A recent Two Sigma paper, Introducing the Two Sigma Factor Lens, provided a framework for analyzing multi-asset portfolios through a lens comprised of broad, liquid, asset class proxy indexes.

As a reminder, the Two Sigma Factor Lens is intended to be:

**Holistic**, by capturing the large majority of cross-sectional and time-series risk for typical institutional portfolios;**Parsimonious**, by using as few factors as possible;**Orthogonal**, with each risk factor capturing a statistically uncorrelated risk across assets;**Actionable**, such that desired changes to factor exposure can be readily translated into asset allocation changes.

This factor lens, and our ongoing work to expand it, form the foundations of the Venn™ platform.^{1}

### The missing ingredient: factor return forecasts

While a factor lens may help provide insight on a portfolio’s historical sources of risk and returns, choosing a desired allocation of factors or assets is a far trickier issue. Doing so requires forecasts of risk and return expectations.

In a new paper, Forecasting Factor Returns, we propose a methodology using historical data to quantify the return premia for major asset-class based factors.

The paper introduces a handful of innovations intended to improve the accuracy of our long-term return forecasts. Specifically, we:

- Use new asset class return proxies to extend our analysis much further back than the daily return histories of most modern indices.
- Separate the most heterogeneous of the prior paper’s factors, Commodities, into six sector-based factors for which the long-term premia are individually estimated.
- Apply (what we believe to be) common sense adjustments to long-term histories — slightly overweighting recent returns and applying empirically-based shrinkage across the observed historical Sharpe ratios to generate our forward-looking estimates of each factor’s premium.