The Index Investor
February 2019

Key Takeaways

• Over the next 12 months, our updated regime probability forecasts are as follows: Continuation of Current High Uncertainty Regime (50% up 10% from last month); transition to High Inflation Regime (5%, no change); return to Normal Regime (5%, no change); and transition to Persistent Deflation Regime (40%, down 10%). 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 (Liquidity and BB spreads over Treasuries) point to a reduced level of stress in the macro system compared to last month, others point in the opposite direction (Economic Uncertainty Index, Asset Class Return Autocorrelation, and changes in the price of Gold).

• On balance, we are inclined to put the most weight on the high level of the Economic Uncertainty Index, and the high level of return autocorrelation across major asset classes. This is consistent with the shrinkage of narrative diversity and increase in social copying that occurs as uncertainty increases. One could also argue that the falls in the liquidity and credit spreads are also consistent with an optimistic financial market narrative which concludes that the FED backing away from its planned rate rises and the possibility of a trade deal with China, portends a return to the Normal Regime. The increasing price of gold is inconsistent with this narrative, but gold investors may be taking a broader and more pessimistic perspective on worrisome developments in both the European and Chinese economies.

• Key indicators this month were (1) the US Federal Reserve backed off its plan to increase rates in 2019; (2) The probability of a face-saving trade deal between the US and China increased; and (3) Newly announced Democratic candidates for the US presidential election in 2020 have taken unusually progressive positions.

Asset Class Valuation and Momentum Indicators (@31Jan19)

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 (@31Jan19)

Market Stress Indicators: Forecast Discussion

We view financial markets as a complex adaptive system. The size of changes generated by such a system follows a power law rather than a normal (Gaussian) 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 increases sharply to .86, versus (.09) last month and .86 the month before that. This indicates that financial markets are becoming more ordered and are potentially closer to 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 humans' evolutionary past, when uncertainty increased the probability of survival was enhanced by staying close to a group. All of us 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.

Based on our continued research into the insights this index can provide, in 2019 we will focus on the number of days, in the previous 30 days, that this index was in the top quartile of all values since the index series begins at the start of 1985. We then compare this statistic to the full set of rolling 30-day periods, and calculate its percentile at the end of the most recent month. At the end of January, our rolling 30 day count of top quartile values was in the 89th percentile – a very high level that indicates a high degree of underlying uncertainty, and a macro system that is primed 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. This is a proxy for the level of investor concern about financial system funding liquidity. At the end of January 2019, this spread stood at 1.16%, (the 46th percentile since the series began in 1983), down from last month’s 1.30% spread. This indicates a reduction in market stress.

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. In contrast, rising BB spreads indicate growing investor concern about the consequences of such growth, and the financial distress lower rated companies could experience in an economic downturn. At the end of January 2019, this spread was 2.75% (36th percentile since the series began in 1996), down from the 3.60% spread at the end of December. While this indicates reduced credit market stress, it is still a very low level for 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 “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 3.2%, after a 4.7% rise in December. For all of 2018, the price of gold fell by about 1.5% since the end of 2017. So, on a rough approximation, the political uncertainty premium at the end of January 2019 stood at about 50% (48% - 1.7% + 3.2%), compared to a low of 39% at the end of August 2018. This is another indicator of rising market stress.

To conclude, three of our indicators – asset class return autocorrelation, top quartile values for the Economic Uncertainty Index, and the price of gold – point to rising levels of underlying market stress, which set the stage for a sudden, sharp change in asset class valuations and prices. In contrast, our metrics for tracking liquidity and credit risk have indicated declining levels of market stress over the last month.

On balance, we are inclined to put the most weight on the high level of the Economic Uncertainty Index, and the high level of return autocorrelation across major asset classes. This is consistent with the shrinkage of narrative diversity and increase in social copying that occurs as uncertainty increases. One could also argue that the falls in the liquidity and credit spreads are also consistent with an optimistic financial market narrative which concludes that the FED backing away from its planned rate rises and the possibility of a trade deal with China, portends a return to the Normal Regime. The increasing price of gold is inconsistent with this narrative, but gold investors may be taking a broader and more pessimistic perspective on worrisome developments in both the European and Chinese economies.

