ETF Portfolio Returns Data
etf_returns.RdDaily log returns (in percent) for a diversified ETF portfolio covering major asset classes. This data is used in the portfolio optimization vignette to demonstrate the tsbs bootstrap methods.
Format
A matrix with approximately 1,750 rows (trading days) and 5 columns:
- SPY
S&P 500 ETF - US large-cap equity
- EFA
iShares MSCI EAFE ETF - International developed markets equity
- BND
Vanguard Total Bond Market ETF - US aggregate bonds
- GLD
SPDR Gold Shares ETF - Gold
- VNQ
Vanguard Real Estate ETF - US REITs
The matrix has the following attributes:
- dates
Date vector of trading days
- symbols
Character vector of ticker symbols
- symbol_names
Character vector of asset class descriptions
- source
Data source (Yahoo Finance)
- download_date
Date when data was downloaded
- description
Brief description of the dataset
Details
Returns are computed as \(r_t = 100 \times \log(P_t / P_{t-1})\) where \(P_t\) is the adjusted closing price. The data spans from January 2018 to December 2024, covering various market conditions including the COVID-19 crash and recovery, the 2022 bear market, and subsequent rally.
See also
etf_prices for the corresponding price data.
Examples
# Load the data
data(etf_returns)
# Check dimensions
dim(etf_returns)
#> [1] 1759 5
# View date range
dates <- attr(etf_returns, "dates")
cat("Date range:", as.character(dates[1]), "to",
as.character(dates[length(dates)]), "\n")
#> Date range: 2018-01-03 to 2024-12-30
# Compute annualized statistics
ann_return <- colMeans(etf_returns) * 252
ann_vol <- apply(etf_returns, 2, sd) * sqrt(252)
print(round(cbind(Return = ann_return, Vol = ann_vol), 2))
#> Return Vol
#> SPY 12.84 19.54
#> EFA 3.90 18.27
#> BND 1.06 6.16
#> GLD 9.37 14.33
#> VNQ 4.79 23.12
# Correlation matrix
print(round(cor(etf_returns), 2))
#> SPY EFA BND GLD VNQ
#> SPY 1.00 0.87 0.16 0.11 0.76
#> EFA 0.87 1.00 0.20 0.21 0.71
#> BND 0.16 0.20 1.00 0.36 0.29
#> GLD 0.11 0.21 0.36 1.00 0.16
#> VNQ 0.76 0.71 0.29 0.16 1.00