All Collections
Onboarding
Venn Factor Interpretation Guide
Venn Factor Interpretation Guide
M
Written by Mei Chung
Updated over a week ago

Rev. Date December 11, 2023

Venn’s Two Sigma Factor Lens uses a multifactor approach consisting of 18 systematic factors. By viewing portfolio risk through the lens of unique and independent risk factors, capital allocators may better understand what is driving risk and return. The purpose of this guide is to provide context and interpretation for Venn’s factor analysis results.

What is beta?

While correlation provides information about the direction and strength of the relationship between variables, beta shows the direction and magnitude of the relationship. Venn factor exposures are measured by an investment’s or portfolio’s betas to each of our factors. A positive (negative) exposure to a factor (ß) indicates that the portfolio’s or investment’s returns are moving in the same (opposite) direction as the factor.

The size of each beta provides insights into how sensitive the portfolio or investment is to the factor’s movements. For example, if an investment’s Equity factor beta is 1.5 and the Equity factor goes up by 1%, we would expect the investment to go up by 1.5% all else equal.

What process is used to compute factor exposures (ß)?

Venn uses a two-step regression process to calculate factor exposures:

Step 1: First, the full factor lens is run through a Lasso regression to balance model fit with the number of factors. Factors with 0 beta coefficients after the Lasso regression are considered irrelevant and are excluded from the analysis. This step can improve the interpretation of risk drivers and enhance the accuracy of the analysis by discarding the noise from irrelevant factors, while also reducing potential overfitting or false positives.

Step 2: The second step is the ordinary least squares (OLS) regression, which applies the selected factors as independent variables against each return stream. The final Betas and t-stats from this step are displayed in Venn. For t-stats, a value greater or less than +/-1.96 is considered statistically significant. Within the selected factors from step 1, Venn will highlight those that are both statistically significant and explain more than 1% of risk.

What is a residual?

Using betas and factor returns, Venn's regression process breaks down the returns of portfolios or investments into their components driven by factors. Residual captures the return or risk that is not explained by the linear factor model. Residual may include risk and returns from manager alpha, factors not included in the Two Sigma Factor Lens, or other unknown factors.

What is factor residualization and how is it done?

One of the four pillars of the Two Sigma Factor Lens is "orthogonal," meaning each factor is intended to capture an independent, statistically uncorrelated risk across assets. Though some factors are naturally orthogonal, we residualize our less liquid macro factors against the more liquid ones to attempt to consolidate common risk exposures and reduce correlations within our factor lens. Our residualization process is intended to provide a truer picture of the total risk contribution of assets, avoiding the illusion of diversification from similar risks contributed by many different asset classes.

For example, our Emerging Markets factor represents a pure EM factor that strips out performance attributable to Equity, Interest Rates, Credit, Commodities and a global currency basket. Please refer to “How to Identify Independent Sources of Risk for Multi-Asset Managers and Portfolios” for examples and additional details on our residualization process.

What do the factor betas mean?[1]

[1] this guide is specific to the USD factor lens. The Two Sigma Factor Lens is available in the G10 currencies.

[2] the Local Inflation factor is only available in USD and GBP

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.