Macro Regime Forecast and Implications for Asset Class Values

Summary: Why Did We Change Our Regime Probabilities?

Perhaps the wisest insight I’ve come across in 40 years of forecasting is this quote by the late economist Rudi Dornbusich: “Crises take a much longer time coming than you think, then happen much faster than you would have thought.”

With that quote in mind, I’ve increased the probability that 12 months from now we will still be in the High Uncertainty Regime from 40% to 50%, and decreased the probability that we’ll be in the Persistent Deflation Regime from 50% to 40%.

Three new pieces of information drove this forecast change.

The first was the decision of by the US Federal Reserve to back off its planned sequence of interest rate rises in 2019, out of concern for what it perceives as deteriorating conditions in the global economy (particularly Europe and China). This is likely to hold off a deeper downturn in the United States, at least for a while.

The second was additional indications that the economic downturn in China may be accelerating. This raises the probability that Xi and Trump will find their way to a trade deal that provides face-saving benefits to both leaders. While I don’t think this changes the underlying “correlation of forces” (to use an old Soviet phrase) that point towards growing and extended conflict between the US and China, in the short-term such a deal would temporarily reduce uncertainty and strengthen the “positive narrative” about the economy, the impact of which can already be seen this month in improving liquidity and credit spreads.

The third piece of new information was a spate of Democrats announcing their candidacies for the 2020 US Presidential election. Most notable about them was that most are staking out “progressive” policy positions well to the left of traditional party candidates (at least since George McGovern’s run in 1972). While some of their economic positions poll quite strongly with the public (e.g., reforming health care, making taxes more progressive), many of their social positions do not (e.g., speech restrictions/political correctness, legalizing third term abortion). How these candidates will fare over time with the public, and whether (given current investigations) Donald Trump will run again both remain highly uncertain.

This combination could sustain the High Uncertainty Regime – and thus competing optimistic and pessimistic narratives – for longer than I previously thought possible, and in so doing delay the onset of the Persistent Deflation Regime that I continue to think will follow.

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, based on three month returns to the end of January 2019, this analysis indicates that, over the next 12 months, the balance of expectations across all four regimes was in rough balance, which we consider to be another indication of high underlying uncertainty.

That said, comparing the change between the two three month periods, positive momentum is strongest for both the Normal and the High Inflation Regimes.

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.

As you can see in the following table, based on three month returns to the end of January 2019, this analysis indicates that, over the next 12 months, the balance of expectations across all four regimes was in rough balance, which we consider to be another indication of high underlying uncertainty.

That said, comparing the change between the two three month periods, positive momentum is strongest for both the Normal and the High Inflation Regimes.

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.

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 50% chance of remaining in the High Uncertainty Regime over the next 12 months (an increase of 10% from last month), which will see the commencement of what promises to be a tumultuous US presidential campaign, and what looks increasingly likely to be a so-called “Hard Brexit” with the UK leaving the EU without a transitional agreement.

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 has declined 10% to 40%, as a result of the Federal Reserve’s decision to back away from its planned rate increases and the higher probability of the US and China reaching a trade agreement, as worsening economic conditions put more pressure on Xi Jinping, and worsening political conditions putting more pressure on Donald Trump. Both leaders need a win, and a face saving trade agreement could provide it. Both of these developments will slow the momentum global economic decline that up to now has been accelerating.

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 our current forecast has turned out to be wrong.

How did this happen? What developments did we fail to anticipate?

• At the end of January 2019, the leaders of the world’s three major powers – Xi Jinping, Donald Trump, and Vladimir Putin are all facing weakening economies and declining political popularity. History teaches us that this can lead to increased “foreign adventurism” to distract the public from worsening domestic conditions, as a nation rallies around its leader in a period of heightened external conflict. Should such a conflict develop between China and the United States, or between Russia and one or more European countries, it would generate a sharp increase in uncertainty that would likely cause a sharp economic slowdown and, given high debt levels, speed the arrival of the Persistent Deflation Regime.

