iPath Bloomberg Lead Subindex Total Return ETN due June 24, 2038
What is LD?
The iPath Dow Jones-UBS Lead Subindex Total Return ETN is designed to provide investors with exposure to the Dow Jones-UBS Lead Subindex Total Return. The Dow Jones-UBS Lead Subindex Total Return (the "Index") reflects the returns that are potentially available through an unleveraged investment in the futures contracts on lead. The Index currently consists of one futures contract on the commodity of lead which is included in the Dow Jones-UBS Commodity Index Total Return.
ETFs related toLD
ETFs correlated to LD include GAPR, MDLV, MAYW
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.