# Load packages
library(tidyverse)
library(tidyquant)
# Choose stocks
symbols <- c("SPY", "EFA", "IJS", "EEM", "AGG")
prices <- tq_get(x = symbols,
get = "stock.prices",
from = "2012-01-01",
to = "2017-01-01")
asset_returns_tbl<- prices %>%
group_by(symbol) %>%
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
period = "quarterly",
type = "log"
) %>%
set_names(c("asset", "date", "returns"))
asset_returns_tbl %>%
ggplot(aes(x = returns)) +
geom_density(aes(color = asset), alpha = 1) +
geom_histogram(aes(fill = asset), show.legend = FALSE, alpha = 0.3, binwidth = 0.001) +
facet_wrap(~asset, ncol = 1) +
labs(
title = "Distribution of Monthly Returns, 2012-2016",
y = "frequency",
x = "rate of returns",
caption = "A typical monthly return is higher for SPY and IJS than for AGG, EEM, and EFA."
)
Between 2012 and 2016, IJS (small-cap value ETF) had the highest typical monthly return, followed closely by SPY (S&P 500 ETF).
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