# Load packages
library(moments)
library(ggrepel)
library(scales)
library(stringr)
library(timetk)
library(PerformanceAnalytics)
# 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", "V", "META", "MSFT", "NFLX", "AVGO")

prices <- tq_get(
  x    = symbols,
  get  = "stock.prices",
  from = "2012-12-31",
  to   = "2022-12-31"
)

2 Convert prices to returns (monthly)

returns_tbl <- prices %>%
  group_by(symbol) %>%
  tq_transmute(
    select     = adjusted,
    mutate_fun = periodReturn,
    period     = "monthly",
    type       = "log"
  ) %>%
  ungroup()

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

weights <- c(0.15, 0.15, 0.2, 0.2, 0.15, 0.15)  # Custom weights

4 Build a portfolio

portfolio_returns_tbl <- returns_tbl %>%
  tq_portfolio(
    assets_col  = symbol,
    returns_col = monthly.returns,
    weights     = weights,
    col_rename  = "returns"
  )

5 Calculate CAPM Beta

5.1 Get market returns

market_returns_tbl <- tq_get("^IXIC",
                             from = "2012-12-31",
                             to   = "2022-12-31") %>%
  tq_transmute(
    select     = adjusted,
    mutate_fun = periodReturn,
    period     = "monthly",
    type       = "log"
  ) %>%
  slice(-1) %>%
  rename(returns = monthly.returns)

5.2 Join returns

portfolio_market_returns_tbl <- left_join(
  market_returns_tbl,
  portfolio_returns_tbl,
  by = "date"
) %>%
  rename(
    market_returns    = returns.x,
    portfolio_returns = returns.y
  )

5.3 CAPM Beta

portfolio_xts <- portfolio_market_returns_tbl %>%
  select(date, portfolio_returns) %>%
  tk_xts(date_var = date)

market_xts <- portfolio_market_returns_tbl %>%
  select(date, market_returns) %>%
  tk_xts(date_var = date)

capm_beta <- CAPM.beta(Ra = portfolio_xts, Rb = market_xts)
capm_beta
## [1] 1.251999

6 Plot: Scatter with regression line

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

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 for my portfolio was 1.251999. Since the value is greater than 1, it means my portfolio is more volatile than the NASDAQ market, meaning it tends to mean big market movements. The positive beta confirms the portfolio moves in the same direction as the market. The scatterplot supports this interpretation: most of the data points cluster closely around the regression line, showing a strong linear relationship between market and portfolio returns.