The Index Investor
May 2019

Key Takeaways

Many asset classes remain significantly overvalued. Timber and foreign developed market equities are exceptions.

Most market stress indicators remain weak. Asset class return indicators imply that aggregate expectations favor a return to the Normal Regime. We believe this conventional wisdom is wrong.

We have not changed our estimated probability that 12 months from now the macro system will still be in the High Uncertainty Regime (60%) or the Deflation Regime (30%). At a 36-month horizon, the regime probabilities are Deflation (40%), High Uncertainty (25%), Normal Regime (25%) and High Inflation Regime (10%).

It is critical to remember that the negative economic impact of high uncertainty only occurs with a lag. Moreover, high uncertainty also promotes higher conformity and more social copying, and thus a reduction of the number of narratives underlying people’s views. In sum, we believe the macro system today is highly fragile and primed for a large downside move, which will likely trigger a negative feedback loop that will cause uncertainty to increase even more.

Asset Class Valuation and Momentum Indicators (@30April19)

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 (@30Apr19)

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 was low in April as it was in March. This indicates that financial markets remained less ordered, and thus potentially 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 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 are focusing 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 April, our rolling 30 day count of top quartile values was in the 7th percentile – a significant drop from last month, which indicates the macro system is still not strongly 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 April 2019, this spread stood at 1.20%, (the 47th percentile since the series began in 1983), unchanged from last month. That said, in April of 2018 this spread was 1.00% - from this perspective, market liquidity stress has slowly but significantly increased over the past year.

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 April 2019, this spread was 2.26% (16th percentile since the series began in 1996), down from from 2.39% last month. This is a very low level for this late in what is already an exceptionally long period without a serious economic downturn, and where qualitative warning signs of danger ahead abound (see both this months Evidence file, and the cumulative Evidence file on our website). As such, the currently low BB spread likely indicates the further buildup of hidden stresses in credit markets.

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 fell by (0.46%). Since the end of 2017, it is now down by (0.86%). Therefore, on a rough approximation, the political uncertainty premium at the end of February 2019 stood at about 48% (48% - .86%) compared to a low of about 39% at the end of August 2018. On balance, this is another indication of falling market stress.

This is a very low level for this late in what is already an exceptionally long period without a serious economic downturn, and where qualitative warning signs of danger ahead abound (see both this months Evidence file, and the cumulative Evidence file on our website). As such, the currently low BB spread likely indicates the further buildup of hidden stresses in credit markets.

Macro Regime Forecast and Implications for Asset Class Values

In response to subscriber requests, we are adding a 36 month regime forecast to our existing 12 month forecast. The logic is that, in a complex evolving system like global macro, a longer forecast horizon gets beyond the “detection range” of algorithmic forecasting approaches, and therefore raises probability that a manager/investor can gain an edge in identifying emerging threats and opportunities.

That said, because evolving (i.e., “non-stationary”) complex systems populated by highly connected human agents are also capable of sudden non-linear changes (with which are hard for algorithmic approaches to predict), we are also keeping our 12 month forecast.

This is consistent with what is perhaps the wisest insight I’ve come across in 40 years of forecasting -- 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.”

12 Month Forecast

While there were certainly new pieces of high value information this month (as you can see later in this report), their cumulative weight was not sufficient to cause a change in our 12 month regime probabilities.

There were more indications that the global economy’s three demand motors (the US, EU, and China) are simultaneously weakening, which is a distinctly negative sign that could, given high debt levels, lead us into the deflation regime. However, as discussed in previous issues, housing has a heavy weight in the US CPI, and we have yet to see significant softening of prices in this sector; if uncertainty remains high, and GDP continues to weaken we almost certainly will. But we’re not there yet.

On the political front, the evidence continues to mount that the Democratic Party will struggle to avoid divisive (and very visible) internal battles in its presidential nomination process. At the same time, there is growing evidence (e.g., in the research of professor Lilliana Mason) that the growing alignment between political party identity and multiple other sources of identity (e.g., racial, economic, education, religious, regional, and ideological) has made party members increasingly intolerant of each other, and thus less likely to switch their vote due to leadership failures (e.g., Donald Trump’s behavior) or policy changes (e.g., the reversal of historical Democratic and Republican positions on immigration). This raises the probability that Donald Trump could be reelected in 2020, which would likely extend the current period of high uncertainty.

