Invesco Exchange-Traded Self-Indexed Fund Trust Invesco BulletShares 2030 Municipal Bond ETF
Top 10 Holdings
What is BSMU?
The Invesco BulletShares 2030 Municipal Bond ETF (Fund) is based on the Invesco BulletShares USD Municipal Bond 2030 Index (Index). The Fund will invest at least 80% of its total assets in municipal bonds that comprise the index. The Index seeks to measure the performance of a portfolio of US dollar-denominated, issued by US state, state agencies, or local governments with effective maturities in 2030. The Fund does not purchase all of the securities in the Index; instead, the Fund utilizes a "sampling" methodology to seek to achieve its investment objective. The Fund and the Index are rebalanced monthly. The Fund has a designated year of maturity of 2030 and will terminate on or about Dec. 15, 2030.
ETFs related toBSMU
ETFs correlated to BSMU include SCMB, NYF, CGMU
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.
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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.