By Alex Botte, CFA, CAIA, Vice President, Two Sigma
At the end of 2019, Two Sigma’s Venn platform added three new macro style factors to its risk model, the Two Sigma Factor Lens: Fixed Income Carry, Foreign Exchange Carry, and Trend Following. The first two factors are meant to capture an investment’s or portfolio’s exposure to carry strategies: i.e., holding higher-yielding bonds or currencies funded by their lower-yielding counterparts. Trend Following is meant to capture momentum, using the trailing returns of macro asset classes.
The latter sounds a lot like the Momentum equity style factor, which already exists in the Two Sigma Factor Lens. In fact, the two have a positive long-term correlation of 0.3. How are these two factors different, and why does it matter?
DIFFERENCE #1: THE UNDERLYING ASSET CLASSES
The first, most obvious difference between the two is the asset classes used to construct each factor. Momentum is an equity style factor that is built using individual stocks — so it could be long Apple and short Alphabet, as an example. Trend Following is a macro style factor and is therefore built using derivatives (e.g., futures and forwards) in several macro asset classes: equities, fixed income, currencies, and commodities — so it could be long an S&P 500 Index futures contract and short a gold futures contract for example.
DIFFERENCE #2: CROSS-SECTIONAL VS. TIME SERIES MOMENTUM
The second, and arguably the most important, difference between the two factors is the type of momentum that they are capturing. The Momentum equity style factor is constructed cross-sectionally, meaning an asset’s momentum is compared to the momentum of other assets. Trend Following, on the other hand, is constructed using time series momentum, which focuses purely on an asset’s own past returns.1
This has an important implication: the Momentum factor will be market neutral to global equity markets at all times, while the Trend Following factor can take on conditional (positive or negative) correlation in any of the four asset classes mentioned above, but it should not demonstrate meaningful correlation to those asset classes over a full market cycle.2
Here’s a simple example that showcases the difference between cross-sectional and time series momentum. Imagine that you are constructing a momentum factor using only two assets. Their momentum, as defined by their past performance,3 looks as follows:
Asset 1: -10%
Asset 2: -15%
A cross-sectional momentum implementation would buy Asset 1 and sell Asset 2 because Asset 1 outperformed Asset 2. Notice how the long-short factor would be market neutral to whatever market Assets 1 and 2 belong to.
A time series momentum implementation would sell both Assets 1 and 2 because they both have exhibited negative momentum. This would lead, at least over the short-term, to a negative correlation with the market that Assets 1 and 2 belong to.
In practice and in academic literature, cross-sectional momentum is applied more so across single-name equities than macro assets. The reason being that there are thousands of single-name equities versus roughly one hundred macro assets spanning various asset classes. Therefore, the greater breadth of the equities universe allows for greater diversification as well as more opportunities for apples-to-apples comparisons (i.e. performance trends of Microsoft versus Google is a more clear comparison versus performance trends of the SPY4 compared to the Taiwanese dollar).
DIFFERENCE #3: THE LOOKBACK PERIOD
The final major difference between the two factors concerns the lookback period used to determine the momentum of an asset. The Momentum equity style factor considers the stock’s performance over the past twelve months, whereas the Trend Following macro style factor considers the contract’s performance over the past six months and over the past twelve months (the two lookback periods are equally-weighted).
The reason for the shorter-term component to Trend Following is that in practice, macro trend followers and Commodity Trading Advisors (or CTAs) tend to focus on shorter horizons due to the abundant liquidity and capacity of the underlying macro derivatives that they trade.5
In summary, the two factors are certainly related — they both intend to capture momentum, or the tendency for an asset to persist in its relative (or absolute) performance. However, we believe it’s important to include both factors in a risk model, as they will tend to show up for different reasons.
The Momentum equity style factor can appear in equity market neutral, long-short equity, or even long-only stock funds, especially when the latter is compared relative to its benchmark.
On the other hand, the Trend Following macro style factor can appear in trend followers, global macro managers, or CTAs.
If you have a mix of these types of managers in your portfolio or if you are evaluating a multi-strategy manager, it is entirely possible for both Momentum and Trend Following to make an appearance in the factor analysis output.
1 Moskowitz, Ooi, and Pedersen (2011) “Time Series Momentum”
2 A full market cycle includes both a bear and bull market for the asset class.
3 In this illustrative example, we assume the past performance is calculated over the same period for both types of momentum. As we’ll discuss in the next section, the lookback period used to calculate past performance can differ.
4 SPY is the ticker of an S&P 500 Index-tracking ETF.
5 There are many decisions and tests that go into the construction of a new factor in the Two Sigma Factor Lens. In general, the research first involves scanning the academic literature to understand how the factor in question is canonically defined. In the case of Trend Following, 1, 3, 6, and 12 month lookback periods were most represented in the academic literature. Upon testing these four lookback periods, it turned out that only the 6 and 12 month lookback periods were significant in explaining the risk and returns of actual managers pursuing trend following strategies.
This article is not an endorsement by Two Sigma Investor Solutions, LP or any of its affiliates (collectively, “Two Sigma”) of the topics discussed. The views expressed above reflect those of the authors and are not necessarily the views of Two Sigma. This article (i) is only for informational and educational purposes, (ii) is not intended to provide, and should not be relied upon, for investment, accounting, legal or tax advice, and (iii) is not a recommendation as to any portfolio, allocation, strategy or investment. This article is not an offer to sell or the solicitation of an offer to buy any securities or other instruments. This article is current as of the date of issuance (or any earlier date as referenced herein) and is subject to change without notice. The analytics or other services available on Venn change frequently and the content of this article should be expected to become outdated and less accurate over time. Two Sigma has no obligation to update the article nor does Two Sigma make any express or implied warranties or representations as to its completeness or accuracy. This material uses some trademarks owned by entities other than Two Sigma purely for identification and comment as fair nominative use. That use does not imply any association with or endorsement of the other company by Two Sigma, or vice versa. Click here for other important disclaimers and disclosures.
Alex Botte, CFA, CAIA is a Vice President at Two Sigma, working on the research team that is focused on new factors and methodologies for the Two Sigma Venn platform. She can be reached at firstname.lastname@example.org.
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