• With the UK “crashing out” of the EU – i.e., “Hard Brexit” – looking more likely by the day, it could also be possible that we have underestimated the economic disruption that will result. This could also cause a sharp increase in uncertainty, not the least because it will further undermine public confidence – perhaps on both sides of the Atlantic – in current political leaders’ ability to successfully navigate an increasingly complex and disordered environment. This could set the stage for later electoral wins by populist politicians on either end of the political spectrum, such as Jeremy Corbyn or Marine LePen. This scenario too would increase the probability of entering the Persistent Deflation Regime.

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 and Predicting Uncertainty Shocks: An Update

Back in 2010, The Index Investor published a two-part series on “Understanding and Predicting Uncertainty Shocks”. Since then, a lot more research on the root cause and effects of uncertainty shocks has been published. This month we briefly review highlights from this research, as well as their implications for investors.

At the outset, let’s all agree that the definition of “uncertainty” is notoriously hard to pin down. The broadest definition we’ve seen is “the absence of certainty.” A better definition may be “doubt that produces anxiety and inhibits action.”

As a practical matter, this definition situates uncertainty in the middle of a spectrum that runs from certainty to risk to uncertainty to ignorance. In the face of certainty, there is no doubt, and action is not inhibited. Nor is action typically inhibited in risky situations, where the full range of possible outcomes, along with their consequences and probabilities, are all believed to be well understood. Strangely enough, it is also usually the case that ignorance does not inhibit action. As John Maynard Keynes noted in Chapter 12 of The General Theory of Employment, Interest, and Money, in the face of ignorance we typically adopt “shared conventions” (i.e., common assumptions) that enable action. As Keynes noted, the most familiar of these “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.”

More so than risk or ignorance, it is uncertainty that is usually the greatest obstacle to action. More specifically, doubt and anxiety typically peak when we confront situations in which we do not understand the full range of possible future outcomes, and/or their consequences, and/or the relative likelihood that they will occur.

The extent of uncertainty we confront today is arguably greater than ever before. There are two main reasons for this.

The first is the inexorable tendency of systems (including organizations) to become more complex over time. Thanks to the second law of thermodynamics, in the absence of new injections of energy and/or information, open systems will inexorably become more disordered over time. Increasing disorder creates problems, and we use energy and information to create solutions to them. Yet these new solutions are often more complex than the ones that preceded them, and thus lead to new problems, necessitating even more complex solutions, whether technological or organizational.

As complexity accumulates, it becomes ever more difficult to fully explain the system effects we observe, because of the existence of multiple interacting causes, some of which produce non-linear and time-delayed effects. In the absence of causal understanding, prediction becomes more difficult, or devolves to associational/statistical reasoning, as in the case today’s deep learning methods whose underlying logic is often difficult or impossible to explain.

The second reason uncertainty is higher today than ever before is the dramatic increase in global connectivity over the last decade. While the increase in complexity has made the behavior of many systems more non-linear, the exponential increase in connectivity and the speed with which information flows across these connections has made many socio-technical systems more tightly coupled (both internally and externally) than ever before. Thirty-five years ago, in “Normal Accidents”, Charles Perrow famously observed that unexpected failure pathways (and the uncertainty they create) increase exponentially as system complexity and connectivity increase.

Recent Research

Research into uncertainty shocks has in recent years been greatly aided by the development of specific indexes for measuring uncertainty, based on the pioneering research of Baker, Bloom, and Davis in “Measuring Economic Policy Uncertainty”.

“Really Uncertain Business Cycles”, by Bloom et al uses a model of the economy with heterogeneous agents (rather than a single representative agent) to find that “uncertainty shocks can generate drops in GDP of around 2.5%”. Increased uncertainty also initially reduces the initial positive impact of policy actions taken to stimulate the economy in a recession.

In “The Nonlinear Effects of Uncertainty Shocks”, Jackson et al from the Federal Reserve Bank of St. Louis find that “when uncertainty is relatively low, fluctuations in uncertainty have small, linear effects. [However] in periods of high uncertainty, the effect of a further increase is magnified, and have a more pronounced effect on real economic variables…This is due to uncertainty propagating through both reductions in consumption spending and business investment and employment.”

