The Index Investor
January 2019

Key Takeaways

Over the next 12 months, our updated regime probability forecasts are as follows: Continuation of Current High Uncertainty Regime (40% up 5% from last month); transition to High Inflation Regime (5%, down 5%); return to Normal Regime (5%); and transition to Persistent Deflation Regime (50%). In making our forecast, we are acutely conscious of Rudiger Dornbusch’s famous quote: “Crises take a much longer time coming than you think, then happen much faster than you would have thought.”

While some quantitative indicators point to a reduced level of stress in the macro system compared to last month, others point in the opposite direction, as do many of the qualitative indicators we track.

Key indicators this month were new reports on some underappreciated indicators, including the extent of the US housing market boom; new IMF data on the level of debt in the global economy; and the apparent speed with which China’s economy is slowing.

As we will discuss at length in next month’s feature article, we are also very conscious of the multi-period negative impact of the sharp increase in uncertainty we have recently experienced.

Asset Class Valuation and Momentum Indicators (@31Dec18)

Note: The language we use to describe our estimated likelihood of asset class over or undervaluation is based on US Intelligence Community Directive 203 on Analytic Standards, which includes the following table:

Market Stress Indicators (@31Dec18)

Compared to November, some of our market stress indicators have generally declined, while others have risen. BB rated bonds’ spread over the 10 Year US Treasury is now 3.60%, which puts it the 57th percentile since the series started in 1996. Such a low credit spread at this late stage in one of the longest expansions on is a sign of excessive credit growth, which underlies many other sources of market stress.

Market Stress Indicators: Forecast Discussion

We view financial markets as a complex adaptive system. The distribution of the changes generated by such a system follows a power law rather than the normal (Gaussian or "bell curve") distribution. The critical point is that large changes are much more common in complex adaptive systems than most people’s intuition leads them to believe.

While predicting the behavior of complex adaptive systems remains far more art than a science, various researchers have found that large changes in such systems are often preceded by subtle warning signs, as stress accumulates within them. While this research is not definitive, we believe that five warning signs are worth monitoring as potential indicators of growing stress within financial markets that could suddenly give rise to large changes in asset class valuations.

Our first indicator is the month-to-month autocorrelation of broad asset class returns (i.e., the relationship of this month’s returns to last month’s). A system under increasing stress loses resiliency, causing it to take longer to recover from perturbations; hence, autocorrelation increases as it approaches a critical transition (see, “Early Warning Signals for Critical Transitions” by Scheffer, et al).

The one-month autocorrelation of returns for the broad asset classes we monitor sharply decreased to (.09) versus .86 last month. This indicates that financial markets are becoming less ordered and are further away from a critical transition point (which would most likely be accompanied by sudden and substantial changes in asset class values) than they were last month.

The second market stress indicator we monitor is the Economic Policy Uncertainty Index published by the Federal Reserve Bank of St. Louis (via its FRED economic database), which is based on research by Baker, Bloom, and Davis (see their paper, “Measuring Economic Policy Uncertainty”). The index is based on automated text analysis of leading newspapers and magazine publications, to identify the frequency with which words and phrases are used that indicate uncertainty.

In our evolutionary past, when uncertainty increased our probability of survival was enhanced by staying close to our group. We still have that instinct. Research has found that as uncertainty increases, we have an unconscious bias towards higher conformity of our own views with those of a larger group (i.e., reduction in cognitive diversity). Behaviorally, heightened uncertainty induces more “social copying” of others, likely due to both conformity bias and the rational belief that others may be acting on the basis of superior information. This increase in conformity and copying makes a social system more ordered as uncertainty increases, and also reduces its responsiveness to perturbations (i.e., increases autocorrelation) because of delays in the social copying process.

The key point is that increasing uncertainty induces more, not less order in social systems, and in so doing primes them for sudden non-linear change.

This month, the average Economic Policy Uncertainty Index stood at the 95th percentile of its values since the data series began in 1985 – to be clear, just 5% of values were higher over that period. This was a further increase in average uncertainty since last month.

Our more fine-grained intra-month measure of uncertainty is the number of daily changes that are in the top and bottom 20% of the historical distribution. This month it fell sharply, to just the 9th percentile (i.e., it was lower than in 95% of the rolling 30-day periods since 1985). This is a sharp drop from last month, when it was in the 68th percentile.

