Core Plus Strategy: The Persistency of Preferences

Core Plus Strategy: The Persistency of Preferences

October 2013 

The Convergence Core Plus Strategy is a sharp departure from the traditional quantitative approach. Nearly all quantitative models are based on extensive back testing into the efficacy of various combinations of factors or metrics. These models can be very simple, consisting of a handful of metrics, applied across the universe of securities and used systematically to rank the relative attractiveness of prospective investments on a regular schedule; or complex, involving mathematical formulas that weight metrics by industries and other sub groups, and set triggers for reevaluation. Nearly all, however, have one aspect in common. The model remains largely static, unresponsive to current or anticipated market conditions. Most attempts to capture “the environment” involve inputs to the model. Macro economic and monetary inputs, earnings estimates, and econometric inputs are added in an attempt to capture the dynamic nature of the investment environment. The success of such attempts has been less than reassuring.

The Convergence Core Plus Strategy utilizes a proprietary dynamic model, where the model changes the relative weights of the metrics within the model, not just the inputs, in response to the changing environment. Developed over the last 8 years, the Convergence model is a mathematical construct that is designed to emulate the decision making process. In an ever changing investment environment, investors consider a myriad of macro inputs along with the growth and valuation characteristics specific to a particular investment. At times, the environment favors companies with maximum operation leverage for example. At other times, large stable companies that provide relative safety are favored. What our research has determined is that as the cycle progresses, these shifts in preferences on the part of investors are not only observable, but also measurable.

In the traditional model, advisors would attempt to deal with these shifting preferences by diversifying their effects away. Fill the “boxes.” The theory, of course, was that the correlation between the performance patterns of the 9 boxes was sufficiently low as to provide a diversification effect, resulting in higher risk adjusted returns. As discussed at length in our August 2012 white paper entitled “A New Model for Improving Returns,” the secular trend toward higher correlations between equities has worked to undermine both traditional active equity management, and the traditional allocation model. In recent years, not only have less than 30% of active equity managers been successful at beating their benchmark, but the advisor constructing their solution around the 9 box model has largely failed to achieve anything above market returns less fees.

The Core Plus Model

The Convergence Core Plus Model is designed to measure these shifting preferences as we progress through the cycle. With that input in hand, the weights assigned to what we refer to as the drivers of stock prices change. These drivers are simply a collection or composite of factors or metrics that reflect the preferences being favored in the market. The net result is that as investor preferences shift from large defensive companies to companies better positioned to benefit from a pickup in economic activity, for example, the characteristics associated with these companies receive a higher weight within the model, and therefore more companies with those characteristics find their way into the portfolio.

Isn’t this process precisely what the investor does every day as they weigh the prospects of a particular investment? Before the investor looks for a “cheap stock,” do they not first consider the environment that they are in, the conditions that would favor a particular industry, or a particular company? The Convergence model is designed to directly capture these considerations as reflected in shifting preferences in the market.

The Persistency of Preferences

Our model makes no attempt to forecast. While we do incorporate some “forward looking” inputs in our model, no attempt is made to forecast GDP growth, monetary policy, industry growth rates or preference shifts. To do so would introduce a vast array of forecasting errors. The relationship between forecasts and performance is tenuous at best. Consider “earnings estimates.” Our work consistently calls into question the efficacy of long term earnings estimates as there exists little correlation with ex post performance.

Instead, the Convergence model relies on the “persistency of preference shifts.” All of our research, covering a period over 30 years, indicates that preference shifts tend to be gradual and persistent. Notwithstanding extreme bouts of volatility at times, investors tend to rationally assess an environment and incorporate that into their evaluation of the characteristics that are to be rewarded. In fact, our work over the past 8 years operating this model confirms that short term volatility spikes do not derail the underlying preferences, but do represent whipsaw traps. Once the volatility spike subsides, the preference trends tend to continue undisturbed.

In this paper we will explore this notion of the persistency of preferences.

Refreshing Something Old

Value versus Growth. Here are two preferences that most of us have followed throughout our career. The value versus growth decision is a classic preference shift.ᅠNote the long term trends.

In the Convergence Model we measure both the market’s preference for “relative valuation” as well as “traditional valuation” as opposed to “value” versus “growth.” We have found that a shift in preferences toward owning “cheap stocks” captures not only what we would consider to be value stocks, but also growth stocks at a reasonable price, so called GARP stocks.

Our research sought to identify the list of preferences that would “explain” the movement in stock prices through the cycle. We found that value versus growth, and large versus small did not account for, or explain price behavior sufficiently. In the end we found that 13 preferences would account for nearly all of the price movement across all 24 industry groups. As stock prices change we are able to specifically measure what combination of preferences is driving performance.

With these in hand, now let’s look at one of the classic preference shifts in modern history, the lead up to, and the subsequent collapse of the stock market in the wake of the internet bubble. In the lead up, stocks possessing momentum out-performed. A throw back to the old physics axiom that a body in motion remains in motion, “price momentum” can at times represent a very powerful preference. In the chart below we compare the rolling 12 month relative performance of the top 10% of stocks in the Russell 3000 as ranked by “price momentum” to the Russell 3000 as well as the top 10% of stocks as ranked by “traditional valuation” versus the Russell 3000. Two noteworthy observations: first momentum worked. In fact, it was the preference with the highest efficacy leading up to the collapse. Second, notice how long price momentum worked. Often, analysts are quick to dismiss momentum as a short term trading, or technical tool. Not in this case. Momentum proved to be one of the most dominant and persistent preferences over a period of years.

