Apply it to your data 10
How sensitive is your portfolio to the market?
Daniel Lee
2022-09-19
# 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("AMZN", "MSFT", "HD", "WMT")
prices <- tq_get(x = symbols,
get = "stock.prices",
from = "2012-12-31",
to = "2022-11-01")
prices## # A tibble: 9,908 × 8
## symbol date open high low close volume adjusted
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 AMZN 2012-12-31 12.2 12.6 12.1 12.5 68380000 12.5
## 2 AMZN 2013-01-02 12.8 12.9 12.7 12.9 65420000 12.9
## 3 AMZN 2013-01-03 12.9 13.0 12.8 12.9 55018000 12.9
## 4 AMZN 2013-01-04 12.9 13.0 12.8 13.0 37484000 13.0
## 5 AMZN 2013-01-07 13.1 13.5 13.1 13.4 98200000 13.4
## 6 AMZN 2013-01-08 13.4 13.4 13.2 13.3 60214000 13.3
## 7 AMZN 2013-01-09 13.4 13.5 13.3 13.3 45312000 13.3
## 8 AMZN 2013-01-10 13.4 13.4 13.1 13.3 57268000 13.3
## 9 AMZN 2013-01-11 13.3 13.4 13.2 13.4 48266000 13.4
## 10 AMZN 2013-01-14 13.4 13.7 13.4 13.6 85500000 13.6
## # … with 9,898 more rows2 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: 472 × 3
## asset date returns
## <chr> <date> <dbl>
## 1 AMZN 2013-01-31 0.0567
## 2 AMZN 2013-02-28 -0.00464
## 3 AMZN 2013-03-28 0.00837
## 4 AMZN 2013-04-30 -0.0488
## 5 AMZN 2013-05-31 0.0589
## 6 AMZN 2013-06-28 0.0311
## 7 AMZN 2013-07-31 0.0813
## 8 AMZN 2013-08-30 -0.0696
## 9 AMZN 2013-09-30 0.107
## 10 AMZN 2013-10-31 0.152
## # … with 462 more rows3 Assign a weight to each asset (change the weigting scheme)
# symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols## [1] "AMZN" "HD" "MSFT" "WMT"# weights
weights <- c(0.30, 0.30, 0.15, 0.25)
weights## [1] 0.30 0.30 0.15 0.25w_tbl <- tibble(symbols, weights)
w_tbl## # A tibble: 4 × 2
## symbols weights
## <chr> <dbl>
## 1 AMZN 0.3
## 2 HD 0.3
## 3 MSFT 0.15
## 4 WMT 0.254 Build a 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: 118 × 2
## date returns
## <date> <dbl>
## 1 2013-01-31 0.0510
## 2 2013-02-28 0.0117
## 3 2013-03-28 0.0295
## 4 2013-04-30 0.0317
## 5 2013-05-31 0.0397
## 6 2013-06-28 0.00350
## 7 2013-07-31 0.0295
## 8 2013-08-30 -0.0453
## 9 2013-09-30 0.0418
## 10 2013-10-31 0.0722
## # … with 108 more rows5 Calculate CAPM Beta
5.1 Get market returns
market_returns_tbl <- tq_get(x = "SPY",
get = "stock.prices",
from = "2012-12-31",
to = "2022-11-01") %>%
# Convert prices to return
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
period = "monthly",
type = "log",
col_rename = "returns") %>%
slice(-1)
market_returns_tbl## # A tibble: 118 × 2
## date returns
## <date> <dbl>
## 1 2013-01-31 0.0499
## 2 2013-02-28 0.0127
## 3 2013-03-28 0.0373
## 4 2013-04-30 0.0190
## 5 2013-05-31 0.0233
## 6 2013-06-28 -0.0134
## 7 2013-07-31 0.0504
## 8 2013-08-30 -0.0305
## 9 2013-09-30 0.0312
## 10 2013-10-31 0.0453
## # … with 108 more rows5.2 Join returns
portfolio_market_returns_tbl <- left_join(market_returns_tbl,
portfolio_returns_tbl,
by = "date") %>%
set_names("date", "market_returns", "portfolio_returns")
portfolio_market_returns_tbl## # A tibble: 118 × 3
## date market_returns portfolio_returns
## <date> <dbl> <dbl>
## 1 2013-01-31 0.0499 0.0510
## 2 2013-02-28 0.0127 0.0117
## 3 2013-03-28 0.0373 0.0295
## 4 2013-04-30 0.0190 0.0317
## 5 2013-05-31 0.0233 0.0397
## 6 2013-06-28 -0.0134 0.00350
## 7 2013-07-31 0.0504 0.0295
## 8 2013-08-30 -0.0305 -0.0453
## 9 2013-09-30 0.0312 0.0418
## 10 2013-10-31 0.0453 0.0722
## # … with 108 more rows5.3 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 0.927portfolio_market_returns_tbl## # A tibble: 118 × 3
## date market_returns portfolio_returns
## <date> <dbl> <dbl>
## 1 2013-01-31 0.0499 0.0510
## 2 2013-02-28 0.0127 0.0117
## 3 2013-03-28 0.0373 0.0295
## 4 2013-04-30 0.0190 0.0317
## 5 2013-05-31 0.0233 0.0397
## 6 2013-06-28 -0.0134 0.00350
## 7 2013-07-31 0.0504 0.0295
## 8 2013-08-30 -0.0305 -0.0453
## 9 2013-09-30 0.0312 0.0418
## 10 2013-10-31 0.0453 0.0722
## # … with 108 more rows6 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 = "purple") +
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 Portfolio is very similar to the market, The beta coefficient I calculated is 0.927, which is very close to the market beta of 1.00. The plot confirms this because there is a strong linear relationship between my portfolio and the market. The relationship is a one to one relationship, which means when the market return is 10%, my portfolio return will also be 10%.