To reiterate the point made above, high levels of uncertainty tend to cause people’s opinions to become more ordered, due to a higher tendency toward conformity and social copying. This primes a system for sudden, non-linear change.

Our third market stress indicator is the spread between the yield on AAA rated bonds and the 10-year US Treasury. We interpret this as a proxy for the level of investor concern about financial system liquidity. At the end of December 2018, this spread stood at 1.30%, (51st percentile), up slightly from last month’s 1.21% spread. That said, at the end of September it stood at .95%. So market concerns about liquidity have been steadily rising.

Our fourth market stress indicator is the yield spread between speculative BB rated bonds and the ten-year US Treasury. Throughout history, excessive credit growth has been a root cause of many financial crises. An indicator of such growth is falling credit spreads, particularly in the case of riskier borrowers. At the end of December, this spread was 3.60%, which still put it in the 57th percentile of spreads recorded since this data series began in 1996. This is a dangerously low level this late in what is already an exceptionally long period without a serious economic downturn.

Our fifth market stress indicator is what we term the implicit “political risk premium” that is implicit in the price of gold. Our starting point for estimating this premium is the three different roles that gold plays. First, gold is a store of value in a world of fiat currencies. When the rate of money supply growth exceeds the growth of nominal GDP, gold’s price should increase to maintain its purchasing power. Between 2007 and 2017, the US money supply (M2) grew by about 86%, while nominal US GDP grew by 35%. The stock of gold grew by 18%, based on mine production over this period. We therefore infer that 33% of the increase in the price of gold represented the maximum potential gold price change that could be attributed to a desire to hedge inflation risk (86% less 35% less 18%).

Second, gold is a unit of account. We take this to mean that the annual change in GDP expressed in terms of physical gold (i.e., nominal GDP divided by the price of gold) should equal the change in real GDP calculated using the GDP price deflator to account for actual inflation over the period. A key challenge is the point at which to start this calculation.

We chose the price of gold in 1995/1996. In that period, the change in real global GDP measured using the IMF’s price deflator just about equaled the change in GDP measured in terms of physical gold. We interpret that coincidence as indicating that at that point in time, concerns about future inflation and political risk were minimal, and the change in the price of gold was mostly driven by its role as a unit of account. We calculated a subsequent series of gold prices that would produce the same change in “gold GDP” as the actual real GDP as calculated by the IMF. Between 2007 and 2017, “gold as a unit of account” warranted a 21% increase in its price.

Gold’s third role is as a hedge against inflation and what we term “political disaster” risk. We subtract the 21% estimated compensation for actual inflation from the 33% “gross” inflation risk hedge to derive an apparent 12% increase in the gold price that reflected the true risk premium to hedge against possible future inflation.

However, between 2007 and 2017 the price of gold actually increased by 81%. This implies that 48% of this (81% less 21% less 12%) represented a premium for some other type of uncertainty at the end of 2017. The interesting question is the nature of the uncertainty for which gold is believed by some investors to be a superior hedge than traditional ports in a storm like short-term US government securities, or similar securities issued by other developed countries.

The logical inference is that the uncertainty in question must reflect a situation in which short term US Treasuries would be a less effective hedge than gold. This could be a world of widespread hyperinflation, capital controls, and/or radical changes in nations’ governments (of course, this would also imply a preference for investing in gold coins rather than bullion, as while the latter may be a store of value, it is far less convenient as a means of paying for transactions).

To put this in further perspective, this gold price “disaster risk” premium sharply increased from 2008 to 2012, then declined before sharply increasing again after 2016. Arguably, a significant part of the former increase reflects concerns about the potential inflationary consequences of dramatic quantitative easing by central banks. But this is not likely to be the case after 2016.

Over the last month, the price of gold rose by 4.7%. Through December 2018, the price of gold has fallen by about 1.5% since the end of 2017, so, on a rough approximation, the political uncertainty premium now stands at about 47%, compared to a low of 39% at the end of August 2018.

Macro Regime Forecast and Implications for Asset Class Values

Macro Regime Probabilities: Forecast Discussion

Summary: Why Did We Change Our Regime Probabilities?

These were the key pieces of new information we observed this month that caused us to decrease the probability that over the next 12 months we will transition into the Normal Regime, and increase the probability of remaining in the High Uncertainty Regime. All of these pieces of new information are discussed in more detail in this month’s Evidence File (see below).