With respect to the high inflation regime, rising tensions between the United States and Iran could lead to armed conflict and attempt to block the Strait of Hormuz, which would sharply raise oil prices. A prolonged rise could, as in the past, give a strong boost to inflation while also reducing economic growth. However, given the dependence of China on oil from the Gulf, and the redeployment of more US military assets to the region, not to mention increased domestic oil production in the United States and the conflict between the Iran and the Sunni regimes in the Middle East, it seems unlikely that an oil price rise would be sustained long enough to substantially increase inflation.

36 Month Forecast

There are, however, two other new examples of the type of supply side shock that could cause a sustained increase in inflation. The first is the worsening of the Ebola hemorrhagic fever outbreak in the Democratic Republic of the Congo, which has a case fatality rate of over 60%. Attempts to control the outbreak are being limited by growing violence and the breakdown of public order that is putting medical personnel at increasing risk. The second example is the rapid increase of hemorrhagic African Swine Fever in China, that has killed or forced the culling of a substantial part of the world’s largest population of pigs, and a concomitant sharp rise in global pork prices. While these are both localized outbreaks (though the Swine Fever one has more potential to spread), they remind us of the range of potential supply side shocks that could lead to sustained higher inflation.

With respect to the Deflation Regime, the new Strategic Analysis from the Levy Economics Institute (see this month’s Evidence File) soberly reiterates that, “for better or worse, the structural problems in the US economy – with some important exceptions -- have not changed significantly over the last two-and-a-half decades.” They include, “(1) weak net export demand [with the exception of the balance on petroleum and gas trade]; (2) fiscal conservatism; (3) increasing income inequality; and (4) financial fragility…Importantly, the situation on most of these fronts is getting worse.”

Elsewhere, the IMF’s latest Global Financial Stability Review was the latest analysis to highlight the dangers posed by the global economy’s high debt levels. Astute commentators like William White (former chief economist at the Bank for International Settlements) suggest that a substantial amount of this debt is effectively unserviceable in a low growth economy (caused by declining demographics and low productivity). As such, provisions should be made for orderly debt reductions (e.g., via write-offs or debt/equity conversions). However, this seems unlikely given current levels of political polarization not to mention the extreme litigiousness of some of the funds that hold the riskiest debt. This raises the probability of an extended period of debt deflation in the case of a severe and prolonged economic downturn (which itself seems likely, given monetary policy stuck at the zero lower bound for interest rates, and fiscal policy hamstrung by political conflict).

The argument that we will have returned to the Normal Regime 36 months hence rests, in our view, of two non-exclusive possibilities. The first is a worsening of the conflict between the United States and China, possibly accompanied by more aggressive actions by Russia and Iran. The realization that the West faces a new and dangerous external threat that is likely to persist for years could lead to and improved domestic political consensus that would make it possible to address the fundamental structural issues that have increasingly hobbled Western economies and politics. The second possibility is that the Democratic Party in the United States avoids what Barack Obama memorably called the “risk of a circular firing squad” and elects a president on the basis of a policy platform that garners the support of the “moderate middle” whose votes increasingly seem to depend on the balance of fear and revulsion they feel towards both parties’ extremes.

Forecast Methodology

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 and equities suffer significant declines; (3) High Inflation (which we deem 5% or more, year-on-year), where commercial property, real return bonds and other traditional hedges are favored; and (4) Persistent Deflation (a year-on-year decline in the US CPI), which up to now has only been seen in Japan, and in which the relative performance of different asset classes remains uncertain, but will likely favor high quality bonds and the consumer staples equity sector.

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 last month, this analysis indicates that 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.

In our methodology, we classify new information as significant and highly valuable if either it (1) is an “indicator”, which reduces or increases our uncertainty about the value of a parameter in our mental model for making sense of the dynamic macro system, or (2) it is a “surprise” which increases our uncertainty about, and causes us to revaluate the structure of our mental model.

