A PDF version of this commentary can be downloaded here.

Market Recap

In our March 9, 2015 commentary, we introduced the concept of micro-correlation storms – short periods of time when major asset classes all move in the same direction. During the week preceding that commentary, ETF proxies for eleven major asset classes lost more than 28% in aggregate. Our analysis suggested that micro-correlation storms – while sure to cause quite a bit of heartburn – ultimately mean little for long-term investors.

Sure enough, just two weeks later the Fed induced a positive micro-correlation storm, reversing many of the losses experienced during the week ended March 6th.

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To create this micro-correlation storm, all the Fed had to do was lower its target federal funds rate range to 0.65-0.90% compared to the range of 1.00-1.25% communicated after the December 2014 FOMC meeting.

The chart below illustrates how the views of FOMC members have changed. In addition to more moderate rate hikes than previously expected, there are a couple of other interesting aspects of the data:

  1. Disagreement was the name of the game at the December 2014 meeting. Some members believed that rates should still be zero while others wanted a full 200 basis point hike implemented within twelve months.
  2. Something seems to have resolved these disagreements in the months following the December 2014 meeting. While the range of targets was still very large (0% to 1.75%), there seems to be a consensus building that 0.50% to 1.00% is the right neighborhood for December 2015 rates.

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Reading the chart: Each bar shows how many FOMC members believe that the target federal funds rate should be in that particular range at the end of 2015. For example, two members believed that the appropriate year-end target was 0.50-0.75% at the December 2014. The number of members supporting this same target range jumped to seven at the March 2015 meeting.

While we can read tea leaves all day long, what is perhaps most surprising is that Mr. Market places so much importance on the FMOC’s forecasts of what it might do in the future. Why is this surprising?

First and foremost, this week highlighted the FMOC’s unpredictability. If the FMOC can turn on a dime meeting-to-meeting, who knows what it may believe nine months from now.

Second, the FMOC is actually predictable in one way – its forecasts are predictably terrible. The following series of charts show how the committee’s forecasts for 2014 growth, unemployment, and inflation evolved through time and how they compared to actual 2014 values. Even forecasts made during 2014 – with the benefit of partial 2014 data – missed the mark.

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To be fair, the FMOC does not have a monopoly on terrible forecasting. Forecasts tend to be pretty bad generally. The next chart plots actual 10-year Treasury rates (solid line) against 10-Year Treasury rate forecasts (dashed line). Time and time again, professionals have mistakenly predicted higher future rates.

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Source: Deutsche Bank

We find interest rate forecasts since the 10-year dropped below 1.5% in 2012 particularly interesting due to the certainty with which calls of rising rates have been made. Many market participants make it seem like a secular bear market in fixed income is inevitable.

At Newfound, we are not in the business of forecasting markets, mainly because we would surely be as bad as everyone else. We have no opinion on the future of interest rates. Nonetheless, I thought it would be interesting to present one counterargument to those in the “rates must rise” camp (Note: the purpose of this exercise is just to illustrate that there are reasons why rates don’t have to rise and to stimulate more thought on the topic. I am not saying that rates won’t rise or that this line of reasoning doesn’t have its flaws. As I said before, we have no view on what the future interest rate path will look like).

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The chart above plots the ratio of the U.S. government’s annual interest payment obligation – estimated with some simple assumptions – to GDP. The ratio has remained within a relatively tight range with a long-term average of around 3.5%.

Federal debt-to-GDP currently sits around 100%. The Congressional Budget Office expects the government to run a deficit through at least 2024. This suggests that further debt growth is likely, but for now let’s assume debt-to-GDP stays constant at 100%. With debt-to-GDP at 100%, 10-year rates must remain anchored around 3.5% if the government hopes to keep the percent of GDP needed to support the federal debt near historical norms. The high rates of the early 1980s were feasible because debt-to-GDP was around 30%, which kept the total debt payment manageable despite elevated borrowing costs.

John Kenneth Galbraith once said, “The only function of economic forecasting is to make astrology look respectable.” Perhaps this is because the future doesn’t often look exactly like the past.

In Our Models

Our Risk Managed Global Sectors strategy rebalanced this week as recent signal changes continue to be implemented per our tranche-based rebalancing methodology. Currently, 6 sectors are exhibiting positive momentum characteristics, 2 are neutral, and 3 are exhibiting negative momentum. Much of the momentum weakness is currently being seen in energy as well as traditionally defensive sectors.

The significant number of sectors exhibiting neutral/negative momentum continues to put the portfolio in a fairly defensive stance with respect to the sort of drawdown that would be required to build a short-term Treasury position within the portfolio.

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Justin is a Managing Director and Portfolio Manager at Newfound Research, a quantitative asset manager offering a suite of separately managed accounts and mutual funds. At Newfound, Justin is responsible for portfolio management, investment research, strategy development, and communication of the firm's views to clients.

Justin is a frequent speaker on industry panels and is a contributor to ETF Trends.

Prior to Newfound, Justin worked for J.P. Morgan and Deutsche Bank. At J.P. Morgan, he structured and syndicated ABS transactions while also managing risk on a proprietary ABS portfolio. At Deutsche Bank, Justin spent time on the event‐driven, high‐yield debt, and mortgage derivative trading desks.

Justin holds a Master of Science in Computational Finance and a Master of Business Administration from Carnegie Mellon University as a well as a BBA in Mathematics and Finance from the University of Notre Dame.