(1) Two new reports on the extent to which house price increases in the United States have been increasing at an unsustainable rate.

(2) New reports indicating a worsening economic slowdown in China.

(3) New IMF analysis that integrates various types of outstanding debt into a single database, and show its true – and historically very high – level.

(4) The inevitable secondary impacts – including a negative impact on aggregate demand – of this month’s uncertainty shocks.

(5) The initial aggressive actions of the new Democratic majority in the US House of Representatives towards the Trump administration, and the president’s equally aggressive response (including the continuing budget stalemate and shutdown of the US government), continuing Gilet Jaune demonstrations in France, and rising uncertainty over Brexit – all of which reduce the probability of returning to the Normal Regime, and increase the probability of remaining in the High Uncertainty Regime for the next 12 months.


Forecast Methodology

The focus of our monthly macro forecast is twofold. First, the probability of a change in financial market regime that causes changes of 20% or more in asset class valuations over the next year. Second, contingent on such a change taking place, the probability of a subsequent transition to other regimes.

Our analysis focuses on four possible macro regimes: (1) Normal Times, where equity asset classes perform well; (2) a High Uncertainty regime that is usually short and transitory, where asset classes like short-term government bonds perform best; (3) High Inflation, where commercial property, real return bonds and other traditional hedges are favored; and (4) Persistent Deflation, which up to now has only been seen in Japan, and in which the relative performance of different asset classes remains most uncertain.

Our forecasting methodology is derived from our experience on the Good Judgment Project, as described in the book, “Superforecasting” by Gardner and Tetlock, as well as a range of other sources, from the intelligence community to systems dynamics and complex adaptive systems to statistics and political economy.

We start with base rate/reference case data about the historical probability of large changes in equity and bond valuations. We then analyze the current situation from both a quantitative and qualitative perspective. In the latter, we focus on the key endogenous drivers of macro regime change, including technological, economic, national security, social, and political trends and uncertainties. We also focus on three potential sources of exogenous shocks that could also produce a macro regime change, caused by environmental, disease, and cyber related events.

While most of our attention typically focuses on various flows (e.g., economic growth, change in the price level, sales, earnings, job creation, etc.), endogenously caused regime changes result when those flows push key stocks beyond a critical threshold or tipping point, often setting off non-linear reactions across multiple areas. As noted by Hyman Minsky and others, a classic example is the steady accumulation of outstanding debt until it reaches the point where it can no longer be serviced and triggers a crisis.

Base Rate Data

Since the end of World War Two, there have been fifteen months where a downturn in the US equity market began that eventually reduced asset class value by 20% of more. That is a hazard rate of about 1.75% per month. Put differently, in any given month there is a 98.25% probability that a 20%+ downturn won’t occur, or, in a given year, an 81% probability.

However, as the time without a 20%+ downturn extends, the compound probability that one will not occur shrinks. At the end of August 2018, it is more than nine years since the last equity market decline of 20% or more. The probability of that happening is only 15%.

To estimate the base rate for a 20% fall in bond prices (which historically has been caused by a sharp increase in inflation, as we saw in the late 1970s and early 1980s), we analyzed monthly historical AAA bond yields since 1919. For consistency, we used them to calculate the price of a ten-year zero coupon bond. We then calculated the probability of a price decline of 20% or more over three different holding periods: 12, 18, and 24 months. In any month, the annualized probability of a decline of 20% or more over the subsequent 12 months is 12%; over 18 months, 20%, and over 24 months, 25%.

The Current State of Quantitative Regime Predictors

Our quantitative methodology focuses on the level and change in three-month returns, over the most recent and previous three-month periods, for those asset classes which should perform best under different regimes.

As you can see in the following table, at the end of December 2018, this analysis indicates that, over the next 12 months, the balance expectations favored continuation of the High Uncertainty Regime with the next strongest support being a transition to the Persistent Deflation Regime.

Qualitative Analysis

Like Professors Andrew Lo, Doyne Farmer and others, we regard financial markets as a complex adaptive system (CAS), that exist as part of a larger macro system comprised of other CAS between which there are multiple feedback loops. These other systems include those that produce technology innovations, and economic, environmental, national security (including cyber), social, demographic, and political outcomes.

We also find that these systems tend to operate and generate effects in a rough chronological sequence, albeit with many feedback loops between them. The following chart highlights that the changes we observe in different areas at any point in time are actually part of a much more complex evolutionary process.

