Special Announcement

On Monday, April 13th at 2:00-2:30PM EST, we will be hosting a live strategy review webinar. The webinar will cover:

  • Β Why shortening duration is not a panacea for rising rates
  • A strategy outlook for Multi-Asset Income
  • A strategy outlook for U.S. Factor Defensive Equity

You can register at https://attendee.gotowebinar.com/register/5053445966609773825

Market Thoughts

In a recent conversation with a financial advisor, I was asked why Newfound offered two risk managed domestic equity strategies: Risk Managed U.S. Sectors and U.S. Factor Defensive Equity. The underlying question was really, β€œWhat’s the difference between a sector- and a factor-based approach?”

The basic definition for a sector and a factor is not too dissimilar. Both group equities based on a common characteristic. Sectors do so by dividing the equity universe based on the segment of the economy that a business operates in. A sector, therefore, is often made up of direct competitors that are all affected by common risks. For a company to change sector, it would have to change its business.

A factor, on the other hand, typically divides the equity universe based on a desired tilt. Commonly, these tilts are based on characteristics of both the security as well as the business. Some of the most popular tilts are:

  • Value: Stocks that are priced cheaply, relative to their peers, with respect to underlying company fundamentals.
  • Size: Stocks with relatively small market capitalizations.
  • Momentum: Stocks that have recently appreciated in price more quickly than their peers.
  • Low Volatility: Stocks exhibiting relatively lower volatility levels than their peers.
  • Dividend Growth: Companies with a history of both paying and growing their dividend.

Factors definitions are often backed by academic research showing that a specific tilt is capable of delivering long-term excess risk-adjusted returns. A common example would be the value factor, where portfolios of stocks priced cheaply relative to fundamentals dramatically outperform their glamour counter-parts.

Unlike sectors, stocks can belong to multiple factors and move between factors over time. For example, a stock that has sold off significantly may find itself in a value basket and upon recovery, move into a momentum portfolio.

Since factors typically cover all sectors, it should come as no surprise that factors have relatively higher correlations to each other than sectors do. The average trailing 1-year cross-correlation between sectors is 65%, with a maximum pairwise correlation of 84% between Financials and Consumer Discretionary and a minimum pairwise correlation of 27% between Energy and Utilities.

The five factors above, on the other hand, have an average pairwise correlation of 91% with a maximum pairwise correlation between Low Volatility and Dividend Growth at 95% and a minimum pairwise correlation between Value and Momentum at 78%. In other words, the minimum pairwise correlation between factors is just shy of the maximum pairwise correlation between sectors.

2015-04-11 – Figure 1

This makes intuitive sense, however: sectors are defined to be disjoint from one another while factors are not. The risks that apply to energy stocks do not necessarily apply to health care. Factors, on the other hand, have varying exposure across multiple sectors over time and may actually hold identical stocks between them. Being relatively small does not prohibit a stock from also being a dividend grower or being a good value.

By their disjoint nature, sectors also do a good job of segmenting risks. It is easy to identify that the energy sector would be sensitive to changes in oil prices. Factor portfolios require looking under the hood to underlying exposures – and just because a factor has exposure today does not mean it will have the same exposure in a year. The consistent nature of sector definitions and the common-sense association with risks is likely what makes them so popular: In a study published on Friday by Goldman Sachs, U.S. sector-based ETFs make up 18% of all ETF volume.

Implementing a trade via sectors, therefore, is generally a play about very specific risks at a very specific time. For example, we removed exposure to energy in our sector portfolios nearly 6 months ago due to waning momentum largely driven by sensitivity to crashing oil prices. We removed exposure to utilities more recently due to degrading momentum from underlying exposure to interest rates. These are highly specific risks that we are seeking to control for.

When a portfolio is built utilizing factors, the idea is to create a portfolio that is consistently tilted towards certain characteristics that will generate excess risk-adjusted returns over the long run. While factors do tend to go in-and-out of favor, ideally a diversified portfolio of such factors will out-perform the broad market on the vast majority of rolling periods.

So a Newfound sector portfolio will likely have higher turnover due to its flexibility to address sector-specific risks. We can remove exposure to energy or utilities while retaining exposure to the other sectors. In a bullish environment where no downside sector-specific risks manifest, however, we do not expect such a portfolio to out-perform the broad market.

A Newfound factor portfolio, on the other hand, is designed to generate excess risk-adjusted returns over all market cycles. To do this, however, we have to give up the ability to selectively hedge sector-specific risks, remaining subject to the exposures in the underlying factor portfolios, which can be extreme. Value, for example, currently has a 33.5% allocation to financials and a 24.3% allocation to energy; momentum has a 32.1% allocation to health care and a 23.5% allocation to technology. There is little flexibility in the portfolio to reduce this exposure.

Of course, capital protection is critical to our portfolio mandates. Believing that sectors provide us a better indicator of economic risks, the amount of short-term U.S. Treasuries introduced into our U.S. Factor Defensive Equity portfolio will actually mirror the amount in our Risk Managed U.S. Sector portfolio.

While both portfolios have the flexibility to move entirely to cash, there are subtle differences in why an investor would choose one over another. A sector-based investor is going to be concerned about sector-specific risks, and to manage them is willing to give up generating excess risk-adjusted returns in a bull market. A factor-based investor is going to try to generate excess risk-adjusted returns in all market cycles, but does so at the cost of being able to hedge sector-specific risks.

In Our Models

There were no rebalances in any of our portfolios this week.

The most significant event we’re tracking is in bank loans in our Multi-Asset Income portfolio. The momentum signal has oscillated several times in the past few weeks. If the signal remains consistently on, we expect it to increase to approximately 18% of the portfolio.

We’re not surprised to see Bank Loans flirting with positive momentum. Often positioned as a high yield alternative, we see two critical advantages that bank loans have over high yield in the current environment. First, while broad high yield indices have a near 14% allocation to the energy sector, bank loans only have a 3% exposure, making them much less sensitive to swings in oil prices. Secondly, not only do bank loans have a significantly lower modified duration, but they are also structured with floating rates, making them less sensitive to the risks of a rising rate environment. As market participants still struggle to find yield, we expect that many may turn from high yield towards bank loans, especially as spreads between the two have come down from December highs.

Corey is co-founder and Chief Investment Officer of Newfound Research, a quantitative asset manager offering a suite of separately managed accounts and mutual funds. At Newfound, Corey is responsible for portfolio management, investment research, strategy development, and communication of the firm's views to clients. Prior to offering asset management services, Newfound licensed research from the quantitative investment models developed by Corey. At peak, this research helped steer the tactical allocation decisions for upwards of $10bn. Corey holds a Master of Science in Computational Finance from Carnegie Mellon University and a Bachelor of Science in Computer Science, cum laude, from Cornell University. You can connect with Corey on LinkedIn or Twitter.