Second, the orange line looks at the same time period but now focuses on the “traditional valuation” preference. In traditional valuation we include all of the classic valuation metrics nearly every analyst uses such as price to earnings, price to free cash flow, price to book. We again look at the relative performance of the top 10% (cheapest) as ranked by these traditional measures to the market. The chart is a mirror image of the momentum chart. We again make two observations: valuation was ignored for years leading up to the market top. Second, valuation then provided one of the most powerful moves in stock prices on a relative basis in history. And it lasted for years. In fact, except for a short period in 2003, traditional value continued to add relative return over the market until 2006.

The markets surrounding the internet bubble provide a classic and abundantly clear shift in preferences. Think of the performance impact shifting your portfolio’s relative tilt from momentum to valuation would have had on the total long term return of the portfolio. What should also be clear is that you did not have to call the turn. Being months early, or months late would have had a minimal impact. Further, an abrupt switch was also not necessary. A gradual tilt in the other direction as the preference shift became more apparent would have also added significant value to your portfolio.

A Little Closer to Home

Now let’s examine some more recent examples. Since the financial crises the market has shown periodic bouts of volatility. Abrupt shifts, as we saw in the summers of 2010, 2011, and 2012 would prove to be ideal test cases for the persistency of preferences.

First we examine “relative value.” Since the market bottom on March 9, 2009, investors have displayed a very strong preference for cheap stocks. Shown below is the performance of the top decile (cheapest) 10% of stocks as ranked by relative value versus the bottom 10% (most expensive). A close examination of the data reveals that in the summer swoons “cheap” stocks did indeed sell off more than their more expensive counterparts. It is often the case that in quick reversals, investors tend to liquidate their winners first, thereby locking in profits. Notice however that the preference push toward rewarding valuation quickly got back on track. This is in spite of what most agree has been one of the more volatile periods in recent history.

Next, let’s examine a preference shift that did not have its inflection point at the market bottom. Shown below is the same examination, showing the top and bottom decile of stocks ranked this time by “earnings momentum.”

Following the market turn on March 9, 2009 investors seemed to make little distinction between companies that were accelerating their earnings and those that didn’t. That was of course until the summer of 2011. When the market sold off, companies with little earnings momentum took an outsized hit, and have never recovered on a relative basis. The recent acceleration in the shift toward companies with accelerating earnings likely reflects a growing level of confidence in the outlook for economic activity. Note again, this trend is now over 24 months in length.

Finally we examine another of our 13 preferences called “capital discipline.” At Convergence we describe a company as having “capital discipline” if the company’s management is shareholder friendly. The two best ways for management to reward shareholders is to increase their dividend, and buy back their shares. Dividend yield is important, but we include yield as part of valuation. Shown below is the same analysis as before but now looking at the top 10% as ranked by capital discipline versus the bottom 10%.

During all of 2011 and the first half of 2012 dividend yield was sought out by investors. Nervous about the general direction of the market, and tired of low interest rates, yield became an overwhelming preference. By the spring of 2012 however, yield started to fall out of favor. But capital discipline gained favor as one of the more powerful shifts in the past few years. As concerns about rising interest rates started to negatively impact the performance of higher yielding stocks, dividend growth and share buybacks gave investors more comfort. The preference shift to capital discipline is now almost 2 years in the making, and recently has accelerated.

Separating the Wheat from the Chaff

Not all preference shifts are as clear and as stable as the examples above. The heart of our research has been devoted to separating noise from a true preference shift. At times, many of our 13 preferences yield little or no informational value. The efficacy of a particular preference in a particular industry group may be elusive. In some industry groups investors assign little or no value to certain characteristics. Performance attribution therefore cannot be ascertained. As a statistically significant shift begins to reveal itself, however, our model is designed to identify it and adjust the portfolio to exploit it. The rewards are significant.

Catching a Falling Knife

The Convergence Dynamic Model is not a trading model. It is an investment model. It is entirely built around capturing a material percentage of the shift in investor preferences as outlined in the above examples. We researched mathematical models designed to capture inflection points while we ran our hedge fund from 2005 until 2010. We found that while return was enhanced, risk exploded. Further, trading costs increase dramatically as you continually position the portfolio based on numerous false signals.

As a result, our model makes no attempt to catch a falling knife.


As outlined in our August 2012 white paper, rising correlations in the market have served to undermine the traditional model. Even as correlations have risen, there exist strong behavioral tendencies on the part of investors, however. Specifically, investors still tend to reward investments that embody characteristics that they believe will prevail in an otherwise uncertain environment. These so called preferences are identifiable and measurable. The issue is whether these preference shifts are sufficiently persistent to be exploitable. In this paper we demonstrated that in fact, notwithstanding bouts of extreme volatility, preferences have remained persistent, and therefore can be captured within the portfolio management process.

About Convergence Investment Partners

Convergence Investment Partners uses quantitative tools to select investments, construct portfolios and manage risk for individuals, businesses, financial advisors and institutional investors. The team applies proprietary investment strategies, supported by extensive research, with a goal of generating alpha across various stages of the market cycle.


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