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 complex adaptive 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. A very coarse-grained reading of history suggests that these states evolve in a predictable cycle, from ordered/cooperative, to disordered/cooperative, to disordered/conflicted, to ordered/conflicted.

We believe that the system is currently in its most uncertain state, characterized by high degrees of underlying disorder and social conflict, both domestically and internationally. Beyond some point, intensifying conflict eventually increases the degree of order in the system. That appears to be happening now, via the increasing conflict between China, Russia, and Iran and the United States and other Western nations.

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 April 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 an equally sharp economic slowdown and, given high debt levels, speed the arrival of the Persistent Deflation Regime.

• As described in this month’s feature article, while remote, a supply side shock of some type could produce a sudden increase in inflation – the most likely scenario being a reduction in oil supplies due to a sudden intensification of the simmering conflict between Iran and Saudi Arabia.

• While we believe it is highly improbable, we can envision a scenario in which for a range of possible reasons, both Xi Jinping and Donald Trump leave their current roles, and are replaced by leaders who are more committed to lessening conflicts both between China and the United States and in the international system as a whole. This would likely provide a strong boost to confidence (and thus lead to an equally strong reduction in uncertainty). Whether this would also create an opening for a reduction in domestic political conflict in the United States, and thus progress on policy reforms to address weak growth and rising inequality isn’t clear.

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.

Across the social sciences the greatest challenge confronting researchers and practitioners is explaining and/or predicting the behavior of complex adaptive systems, which have many parts that interact in non-simple ways (e.g., multiple possible causes for an effect, feedback processes and non-linear effects, and time delays between causes and effects).

In such systems, behavior emerges from these complex interactions, not just from the rules followed by any individual agent. Moreover, as the agents that populate these systems constantly adjust their beliefs and behavior to achieve their (sometimes conflicting) goals, the structure of a complex adaptive system itself constantly evolves; more technically, such systems are “non-stationary” data-generating processes, in which an understanding of the past does not automatically confer an ability to accurately predict the future. In sum, complex adaptive systems are organic, not mechanistic, and are far more likely to produce outcomes whose distribution is best described by a power law, rather than the more familiar bell curve.

Explanation and prediction challenges become exponentially more difficult as more than one complex adaptive systems are interconnected. For example, our model of “global macro” includes changes in six key areas: technology, the economy, national security, society, politics, and financial market structure and behavior. We also recognize the potential for substantial “wildcard” effects from less predictable changes in the areas of health and infectious disease; energy and the environment; and cyber and solar events.

There are a number of different approaches that can be used to address the challenge of predicting the behavior of complex adaptive systems.

One approach is directly modeling them (albeit at a high level). For example, Monte Carlo and System Dynamics are top down quantitative modeling approaches based on underlying causal beliefs. The former captures the impact of variation in the values of key variables, while the latter focuses on the impact of feedback effects and stock/flow constraints (e.g., a bathtub that eventually catastrophically overflows if the rate of water inflow exceeds the rate of water outflow). In contrast, agent-based modeling is a bottom-up technique that seeks to identify the outcomes that emerge from the interaction of agents who follow a limited number of assumed goals rules to guide their behavior. In some cases, aspects of these techniques are combined (e.g., see, “Agent-Based Stock-Flow Consistent Macroeconomics: Towards a Benchmark Model” by Caiani et al, and papers by Giovanni Dosi, Didier Sornette, Doyne Farmer, Brian Arthur, Xavier Gabaix, and Car Hommes – to name just a few of the growing number of researchers in this area).


Another family of quantitative approaches to the prediction challenge is associative, rather than causal, and based on statistical methods. These include a wide range of econometric techniques that seek to reduce the dimensionality of the problem, for example by identifying a limited number of factors whose variation, at least in the past, can account for a substantial portion of the variation in a much larger number of outcomes (e.g., using Fama-French factors to forecast equity returns).

