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Factor investing has been around for decades. It involves targeting quantifiable characteristics or “factors” that are unique sources of risk and return, and that are common across asset classes
There are two main types of factors that have driven risk and returns over time: macroeconomic factors and style factors. The former captures broad risks across asset classes while the latter seeks to explain risk within asset classes.
Want to dive into a different way to think about factors? Read more about our "Bacon Factor" analogy.
Traditional asset allocation can hide risk in portfolios because different asset classes may have exposure to the same risk factors. For example, high yield corporate bonds and stocks have exhibited a long-run positive correlation due to each having exposure to the Equity risk factor (i.e. long-term economic growth and profitability of companies). By viewing portfolio risk through the lens of unique and independent risk factors, capital allocators can better understand what is driving risk and return, which may lead to more precise decision making.
In fact, an analysis done in 2022 using Venn’s Two Sigma Factor Lens™️ of the Bloomberg Barclays Global High Yield Index indicated that only 21% of the index’s risk was driven by the Credit factor, whereas an additional 42% of the risk was driven by the Equity factor.
The Two Sigma Factor Lens™️, by Venn, uses a multifactor approach consisting of 18 factors in four categories: Core Macro, Secondary Macro, Macro Style, and Equity Style. Venn decomposes risk into these factor categories, providing a clear understanding of how to better manage your portfolio and use factor investing for diversification. Venn is designed to be:
Macro Factors are risk factors shown to correspond to the principal drivers of asset class returns. Macro risk factors are broadly known, widely accessible at a relatively low cost, and often can explain significant amounts of risk in diversified institutional portfolios. They are common in institutional investor’s portfolios due to their high liquidity and capacity.
These are lower-capacity risk factors shown to correspond to sizable common risk drivers across individual securities, but with lower correlations to asset class returns. Academic research has identified multiple style factors that appear to have long-term return premia resulting from investor behavioral biases or risk aversion. In short, style factors correspond to sizable common risk drivers within asset classes, such as individual stocks or bonds.
Read our most recent Factor Performance report.
These are idiosyncratic sources of risk (i.e., uncorrelated to other known factors) that are limited in capacity and have historically commanded higher fees. These risks generally appear as “residual” in a returns-based statistical factor analysis given their low correlation with known factors. It is essentially the risk not captured by any factors in a factor model, and often be interpreted as the risk and return driven by unique manager skill.
However, not all residual risk generates a return premium, and investors should be careful when interpreting the sources behind residual risk and return when looking at historical performance.
Factors with strong empirical evidence and/or fundamental justification for a long-term return premium are considered to have a “risk premium,” which may reward investors for holding exposure to that risk factor over time. However, not all identifiable risk factors carry a corresponding risk premium.
A risk premium may compensate investors for bearing certain risks such as undiversifiable market risk, mandate constraints, operational complexity, or behavioral biases like risk/loss aversion, herding mentality, or recency bias.
The Equity factor, which represents exposure to fundamental risks such as macroeconomic growth and corporate profitability, is an example of a macro factor that has historically delivered a positive long-term return in excess of the risk-free rate.
The Momentum factor, not to be confused with Trend Following, is often thought to be driven by investor behavioral biases such as initial under-reaction to fundamental news about companies. This is an example of a style factor that has historically delivered a positive long-term return in excess of the risk-free rate.
A common Venn use case is reviewing a manager’s factor analysis output to better understand their style and approach, and how it may lead to portfolio risk.
Sometimes the output can help confirm one’s understanding of what the manager is doing, and other times it can lead to questions for the manager about portfolio management and potential exposure.
An example may be a manager that claims to invest in stocks based on value characteristics, but factor analysis output indicates a zero or negative Value factor exposure. This would be unexpected based on the mandate, so an investor might want to better understand what’s driving that result. Read more about the manager review use case.
Venn leverages Two Sigma research and expertise in data science to help institutional investors better understand the risk composition of their portfolios and investments.
By using our regression based approach, Venn can help allocators learn more about the potential factor exposure of their portfolios (absolute or relative to benchmark) based on the return histories of their current portfolio holdings.
Venn displays the estimated exposures and contribution to risk and return for each factor identified in any return stream. Venn can also assist in allocation decisions by analyzing and estimating the historical marginal impact of adding, eliminating or reweighting an investment in your portfolio.
Our clients use Venn factor analysis to help them quickly answer critical investment and portfolio questions including:
Venn offers returns-based analysis that helps our clients measure exposure to factors that cut across asset classes such as inflation, interest rates, or commodities even when they do not have access to holdings level information in their portfolio. Returns-based analysis requires only a time series of manager or portfolio returns and the time series factor returns provided by Venn. This is designed to aid in the analysis of institutional investor portfolios whose investments span multiple asset classes, and where holdings data is more sparse.
For example, fine wine is popular among investors as an alternative asset, particularly for its low correlation to traditional investment assets and lower volatility profile. Read more about how you can use Venn to analyze many asset types, even wine.