“The Macroeconomic Impact of Financial and Uncertainty Shocks”, by Caldara et al finds that financial shocks are distinguishable from uncertainty shocks; however the latter “have an especially significant economic impact when they trigger a tightening of financial conditions, as in the Great Recession.” These same issues are also addressed in another paper, “The Real and Financial Impact of Uncertainty Shocks”, by Alfaro et al, which also concludes that uncertainty shocks can be particularly damaging when financial conditions are fragile.

In “Policy Uncertainty and Aggregate Fluctuations”, Mumtaz and Surico find that uncertainty about taxes and government debt have a “large and persistent effect on output, consumption, investment, consumer confidence, and business confidence.” In contrast, uncertainty about government spending and monetary policy has a significantly smaller impact on the real economy.

In “Uncertainty Traps”, Fajgelbaum and his co-authors show “how large by apparently short-lived uncertainty shocks can generate long-lasting recessions.” As noted by other researchers, an increase in uncertainty generates a fall in consumption and investment. “Since agents learn from the actions of others, information flows slowly in times of low economic activity, uncertainty remains high, further discouraging consumption and investment” in a self-perpetuating cycle.

Finally, in “The Evolving Impact of Global, Region-Specific, and Country-Specific Uncertainty”, Mutaz and Musso find that “common global uncertainty plays a primary role in explaining the volatility of inflation, interest rates, and stock prices.” This is similar to the conclusions in a recent IMF working paper “Media Sentiment and International Asset Prices”, which found that “not all news sentiment is alike. Changes in global news sentiment have the largest impact on equity returns around the world, which does not reverse in the short run.”

Similarly, in their new paper, “What are Uncertainty Shocks?”, Kozeniauskas et al begin by observing that “one of the primary innovations in modern business cycle research is the idea that uncertainty shocks drive aggregate fluctuations. A recent literature demonstrates that uncertain shocks can explain business cycles, financial crises, and asset price fluctuations with great success. But the measures of uncertainty used are wide ranging…If these disparate measures are really capturing a common underlying shock, what is it?”

To answer this question, they note that, “people become uncertain after observing an event that makes them question future outcomes. That raises the question: What sorts of events can make agents uncertain in a way that shows up in a wide variety of disparate measures?” They note that the fundamental driver of uncertainty shocks across multiple metrics is unexpected changes in the macro political economy (including perceived “disaster” or tail risk). This is also the exact focus of analysis and forecasting here at The Index Investor.

What Causes Uncertainty Shocks?

The economist Rudiger Dornbusch famously noted that, “crises take a much longer time coming than you think, and then happen much faster than you would have thought.”

Today’s hyperconnected world provides ample evidence of the accuracy of this insight. The more connected we become, the greater the potential for rapid shifts in opinion and behavior that create new strategic risks for many investors and organizations.

In order to better anticipate these sudden and substantial shifts, investors should understand the underlying processes that are at work. In practice, these often operate together, with positive (amplifying) feedback loops between them.

Our starting point is Daniel Kahneman’s distinction (made in his book, “Thinking Fast and Slow”) between two modes of thought.

“System 1” is rapid, instinctive, based on association, and generally unconscious. It generates fast conclusions and emotions that prime us for actions (e.g., fight or flight) that, from an evolutionary perspective, have been highly adaptive for the survival and success of the human species.

In contrast, “System 2” thinking is slower, deliberate, logical, and conscious. Because it is also more effortful, most human cognition is based on System 1, not System 2.

Rapid shifts in popular perception, opinion, and behavior that generate uncertainty shocks originate in both System 1 and System 2.

In the case of System 1, the most primal driver is human beings’ capacity to rapidly transmit fear, through a variety of non-verbal means, including facial expressions and odor.

A slightly more evolved System 1 response is the automatic increase in our desire to avoid disagreement or abandonment by a group that occurs when perceived uncertainty or adversity increase. In the evolutionary sense, this is adaptive behavior. However, in today’s environment it is the root cause of excessive conformity and groupthink in periods of high uncertainty, which, as we will describe below, reduces the diversity of narratives in a system and sets the stage for sudden, substantial change.

Conscious and effortful System 2 processes can also contribute to uncertainty shocks.

