ALPS O'Shares U.S. Quality Dividend ETF
Top 10 Holdings
What is OUSA?
The Fund seeks to track the performance (before fees and expenses) of its target index, the ALPS OShares U.S. Quality Dividend Index (the Target Index ). The Target Index is designed to measure the performance of publicly-listed large-capitalization and mid-capitalization dividend-paying issuers in the United States that meet certain market capitalization, liquidity, high quality, low volatility and dividend yield threshold. The high quality and low volatility requirements are designed to reduce exposure to high dividend equities that have experienced large price declines.
ETFs related toOUSA
ETFs correlated to OUSA include VIG, QUS, FDLO
What is ETF correlation?
Correlation is a measure of the strength of the relationship between two ETFs. It quantifies the degree to which prices of the two ETFs typically move together.
Here, correlation is measured over the past year with the Pearson correlation coefficient (Pearon’s r), which ranges from -1 to 1.
Using ETF correlations in portfolio and strategy construction
ETF correlations can help you create investing strategies and portfolios. Use them to:
- •Build a diversified portfolio from uncorrelated or inversely correlated ETFs with the aim of minimizing portfolio risk.
- •Compare correlated or related ETFs to find one with a lower expense ratio or higher trading volume.
- •Create an investing strategy that hedges an ETF with an uncorrelated or inversely correlated ETF.
We show information directly obtained from our data provider, Xignite. Data shown here is provided by Xignite, an unaffiliated third party. Composer believes the information shown here is reliable, but has not been verified and there is no guarantee that the information is accurate.
We show information based on calculations performed by Composer using data from our provider. Information provided here is based on calculations performed by Composer using data sourced from Xignite, an unaffiliated third party. Composer believes this information is reliable, but has not verified the data and there is no guarantee that the calculations are accurate.