While most media coverage of these systems focused on flows (e.g., the size of the government deficit), rapid non-linear change in complex adaptive systems is often caused by a key stock (e.g., the amount of outstanding government debt) exceeding a critical threshold.

The next table highlights the key macro system stocks that we monitor.

In the next section, we will discuss information received over the past month that is related to these stocks, and which we believe is significant to our assessment of the probabilities that a critical threshold will be reached and a regime change will occur. We will conclude with our estimate, at the end of this month, of how close the macro system is to these critical thresholds, and the implications for financial market regime change probabilities.

Significant New Information in Observed in November 2018

In our methodology, new information is valuable insofar as it provides an updated indicator of how close we are to a critical threshold, or it is surprising and causes us to question the structure of our overall system model (e.g., the existence of another critical threshold we should monitor, or the range of possible outcomes for a key uncertainty).

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How Close is the Macro System to One or More Critical Thresholds?

As we have noted, the macro drivers of financial market regime changes typically follow a rough chronological sequence, from technology to economic, security, social, and political causes and effects. Yet there are many feedbacks loops between them, creating complex root causes for many of the critical thresholds we have identified.

Understanding the time dynamics in this complex system is critical to avoiding substantial downside investment risk.

We use the UK Met Office Warning Model to communicate our assessment of these time dynamics. We estimate the time remaining before a critical macro system threshold is reached that could trigger a regime change, which is usually accompanied by substantial changes in asset class valuations.

The model uses three increasingly serious levels of warning, from “Be Aware” (condition yellow), to “Be Prepared” (condition orange), to “Take Action” (condition red).

For our purposes, we denote as “Be Aware” (yellow) critical thresholds that we assess to be three or more years away. We estimate that “Be Prepared” (orange) thresholds could be reached within 1 to 3 years. “Take Action” thresholds are very likely to be reached within one year.

Given their nature, we also note that in our three “wildcard” areas (Environment and Energy related; Disease and Human Caused Bioevents; and Cyber and Electromagnetic Events), our forecasts have higher levels of uncertainty.

The following charts summarize our current estimate of the time remaining before different critical thresholds will be reached.

Conclusion

At the highest level, we believe the global macro system can be in one of four states, based on its degree of order versus disorder, and degree of social cooperation versus conflict. We believe that the system is currently in its most uncertain state, characterized by high degrees of underlying disorder and social conflict.

Our current forecast question is this: what is the probability we will either remain in the High Uncertainty Regime or transition to another regime over the next twelve months?

We currently estimate there is a 40% chance of remaining in the High Uncertainty Regime over the next 12 months, which will see the commencement of what promises to be a tumultuous US presidential campaign, and perhaps resolution of the Brexit saga (or at least the “end of the beginning”, and possibly the “beginning of the end”).

We also conclude that over the next 12 months, the probability of returning to the Normal Regime is slight, at 5%, as is the probability of entering the High Inflation regime over the next 12 months, at only 5%.

We estimate that the probability of entering the Persistent Deflation Regime over the next 12 months is now 50%. With the waning in the US of the stimulative effect of the Trump tax cuts, increasing uncertainty will have a more strongly negative impact on aggregate demand. This will be magnified by the substantial amount of debt that has been taken on by many companies, which more of them will likely struggle to service, which in turn will put more pressure on their cost structures, and potentially lead to increased unemployment, which will accelerate the downward spiral.

While we expect the High Uncertainty Regime will produce declines of 20% or more in equity asset classes, it is unlikely that any other asset class will experience a gain of 20% or more. We estimate there is a roughly even chance that gold could be the exception, with that increase heavily tied to continued global confidence in the US government and economy. While the apparent inflation and political uncertainty premia in the gold price today are high relative to the last 25 years, they are still below the peak reached in 2012, and it is possible that herding in the face of increasing uncertainty could produce 20% price gains.

Pre-Mortem Analysis

One of the most important forecasting disciplines is to ask yourself why your forecast could be wrong. Dr. Gary Klein’s research has shown that a very powerful and insightful way to do this is via a “pre-mortem analysis.” This method asks you to assume that it is a point in the future, and your forecast has been proven wrong (or your strategy or company has failed). You are then asked to look backward from this imagined point in the future, to explain why you failed, what you missed, and what you could have done differently to avoid your fate.

