Skip to main content

You are here

Multifactor Indexes: The Power of Tilting

It wasn’t too long ago that the concept of factors in investing was the exclusive province of professors of finance and a few active “quant” managers. Mainstream portfolio construction was focused primarily on asset allocation. Within equities, that meant achieving the right balance in allocation to various segments such as large cap and small cap, country and sector, and perhaps value and growth style.

Today, factor allocation has entered the mainstream as a complementary approach to portfolio construction, alongside traditional asset allocation. An important driver of this development has been the creation of a new array of indexes that sharply focus on one factor at a time. This has opened up new possibilities for asset owners and advisors, including investing in index-replicating financial products, both to seek a desired factor exposure at low cost and to benchmark active managers to assess the value of their fees.

One thing that followers of single-factor indexes quickly realize is that the payoff for exposure to any one factor is highly variable. Factors typically follow different return patterns: value usually exhibits pro-cyclical performance, while quality is often countercyclical, for example. Market participants who do not employ a factor-timing or factor-rotation strategy are increasingly looking at fixed combinations of factors to gain potential improvements in risk-adjusted outcomes as compared to single-factor outcomes.

This paper compares three methods for constructing multifactor indexes. We will rank them as “good,” “better” and “best” based on their factor exposure strength. Spoiler alert: we will conclude that the FTSE Russell sequential tilting or “tilt-tilt” approach is the best alternative for strength of factor capture. For reference, this paper’s appendix contains a summary of the construction of the single-factor FTSE Russell Global Factor series.

The evolution of multifactor indexes: diversification without factor dilution

A lot of the discussion concerning factor combinations seems to focus either on reductions in tracking error with respect to a broad cap-weighted benchmark or on reductions in turnover, or both. As well, since not all factors underperform at the same time, a lot of timing risk can be eliminated by combining factors. But there is a legitimate concern about diluting individual factor exposures in the construction of a multifactor index. In this section, we will review three approaches to combining factors and illustrate them with a simple three-stock example.

Good: A composite index. The first step – the simplest multifactor index – is to take a weighted average of two single-factor indexes, say 50% value and 50% quality. Let’s call that a “composite index.” The advantage of this approach is its top-down simplicity. In principle, this is no different than replicating single-factor indexes in the chosen weights. An advantage in having both factors together in one index is that the index provider maintains the fixed weights, relieving the market participant of having to adjust index-replicating products.

Better: Composite factors. The next step in the evolution has been to combine a weighted average of the individual factors in a bottom-up approach. This takes better advantage of the interaction between factors.[1] It also offers potential trading economies. If a stock is eliminated from inclusion in one factor but added to another factor, then no trade needs to take place to maintain the index replication. Two trades would need to take place in the composite index approach.

Best: The tilt-tilt approach. The most recent step in multifactor index evolution is to construct the index as a tilt of one factor on another, rather than as an averaging of the factors. This multiplicative approach, also called sequential tilting, has the best chance of achieving multifactor objectives, as we will show.


[1] Bender, Jennifer, “The Whole is Not the Sum of the Parts,” State Street Global Advisors (2015).

— Download the complete paper —