Dimensional US Large Cap Value ETF
Top 10 Holdings
What is DFLV?
The investment objective of the Dimensional US Large Cap Value ETF is to achieve long-term capital appreciation. The Portfolio is designed to purchase a broad and diverse group of readily marketable securities of large U.S. companies that the Advisor determines to be value stocks. A company market capitalization is the number of its shares outstanding times its price per share.
ETFs related toDFLV
ETFs correlated to DFLV include DFUV, AVLV, JAVA
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.
Opus, Investing for the Long-Term
Create your own algorithmic trading strategy with DFLV using Composer
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.