In “The Evolution of Social Learning and Its Economic Consequences”, Bossan et all demonstrate how over the course of evolution, social learning – deliberately imitating others – has became dominant. They note that this has had three critical effects. “First, for better or worse, the decisions of social learners are more exaggerated than those of individual [trial and error] learners. Second, social learners react with a delay to changes in the environment. Third, the behavior of social learners can become more and more detached from reality.”

In “Social Learning Strategies Regulate the Wisdom and Madness of Interactive Crowds”, Toyokawa et al ask, “why do groups of individuals sometimes exhibit collective wisdom and other times maladaptive herding?” They find that “challenging tasks [which generate greater uncertainty] elicit more conformity among individuals, with rates of social copying increasing with group size, leading to a higher likelihood of herding among large groups confronted with high uncertainty.”

Similar conclusions have been reached by two other research teams, including Escudero and Polavieja in “Adversity Magnifies the Importance of Social Information in Decision Making” and Rendell et al in “Why Copy Others? Insights from the Social Learning Strategies Tournament”.

Researchers have also found that while “loss aversion” is a normal human response when the result of a decision will remain private, when those results will be visible to others gains become more important (see, “Interdependent Utilities: How Social Ranking Affects Choice Behavior” by Bault et al). Combine this with human’s optimism and confirmation biases, and you can see why investors have a bias towards believing in positive market and macro narratives despite accumulating evidence that tells a more negative story.

Still other research finds that a relatively small fraction of agents in a population (about 10% or more) can produce and sustain significant opinion shifts (for example, see, “Social Consensus Through the Influence of Committed Minorities” by Xie et al, and “Committed Activists and the Reshaping of the Status Quo Consensus” by Mistry et al). Put differently, when a relatively small percentage of people in a group switch to a different narrative, or when an even smaller but more highly connected group does this, sudden and substantial changes in behavior and asset values – a classic uncertainty shock – can result.

Broadly speaking, decisions made by System 2 are based on three types of input: private information not available to everyone, public information, and social information.

As described in the work of a number of researchers (e.g., David Tuckett, Robert Shiller, Paul Ormerod, and Marvin Cohen), in the face of situations characterized by high complexity and uncertainty, humans tend to decide and act not on the basis of detailed quantitative analyses (which are impractical and often impossible), but rather on the basis of so-called “conviction narratives” or, more simply, the stories we tell ourselves and others to justify our beliefs and actions on the basis of relatively simple causal models.

As system complexity increases and causation becomes exponentially more difficult to fully understand, these simplified narratives rest on progressively more fragile assumptions (for more on this, see Gary Klein and Robert Hoffman’s papers on “Explaining Explanation”).

These narratives produce varying levels of certainty (or “conviction”), along with similarly varying degrees of emotional excitement or anxiety regarding the accuracy of our perceptions, the appropriateness of our proposed actions, and the likelihood they will achieve our goals (e.g., see: “News and Narratives in Financial Systems: Exploiting Big Data for Systematic Risk Assessment” by Nyman et al, and “The Economics of Radical Uncertainty” by Paul Ormerod).

From the aggregation and sharing of these individual conviction narratives and the net impact of their associated levels of emotional valence and arousal there emerges the phenomenon Keynes called “animal spirits," or what we more generally call the level of public confidence (and its complement, the level of uncertainty).

People have varying degrees of conviction about the accuracy and importance of the private information they possess. As we have noted, in the face of high uncertainty, or when they are members of large groups (which naturally arise in our age of hyper-connectivity) people will often weigh social information much more heavily than private information when constructing narratives to explain the past and forecast the future.

The surprising result is that increasing uncertainty therefore tends to produce a reduction, rather than an increase, in the number of competing narratives in a population, which makes a system more susceptible to sudden and dramatic change.

Under these circumstances, an unexpected public signal – for example, the announcement that a high-ranking government official has been indicted for a crime, or that a key party is abandoning a coalition government – can quickly undermine fragile but widely believed narratives, and cause dramatic changes in consumer and business confidence and behavior.