Finally, uncertainty about the nature of relationships between the variables in the system being modeled, and/or multiple conflicting goals can often be managed through so-called “ensemble modeling”, which combines the results of multiple runs of different models to estimate the full range of possible system outcomes.

In contrast to the hypothesis testing method that underlies econometrics, “machine learning” approaches (including deep learning artificial intelligence techniques) seek to maximize predictive accuracy without making any assumptions about the underlying causal model. In effect, these methods analyze historical or synthetic data to create extremely complex statistical models to predict a target outcome from a very diverse set of inputs. A key issue with such models is whether end users will trust their outputs if they cannot easily understand how they were derived (hence the growing popularity of “explainable AI”).

Qualitative methods are also used to predict the behavior of complex adaptive systems. Perhaps the most familiar is the use of historical analogies (e.g., “applied history” techniques, or the case method). Another widely used approach is the scenario method, which derives alternative future narratives from the interaction of relatively predictable trends and a limited number of critical uncertainties.

At the Index Investor, we use an approach that combines some of these techniques. Fist, we specify our forecasting goal: accurately estimating the probability that the global macro system will be in one of four regimes at some point in the future (e.g., in 12 and 36 months). Based on our analysis of economic and financial market history, we define these regimes as normal times (which equities and high yield debt deliver the highest relative returns); high inflation (when inflation linked bonds, property, and commodities like gold and timber perform best); high uncertainty (usually a transitory regime in which short term government securities, gold, and the Swiss Franc usually outperform); and persistent deflation, as we have seen in Japan over the past thirty years (where long term government and high quality corporate bonds and consumer staples equities should outperform). These four different regimes can be thought of as “macro factors” that drive the relative returns on broadly defined asset classes.

We assume that the emergence of these regimes reflects the interaction of five six macro drivers, including changes in technology (including healthcare and education as two critical “social technologies”); the economy; national security; social values, beliefs, and behavior; politics; and the structure of financial markets. As noted above, we also recognize the potential for “wildcard” effects on regime probabilities from changes in the areas of health and disease; energy and the environment; and cyber and solar events.

Critically, our model assumes that the effects produced by these drivers occur in a rough chronological pattern (albeit with many feedback loops), in which changes in technological possibilities drives changes in the economy and national security (with an interaction between those two), which produce social changes that have a significant impact on political changes. These interact with changes in the structure of financial markets (e.g., increases algorithmic trading, increased connectivity between global markets, new products like ETFs, increased assets committed to private capital strategies, etc.) to determine the macro (broadly defined) regime we are in at any point in time.

To reduce the complexity of these drivers, we employ a scenario approach, based on the interaction of two critical uncertainties. This generates four possible outcomes for each of our five key drivers (technology, the economy, national security, society, and politics). Mathematically, this simplifying approach generates a still unwieldy 1,024 (4 to the 5th power) possible scenarios – without considering the wildcards or changes in financial market structure.

To further simplify our forecasting problems, we employ the Bayesian concept of Likelihood. Working either backward in time from future regimes through different drivers (i.e., “prospective hindsight”) or forward in time starting with technology drivers, we ask whether, given our starting scenario, one scenario in the next set of drivers is significantly more likely than others. For example, slow economic growth and worsening inequality make it less likely that immigration problems will be resolved and social capital renewed. To be clear, this method does not make causal assumptions, as all of our scenario outcomes emerge from complex processes that can at best be imperfectly understood. Rather, our approach is associative, and is based on our estimate of the extent to which a scenario outcome for a given driver is more likely to be observed than others for that driver assuming a specific scenario for another driver occurs.

We use this method to construct a limited number of logically and chronologically coherent pathways through the 1,024 possible scenarios that lead to different regime outcomes. Finally, given variations in the likelihood ratios, some pathways will appear more and less uncertain that others, which in turn helps us to focus our information collection efforts.

The graph below presents a visual overview of this methodology. The red arrows show a narrative pathway proceeding forward in time from a technology scenario, while the green arrows show a pathway that proceeds backwards in time from an assume future regime outcome. The dashed lines represent weak likelihood ratios.