The pre-mortem method takes advantage of the fact that humans reason much more concretely and in more detail when explaining the past than they do when trying to forecast the future.

So let us assume that it is one year from now, and the high uncertainty regime has given way to a return to the Normal Regime, rather than to the Persistent Deflation regime.

How did this happen? What didn’t we anticipate happening?

(1) In previous issues, we have conjectured that perhaps because of an intensifying domestic debt crisis (and its own fear of Japanese-style deflation), or a belief that it had not yet achieved sufficient advantages to pursue more intense conflict with the United States, China could reach a new trade agreement with the US and EU to support continued economic growth. This would reverse (at least in the short-term) the growing tension in the US/China relationship, providing a strong confidence boost to the world economy and financial markets. In light of the twin deterioration of the Chinese economy and president Trump’s apparent political support in December, the probability that a temporary truce in the building trade war has risen. However, we do not believe this will reverse the fundamental transition that has taken place in US-China relations, from wary cooperation to increasing conflict. Hence we conclude that it is unlikely that a new US-China trade agreement would result in a sustained return to the Normal Regime.

(2) However we have not dismissed Donald Trump’s replacement by Mike Pence as a viable pre-mortem scenario that could cause our forecasts to be wrong. That said, upending our forecast would still require a Pence administration to make significant progress on a number of critical policy (and staffing fronts, such as the return of James Mattis to the role of Secretary of Defense) fronts, including relations with China, the productivity of the US healthcare and education systems, increasing real median household income, and reducing inequality.

Note: Combining this Forecast with Others and Extremizing the Result Should Increase Predictive Accuracy

Research has found that three steps can improve forecast accuracy. The first is seeking forecasts based on different forecasting methodologies, or prepared by forecasters with significantly different backgrounds (as a proxy for different mental models and information). The second is combining those forecasts (using a simple average if few are included, or the median if many are). The final step, which significantly improved the performance of the Good Judgment Project team in the IARPA forecasting tournament, is to “extremize” the average (mean) or median forecast by moving it closer to 0% or 100%.

Forecasts for binary events (e.g., the probability an event will or will not happen within a given time frame) are most useful to decision makers when they are closer to 0% or 100% than the uninformative “coin toss” 50%. As described by Baron et al in “Two Reasons to Make Aggregated Probability Forecasts More Extreme”, forecasters will often shrink their probability estimates towards 50% to take into account their subjective belief about the extent of potentially useful information that they are missing.

When you average multiple forecasters’ estimates, you are including more information, which should increase forecast confidence and push the mean estimate closer to 0% or 100%. However, this doesn’t happen when you use simple averaging. For this reason, forecast accuracy is increased when you employ a structured “extremizing” technique to move the mean estimate closer to 0% or 100%.

You can download an extremizing model from our website to use when combining the forecasts you use in your decision process. The extremizing factors in our model are those that the Good Judgment Project found maximized the accuracy of combined forecasts. Note that the extremizing factor is lower when average forecaster expertise is higher. This is based on the assumption that a group of expert forecasters will incorporate more of the full amount of potentially useful information than will novice forecasters.

Feature Article: Understanding the Critical Difference Between Macro Threats and Threat Signatures

In our work at Britten Coyne Partners, we focus on helping clients anticipate, accurately assess, and adapt in time to emerging threats that could become existential – i.e., they could put the survival of the organization at risk.

We think of these threats as existing in three increasingly challenging realms, which you can visualize as three concentric circles. The innermost is the realm of risk, where the nature of a threat is well understood, including its range of possible outcomes and affects, and the probability of their occurrence. Because they can be assessed using traditional frequentist statistics, these threats seem easy to price and hedge, via insurance or derivative contracts. Yet as Long Term Capital Management demonstrated, they can still be existential, for example, because they are poorly modeled or if a hedge counterparty defaults.

A far larger circle encompasses the realm of uncertainty, in which some combination of a threat’s possible outcomes, affects, and probabilities is poorly understood. The challenge posed by uncertainty was famously described by Frank Knight in his 1921 book, “Risk, Uncertainty, and Profit.” Quantitatively, uncertainty is usually assessed using Bayesian statistics, in which probability represents not the historical frequency of a phenomenon’s occurrence, but rather an observer’s subjective belief that it will occur in the future, and the consequences it will have. The basis for such beliefs ranges from intuition, to copying the beliefs of others, to more sophisticated approaches to evaluating and weighing relevant evidence (e.g., Dempster-Shafer or Baconian methods).