In a hyper-connected world, this result can also be produced endogenously, if, as noted above, a relatively small percentage of the population changes its view due to accumulating private information. Indeed, this is the underlying causal logic guiding the actions of those who attempt to change public opinion by targeting information (be it press releases or fake news) at highly connected people (e.g., “influencers”) in social networks.

Can We Anticipate Uncertainty Shocks?

After four years on the Good Judgment Project, there isn’t any doubt in my mind about that. The more important questions are how far in advance we can do this, at what level of aggregation, with what degree of accuracy, and whether and to what extent we act on our forecasts in a timely manner.

Let’s look first at the time horizon question. Perhaps the oldest technique for anticipating uncertainty shocks in financial markets is calculating broad asset class valuation ratios, and comparing them to historical averages. Closely related to this is the work of Didier Sornette and others on the identification of emerging financial market bubbles. However, when these techniques lead to the conclusion that an asset class is severely overvalued (and therefore likely to produce a future uncertainty shock), you will always be told that, “this time it’s different.” Reinhart and Rogoff looked at eight centuries of data and wrote a book with this title to remind you that it’s not.

The larger problem, as Keynes said, is that, “the market can stay irrational longer than you can stay solvent.” To which professional asset managers often add, “or sustain the career risk of being underweight in an asset class when both its returns and its overvaluation is increasing.”

That has led to the search for tools and methods that would enable a manager to more accurately time the arrival of uncertainty shocks. The simplest of these are based on differential returns between assets that perform best under different regimes (e.g., persistent deflation or high inflation). Other simple tools use debt market credit and liquidity spreads. We use all of these.

More advanced methods either make use of larger amounts of data and/or advanced methods of extracting insights from it. A simple example of this is our use of the month-to-month autocorrelation of returns across multiple asset classes, which is an indicator that research has found to be useful (in some, if not all cases) when predicting large changes in the behavior of complex systems.

Various machine learning techniques are now widely used to extract insights from the large amounts of data that is available today. For example, text data from traditional and social media feeds is mined to identify emerging issues and measure changes in various types of sentiment (see, for example, Richard Nyman’s excellent thesis, “An Algorithmic Investigation of Conviction Narrative Theory”). In some cases, insights derived from textual data are converted into standardized metrics, like the Economic Policy Uncertainty Index we use in our analysis of market stress levels.

However, these techniques are still limited by a number of important shortcomings. For example, text mining usually depends on the number of times certain words or phrases appear in the data set being analyzed, and/or the frequency of their use by agents in a network (e.g., what CEOs of public companies said in this quarter’s earnings conference calls with investment analysts). Simple frequency based approaches require that word or phrase use reach a certain level before being deemed significant. This creates the classic tradeoff between Type 1 and Type 2 errors, or false alarms and missed alarms. If the threshold word or phrase frequency trigger is set low, there will be a lot of false alarms; if it is set high, there will be more missed alarms.

Tracking word or phrase usage by important individuals (or high value nodes in network science terms) is one approach that has been used to improve the Type 1 vs. Type 2 tradeoff. However, placing more weight on fewer sources runs its own set of risks, from deliberate deception to lack of independence (e.g., as when many high value nodes are basing their opinions on the same set of flawed information inputs).

A far more fundamental limitation of machine learning techniques based on natural language processing is the subject of “The Book of Why”, by Dr. Judea Pearl, one of the preeminent scientists in the field of artificial intelligence. He posits a hierarchy of reasoning skills. At the lowest level is association or correlation. Today’s machine learning methods can recognize complex patterns in large data sets better than ever before. Yet they fundamentally operate in a statistical rather than model-driven mode.

The next highest level is causal reasoning that clearly connects causes and effects. This is something that today’s artificial intelligence techniques are far from mastering, particularly in the case of complex adaptive systems where the rules governing allowable actions are constantly evolving.

As we noted in last month’s feature article on the critical difference between threats and threat signatures, making AI more “context aware” (i.e., able to combine data with existing knowledge), and capable of creating abstractions and using them to reason across different situations (i.e., “transfer learning”) is the subject of multiple initiatives in both the private (e.g., computational narrative intelligence and story generation to explain data) and public sectors (e.g., DARPA and IARPA projects like AIDA, KAIROS, CREATE, and ICARUS).

