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.
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.
Few people bothered to see Trouble with the Curve, a recent baseball movie starring Clint Eastwood and Amy Adams, and most critics didn’t like it. I did see the movie, and without giving away the plot, it is fair to say that the film is a cry against quantitative analysis in sports. Eastwood plays an aging baseball scout with failing eyesight who has to rely on his daughter (Amy Adams) to evaluate the home office’s number one prospect. In the end, all of the number crunching in the world can’t come up with a better analysis than Eastwood, who can hear the sound of the bat on the ball and subsequently knows better than to sign the prospect. It was impossible to watch this movie without thinking of last year’s hit film Moneyball.
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:
Over the past several weeks we have seen some positive developments come out of the quant department. Our proprietary quant model, which officially went live in the spring of this year (but was extensively back-tested through the rocky 2007-2011 period), gave its first bullish signal on 6/8/2012. Since then, the model has stationed in a tight range in the upper half of the mildly bullish bracket, briefly touching the bullish bracket in a few occasions. In the meantime, one after the other, the quant models of our independent research providers joined the chorus. As a result, our quant model scorecard (see below) is currently showing a whole lot of green (bullish) and no red (bearish).
Two of the most destructive forces for the economy are the words “let’s suppose” and the use of the = sign. When we put the two together, they form a combustible combination that gives seemingly well-intentioned and rational investors the power to disintegrate assets at will. This is because investors have a desperate need for quantitative models that will justify or ‘prove’ their investment theses, if for no other reason than it provides much better job security than having an investment thesis based on their good judgment and common sense. Unfortunately, this state of affairs gives rise to some of the most egregious misuses of the scientific method that one could imagine.