The far larger circle, whose true dimensions are unknowable, is the realm of ignorance (both individual and organizational). Chapter 12 of John Maynard Keynes’ 1936 book on “The General Theory of Employment, Interest, and Money” is still one of the best descriptions to how we make decisions in the face of ignorance, and the fragility of the assumptions (“conventions”) that underlie them. As Keynes wrote:

“The state of long-term expectation, upon which our decisions are based, does not solely depend, therefore, on the most probable forecast we can make. It also depends on the confidence with which we make this forecast — on how highly we rate the likelihood of our best forecast turning out quite wrong. If we expect large changes but are very uncertain as to what precise form these changes will take, then our confidence will be weak. The state of confidence, as they term it, is a matter to which practical men always pay the closest and most anxious attention. But economists have not analysed it carefully and have been content, as a rule, to discuss it in general terms…

“The outstanding fact is the extreme precariousness of the basis of knowledge on which our estimates of prospective yield have to be made. Our knowledge of the factors which will govern the yield of an investment some years hence is usually very slight and often negligible. If we speak frankly, we have to admit that our basis of knowledge for estimating the yield ten years hence of a railway, a copper mine, a textile factory, the goodwill of a patent medicine, an Atlantic liner, a building in the City of London amounts to little and sometimes to nothing; or even five years hence. In fact, those who seriously attempt to make any such estimate are often so much in the minority that their behaviour does not govern the market.

“In practice we have tacitly agreed, as a rule, to fall back on what is, in truth, a convention. The essence of this convention — though it does not, of course, work out quite so simply — lies in assuming that the existing state of affairs will continue indefinitely, except in so far as we have specific reasons to expect a change. This does not mean that we really believe that the existing state of affairs will continue indefinitely. We know from extensive experience that this is most unlikely. The actual results of an investment over a long term of years very seldom agree with the initial expectation. Nor can we rationalise our behaviour by arguing that to a man in a state of ignorance errors in either direction are equally probable, so that there remains a mean actuarial expectation based on equi-probabilities. For it can easily be shown that the assumption of arithmetically equal probabilities based on a state of ignorance leads to absurdities. We are assuming, in effect, that the existing market valuation, however arrived at, is uniquely correct in relation to our existing knowledge of the facts which will influence the yield of the investment, and that it will only change in proportion to changes in this knowledge; though, philosophically speaking it cannot be uniquely correct, since our existing knowledge does not provide a sufficient basis for a calculated mathematical expectation. In point of fact, all sorts of considerations enter into the market valuation which are in no way relevant to the prospective yield…”

“A conventional valuation which is established as the outcome of the mass psychology of a large number of ignorant individuals is liable to change violently as the result of a sudden fluctuation of opinion due to factors which do not really make much difference to the prospective yield; since there will be no strong roots of conviction to hold it steady. In abnormal times in particular, when the hypothesis of an indefinite continuance of the existing state of affairs is less plausible than usual even though there are no express grounds to anticipate a definite change, the market will be subject to waves of optimistic and pessimistic sentiment, which are unreasoning and yet in a sense legitimate where no solid basis exists for a reasonable calculation…”

“Thus the professional investor is forced to concern himself with the anticipation of impending changes, in the news or in the atmosphere, of the kind by which experience shows that the mass psychology of the market is most influenced.”

As Keynes noted, in the face of uncertainty and ignorance, our capacity for anticipation is critical.

Our methodology decomposes anticipation into four challenges, as shown the following matrix:

As the terms are used in this matrix, “threats” are distinguished by a clear causal story that links trends and/or events to specific negative consequences (e.g., for an individual, company, or nation). Threats can be further categorized by the time remaining before negative consequences are expected to occur, and the extent of those consequences – e.g., an imminent existential threat.

In contrast, a “signature” is a signal or set of signals that has a high probability of being associated with a threat. Such probabilities can be derived by multiple means, including the study of history, simulation modeling, deduction from axioms and theories, identification of patterns in current intelligence (e.g., the monthly reports in our Evidence File, which are chronologically organized by issue area in the subscribers-only section of our website), and intuition.

In the matrix above, most individuals and organizations likely spend most of their time in the bottom two quadrants – monitoring signals that are associated with known threats, and identifying new signals that can be used for this purpose.