At the top of Pearl’s hierarchy sits counterfactual reasoning, which builds on associational and causal methods and enables both retrospection and imagination. For example, the goal of IARPA’s FOCUS project is to substantially improve counterfactual reasoning methods.

Given the limitations of current artificial intelligence and other machine learning technologies, predicting uncertainty surprises, particularly in complex adaptive systems over longer time horizons, remains an activity where human cognition still reigns supreme.

Another important issue when predicting uncertainty shocks is the focus on your analytical efforts. More specifically, how do you divide your time between forecasting discrete variables (i.e., events) and continuous variables (i.e., trends)? For example, consider a company with a substantially overvalued stock price. In this case, a specific event – an earnings miss, a failed transaction, or publication of a critical research report – will likely trigger an uncertainty shock and a sharp fall in the company’s valuation. The key forecasting question is when one of those events is most likely occur.

Forecasting macro uncertainty shocks is different. To be sure, when they occur the media will always identify proximate events that they deem to be causal. They may well be, but only in the very narrow sense of the last grain of sand that causes a sandpile in which stress has been building to give way in a large slide.

The forecasting problem is that, in an integrated multilevel complex adaptive system characterized by constant changes in technological, economic, national security, social, and political conditions, there are an unknowable number of specific events that could trigger an uncertainty shock that produces large changes in financial asset prices. Trying to forecast them all is impossible.

Instead, our methodology focuses on forecasting the evolution of continuous variables, and how close we are to critical thresholds that will trigger uncertainty shocks and regime changes in the global macro system.

For a specific example of a forecast that was based on this approach, see our May 2007 article, “Why We Don’t Sleep Well These Days, and Moving Into Cash Looks Like a Good Idea.”

Can We Hedge Exposure to Uncertainty Shocks?

In broad terms, there are three strategies for hedging exposure to uncertainty shocks.

The first is to formulate “robust” plans that maximize the probability of achieving your goals across a wide range of possible future scenarios. In an investment context, the application of this principle leads to the recommendation to structure portfolios that are broadly diversified across a wide range of asset classes.

The second strategy is to ensure that a system or organization or portfolio has an adequate level “resilience” in case robustness fails. In investment terms, this takes the form of liquid assets; at a company this is more broadly interpreted as redundancy and hardening of critical systems, organizational techniques like critical incident teams and crisis training, and foregoing maximum efficiency in favor of maintaining a certain degree of “slack” resources that can be quickly deployed following a shock.

The final strategy is maximizing the adaptive capacity of an organization or portfolio. If robustness fails, and resiliency absorbs the initial negative impact, adaptation drives a quick return to previous levels of performance. Indeed, along with effectiveness and efficiency, adaptability is one of evolutions three core performance metrics, that apply to every organism, from the smallest bacteria to the largest organizations and nations.

There are three key elements to adaptability. Skill in anticipatory thinking continuously identifies potential threats, and enables the preparation and prioritization of options for responding to them. Tools like the “Met Office Model” we use at both the Index Investor and Britten Coyne Partners enable organizations to manage the complex dynamic of the speed at which threats are developing and the remaining time required to adequately respond to them. And developing organizational capacity for problem detection, solution design, and rapid implementation (often collectively termed “agility”) ensures that the adaptive process is continuous and effective.

So, to sum up:

(1) Macro uncertainty shocks are caused by individual and collective forces that are deeply rooted in our evolutionary past, and have been supercharged in our present age of high complexity and hyper-connectivity. As former Bank of England Governor Mervyn King has observed, we now live in “an age of radical uncertainty”.

(2) Macro uncertainty shocks have substantial and long-lasting effects, and their full impact usually occurs with a time lag.

(3) Macro uncertainty shocks can be anticipated, not by trying to forecast the exponentially large number of discrete events that can be their proximate cause, but rather by focusing on the trends that can push complex adaptive systems beyond critical thresholds (i.e., “tipping points”) and trigger sudden non-linear effects and regime change.

(4) By focusing on robustness, resiliency, and adaptability, we can hedge our exposure to uncertainty shocks.