Rev. Date April 30, 2020
What is a proxy and how are they used on Venn?
Sometimes an investment does not have a sufficiently long return history to be analyzed on Venn, as most analytics on the platform require 1 year of monthly data for analysis. To enable analysis of investments with shorter return streams, users can opt to append other investments, or “proxies,” that appear to have similar historical return streams to the subject investment.
What is an example of a proxy?
For example, say a user invested in Manager A, a long-only U.S. Large Cap bottom-up stock picker. The Manager has actual returns dating back to 2010. However, the user would like to run analysis on their entire portfolio, including Manager A, going back to 2000. In order to do this, the user could use the S&P 500 Index to proxy the returns of Manager A from 2000-2009.
What are the limitations of a proxy?
Proxies are merely estimates of an investment’s return history and will be less accurate than using an investment’s actual returns. All else equal, the less return history there is for an uploaded investment, the more difficult it will be to find an accurate proxy.
How can I select a proxy for an investment on Venn?
Users can select proxies for investments in several places throughout Venn, including:
- Library: mouse over the Factor Risk column and click “Add a Proxy”
- Tearsheet: click “Add a Proxy” at the top of the page
When clicking to add a proxy on an investment, users may be prompted to select from Venn’s identified “potential proxies” (if Venn can find a potential match for the investment using the methodology outlined below). Alternatively, users may search for a proxy based on their own preferences. After a proxy has been set, users can view the proxy date range as well as its correlation to the subject investment for the overlapping period.
Users may change or remove proxies at any time through the Investments or Portfolio Configuration pages. Venn will not automatically change a proxy on users’ behalf, even if the subject investment’s return history becomes sufficient for Venn analysis.
What types of investments can be used as proxies on Venn?
Investments that can be used as proxies include pre-populated investments on Venn such as indices, ETFs, and mutual funds. Additionally, user uploaded investments can be used as proxies, although such investments will not be in the pool that Venn uses to suggest potential proxies.
How does Venn help users identify potential proxies?
Venn identifies potential proxies, which users may opt to use, for subject investments. Potential proxies seek to suggest investments whose return history is pre-populated on Venn, that have returns similar to those of the subject investment, and that might be suitable for use as a proxy.
Please note that Venn will not automatically select a potential proxy for any given investment. Rather, Venn will seek to identify potential proxies, and the user can opt to select one of the identified potential proxies, select a different investment whose returns are available on Venn (including investments uploaded by the user) as a proxy, or proceed without a proxy. Additionally, Venn will not alert users in the event that the correlation between a selected proxy and the subject investment drops, making it a less accurate proxy. However, users can view the correlation between the investment and selected proxy at any time in the Library by hovering over the “Proxy” column of the investment.
What is the minimum number of data points for a subject investment that Venn requires to identify a potential proxy?
Venn requires at least 6 data points to identify a potential proxy.
How does Venn approach identifying potential proxies?
Venn identifies potential proxies by conducting a search through its library of pre-populated ETF and mutual fund investments and finding investments that have high match scores with the subject investment. The match score is a proprietary measure between 0% and 100% that is calculated using cosine similarity and tracking error. Potential proxies with higher cosine similarity and lower tracking error (i.e. the volatility of the differences between return streams over time) to the subject investment will result in a higher match score. Venn does not prompt users to select potential proxies with match scores less than 50%.
What is cosine similarity?
Cosine similarity is a measure that is comparable to correlation. It seeks to identify the extent to which two investments move in the same direction. Instead of centering around a return stream’s mean, like correlation does, cosine similarity is centered around zero and measures the cosine of the angle between two data points.
Proxies are for estimation purposes only and have many inherent limitations. The methodology for calculating potential proxies was chosen in our professional judgement, and will not always yield the most accurate available proxy. Our potential proxy suggestions are not a recommendation as to any portfolio, allocation, strategy, or investment nor an offer to sell any security.
 The minimum amount of data required depends on data frequency and type of analysis. For Potential Proxies, if a user’s portfolio or investment has daily data, Venn will require at least 6 months, or if monthly it will require at least 1 year. Investments with quarterly data can’t be proxied on Venn.
 The proxy date range starts when Venn has data for the proxy and ends when the subject investment data begins.
 Daily returns are rolled up to weekly, therefore 6 weeks are required for investments with daily data. 6 months are required for investments with monthly returns.
This document highlights certain aspects of this feature. As an overview, it does not discuss all material facts or assumptions. Please see Important Disclosure and Disclaimer Information.