2016 began with a thud and ended with a bang. After one of the worst-ever starts to a year, U.S. stocks managed to rebound and ultimately finish the year with solid gains. Much of the rise came in the final few weeks of the year, following the surprising results of the U.S. presidential election. Indeed, there has been an abrupt change in market sentiment, and asset prices have largely taken their cues from a recalibration of economic expectations in the wake of the surprising Trump victory and Republican sweep of Congress.
On September 19, 2016, S&P Dow Jones and MSCI, Inc. added a sector for Real Estate. Up to this point, REITs have traditionally been considered a sub-industry and part of the Financial sector, but as of the market close on August 31, 2016 (and effective September 19, 2016), they were split from the Financial sector and moved to their own sector (with the exception of Mortgage REITs). This should not be a surprise for investors, as the change had been announced by index providers, S&P Dow Jones Indices and MSCI, back in March 2015.
Over the last couple of months, we have been preparing to expand our product offering by launching two new sets of strategies, called the Market series and the Quant series. They are offered as alternatives to our traditional strategies, now referred to as the Prime series, for a chance to help our current and future clients achieve their financial goals. While the Market series and the Quant series are different under many aspects, they share one important feature: under both strategies, a portion of the client’s portfolio is managed according to a rules-based, quantitative model developed in house at Pinnacle. Diversification has always been a core tenet of Pinnacle’s investment process and the way we manage risk. However, with this move, Pinnacle has now further expanded the diversification it offers to clients to a new dimension of risk: decision risk. While the Pinnacle traditional (now Prime) strategies rely primarily on the time-proven judgment, experience, and intuition of the members of the Investment Team, the new strategies are based on a rules- based decision-making process that is more objective and unemotional. In Pinnacle jargon, we say the Prime strategies are subject to manager risk, while the new strategies are subject to model risk. Modern Portfolio Theory tells us that by combining different sources of uncorrelated risks, we can move our portfolio farther out in the efficient frontier and achieve a better expected return-to-risk ratio.
According to the National Association of Home Builders (NAHB):
The Housing Market Index (HMI) is based on a monthly survey of NAHB members designed to take the pulse of the single-family housing market. The survey asks respondents to rate market conditions for the sale of new homes at the present time and in the next six months as well as the traffic of prospective buyers of new homes.
Our proprietary work shows that the HMI Index is negatively correlated to changes in interest rates, with a lag of about one year. This means that when interest rates fall (or rise), the HMI index tends to move in the opposite direction a year later. The rationale behind this relationship is simple: lower interest rates make new homes more affordable, thus leading to a brighter outlook for housing, as measured by the HMI index. In turn, increases in the HMI index typically coincide with better performance for home builders stocks.
Back in June our proprietary quantitative model gave us a warning signal by dipping below the neutral bracket into what we consider mildly bearish territory (see the red line in the chart). The fact that the external models we follow were also behaving similarly had us somewhat concerned. However, that turned out to be a brief signal, as the model quickly reversed course and crossed the neutral bracket in just a few weeks, landing in mildly bullish territory last week. The message was again confirmed by the external models, which all turned up over the past couple of weeks.
Over the past few weeks our proprietary quantitative model has experienced a significant decline, falling from an almost unequivocally bullish reading of 7.45/10 to a lower neutral reading of 4.33/10. The deterioration in the overall score was caused by a broad-based decline in several important variables including, among others, the relative momentum in early cyclical, late cyclical, and defensive sectors, the steepening of the yield curve, the growth-sensitive Australian dollar to Canadian dollar exchange rate, and implied volatility.
In a blog post I wrote in June 2012 (“Under the Hood of the New Manufacturing Report”) I wrote about the difference between the New Orders component and the Inventory component of the Manufacturing PMI, and how it tends to lead the overall PMI by about three months. Today, I want to look at how this indicator has performed since then, and what it is signaling for the near future.
When I look at nonfarm payrolls, I try to disregard the headline figure and look at the year-over-year percent change in unadjusted total payrolls. This allows me to remove any seasonal effects from the series without making any of the hard assumptions required by “fancier” seasonal adjustment methodologies. The November report that came out Friday puts us at 1.42% year-over-year growth, up from 1.38% in the previous month. The exponential three-month moving average, which serves to smooth out some of the month-to-month volatility in the series, sits now at 1.41%, down slightly from 1.43% last month. This moving average is plotted as a green line in the chart below. The purple line in the chart is a measure of the six-month trend in year-over-year payrolls growth. This measure will be positive (negative) if in the previous six months year-over-year payrolls growth has been accelerating (decelerating) and near zero if it has been relatively steady. Currently, the six-month trend reads 0.19%, which is remarkably close to zero. In fact, over the past six months year-over-year payrolls growth has been range-bound between 1.3% and 1.5%. Analyzing the entire sample, which goes all the way back to the 1930s, we found that the following two conditions almost always coincide with economic recessions: