volatile with the market. The portfolio performs well when the market performs well. When the market moves 1% my portfolio moves 1.93% on average. The variation in my portfolio is tight range and more likely to have the returns of 1.93%.