We would argue, however, that when it comes to successful anticipation, activities in the upper half of the matrix are even more important.

For example, the upper right box of the matrix at first seems an impossible task: Where does one begin when trying to simultaneously discover new threats and signatures?

In the intelligence community, a new methodology in this quadrant is known as “Activity Based Intelligence” or ABI. It integrates a huge volume of data from multiple sources and “analyzes the interactions of people, activities, and events, in order to discover relevant patterns, and characterize those patterns” in order to identify new threats and signatures.

Even newer is the Defense Advanced Research Project Agency’s “KAIROS” initiative, which stands for “Knowledge-directed Artificial Intelligence Reasoning Over Schemas.”

A “schema” is an “organized unit of knowledge about an event or series of events”, that is based on past experience. Schemas are the building blocks of more complex mental models.

The goal of KAIROS is to use artificial intelligence to induce schemas from massive sets of unstructured (and often textual) data, and use them “to enable contextual and temporal reasoning about complex real-world events, in order to generate an actionable understanding fo them and predict how they will unfold.”

Lacking the resources of the world’s military and intelligence services, in our consulting work we have found that organizations can more usefully focus their anticipation efforts in the top left quadrant of the matrix, where they seek to recognize common threat signatures, and then use them as the starting point for identifying specific threats that could be associated with them.


Here are some examples:

• Evolution has primed human beings to automatically allocate attention to changes in their environment that are unanticipated (i.e., surprising), large, and/or rapid. We are less sensitive (at least at first) to changes whose rate is accelerating, though these are no less important.

• As individuals, we are also primed to rapidly recognize indications fear in others, and to stick more closely to our group when we receive signals indicating our environment has become more uncertain (which leads to higher levels of conformity and copying the behavior of others).

• In recent years, complex adaptive systems research has provided us with new threat signatures, including the “critical slowing down” (e.g., rising autocorrelation) of some systems before large changes occur. In social systems, both “Conviction Narrative Theory” (see David Tuckett’s work) and “Narrative Economics” (see Robert Shiller’s work) have highlighted the critical role of narrative in group behavior, and how shrinkage in the number of or support for competing narratives (i.e., the emergence of a dominant narrative), is an indicator of increasing fragility and a precursor of non-linear changes. Finally, Benoit Mandelbrot’s research on fractals, as well as Murray Gell Mann‘s and Didier Sornette’s related work have demonstrated that in many systems the size of changes follows a power-law pattern, with a growing number of smaller changes often indicating the buildup of stresses within a system that eventually gives rise to an exponentially larger change.

Complex adaptive systems like financial markets are characterized by multiple links between causes and effects, which are themselves often time-delayed and non-linear. This is why the identification of specific threats – with clear causal pathways and indicators – is so notoriously difficult, and why many narrow forecasts turn out to be inaccurate.

In our forecasting work over the years, we have found it is more productive to pay attention to the signatures that indicate growing stresses within a complex adaptive system, which can eventually produce changes that are sudden, large, and usually very disruptive.

Here are some examples of signatures in the five issue areas we focus on when developing our macro regime forecasts:

Technology

• New functionality

• Improvements in form/convenience

• Large gains in the performance of existing functionality

Economy

• Rapid growth in all forms of debt (e.g., bank, bond, pension, unfunded entitlements, etc.)

• Substantial change in energy prices
• Slowing demographic and/or productivity growth
• Introduction of significantly different business models and architectures that drive large changes in profitability
• Increasing and increasingly visible inequality
• Rapid environmental change

National Security

• Increasing mismatch between goals and resources, with minimal change in strategy (e.g., Paul Kennedy’s “imperial overstretch”).
• Rising challenger powers
• Increasing system disorder and level of conflict
• Rapid changes in weapons capabilities, along with military doctrine and organization, leading to increasing asymmetry between nations

Society

• Worsening educational and health outcomes
• Declining social mobility
• Increasing middle class frustration
• Declining popular legitimacy of traditional elites
• Rising migration pressures

Politics

• Increasing polarization
• Political shifts away from the center, especially of they are extreme and symmetrical
• Weakened institutional capacity to implement policy to produce improving results
• Political gridlock

Individually or in combination, these signatures can be used to trigger and guide the search for more specific threats when one or more of them are observed. And collectively, they can also provide a “coarse grained” warning that dangerous stresses are building up within the macro system.