# 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", "MSFT", "TSLA", "AMS")
prices <- tq_get(x    = symbols, 
                 get  = "stock.prices",
                 from = "2012-12-31",
                 to   = "2023-12-31")

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"))

asset_returns_tbl
## # A tibble: 528 × 3
##    asset date       returns
##    <chr> <date>       <dbl>
##  1 AMS   2013-01-31 -0.193 
##  2 AMS   2013-02-28 -0.0690
##  3 AMS   2013-03-28 -0.0588
##  4 AMS   2013-04-30 -0.182 
##  5 AMS   2013-05-31  0.217 
##  6 AMS   2013-06-28  0.0661
##  7 AMS   2013-07-31  0.291 
##  8 AMS   2013-08-30 -0.0137
##  9 AMS   2013-09-30 -0.121 
## 10 AMS   2013-10-31 -0.0480
## # ℹ 518 more rows

3 Assign a weight to each asset (change the weigting scheme)

# symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "AMS"  "MSFT" "NVDA" "TSLA"
# weights
weights <- c(0.25, 0.25, 0.25, 0.25)
weights
## [1] 0.25 0.25 0.25 0.25
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 4 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AMS        0.25
## 2 MSFT       0.25
## 3 NVDA       0.25
## 4 TSLA       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: 132 × 2
##    date       returns
##    <date>       <dbl>
##  1 2013-01-31 -0.0160
##  2 2013-02-28 -0.0210
##  3 2013-03-28  0.0169
##  4 2013-04-30  0.0971
##  5 2013-05-31  0.231 
##  6 2013-06-28  0.0298
##  7 2013-07-31  0.115 
##  8 2013-08-30  0.0743
##  9 2013-09-30  0.0158
## 10 2013-10-31 -0.0500
## # ℹ 122 more rows

5 Calculate CAPM Beta

5.1 Get market returns

market_return_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_return_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       0.871

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")

Answer

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 scatter plot of my portfolio is showing returns against market returns and helps illustrate the beta coefficient I calculated at 0.871, indicating my selected portfolio might be less volatile than the market. Also The positive slope of the trend line in my plot confirms this beta value, that is demonstrating that my portfolio generally moves in the same direction as the market but with less intensity. The data points of my portfolio is closely aligned with the trend line, validate the beta, showing that my portfolio might experiences milder fluctuations compared to broader market movements. Overall, I would say that the plot and my beta calculation suggest that my portfolio maintains moderate sensitivity to market changes, but offering stability during periods of market volatility.