Let’s now turn to the current assumptions we are using in our analytical model, beginning with the two uncertainties that underlie our four scenarios for each macro driver:

Technology

• Fast versus slow development and deployment of automation and artificial intelligence technologies.

• Strong versus weak productivity growth in healthcare and education, the two key “social technologies” that now account for almost 25% of US GDP.

• Faster versus slower growth in aggregate demand.

• Increasing or declining inequality.

• More cooperative or more conflict driven US-China relations.

• Increase versus either no change or decrease in China’s military power relative to the United States and its allies.

• Consensus solution to immigration problem is implemented, or no solution is arrived at and conflict over this issue worsens.

• Social capital increases, or continues to decrease, as it has in recent years.

• The relative strength of the center strengthens or weakens.

• The popular legitimacy of government institutions strengthens or weakens.

1. Fast development and deployment of automation and artificial intelligence technologies will be associated with higher demand growth, all else being equal, via productivity improvement.  Slower D&D of A&A is associated with slower demand growth.

2. Faster healthcare and education productivity improvement (and hence reduced cost/price and pressures and improved outcomes) is associated with reduced inequality. Slower productivity improvement in these areas is associated with increased inequality.

3. All else being equal, slower development and deployment of automation and artificial intelligence technologies in the US is associated with weakening of national power vis-a-vis China. Faster D&D of A&A maintains or improves the balance in favor of the US.

4. Falling inequality in the United States is associated with a reduction in the level of conflict in the US-China relationship, which in part based on the search for an external “other” to blame for worsening domestic conditions. Rising inequality is associated with a higher level of conflict.

5. Faster demand growth and reduced inequality are associated with reduced conflict over immigration (which, as in the case of China, provides an external “other” to blame for worsening domestic conditions).  Slower growth and increasing inequality are associated with increased conflict over immigration issues.

6. Weakened US national power vis-a-vis China is associated with increasing social capital in the face of a potentially dangerous external threat. Increasing national power vis-à-vis China is not associated with increasing social capital.

7. Weakened US national power vis-a-vis China is associated with increasing institutional legitimacy (assuming this leads to higher degrees of cooperation and better institutional functioning in the face of a strengthening external threat). Increasing national power vis-à-vis China is not associated with increasing institutional legitimacy.

8. Reduced inequality is associated with increasing social capital. Worsening inequality is associated with declines in social capital.

9. Strengthened social capital is associated with the relative strengthening of the political center. Weakened social capital is associated with further weakening of the political center.

10. Reduced conflict over immigration is associated with strengthened institutional legitimacy.  Continued or worsening conflict over immigration is associated with further weakening of institutional legitimacy.

• Fast Automation and AI Development and Deployment

• Strong Productivity Growth in Healthcare and Education

• Faster demand growth

• Declining inequality

• Improving US-China relations

• No or negative change in China’s relative power

• Consensus solution to immigration

• Increase in social capital

• Relative strength of the center increases

• Institutional legitimacy increases

• Return to Normal

• Fast Automation and AI Development and Deployment

• Slow Productivity Growth in Healthcare and Education

• Faster job displacement leads to falling demand

• Rising inequality 

• Worsening relations with China

• Either no or negative change in China’s relative power

• No consensus solution to immigration

• Decrease in social capital

• Increased popularity of extremes; further collapse of center

• Decreased institutional legitimacy

• Deflation

• Slow Automation and AI Development

• Slow Productivity Growth in Healthcare and Education

• Slow demand growth

• Increasing inequality

• Increasing US-China conflict

• China gaining relative power

• Immigration not resolved

• China perceived as a unifying external threat, increasing social capital

• Increased popularity of extremes; further collapse of center

• Institutional legitimacy (defense and security) increases

• High Uncertainty

• Slow Automaton and AI Deployment

• Fast Productivity Growth in Healthcare and Education

• Slow growth

• Declining inequality 

• Improving relations with China

• China also gaining relative power

• Immigration not revolved

• But social capital increases

• Center strengthens

• Whether institutional legitimacy recovers depends on balance between fear of increasing relative Chinese power versus slow growth and failure to resolve immigration 

• High Uncertainty