The Index Investor
October 2019

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 (@30Sep19)

At the end of September, four of our five quantitative indicators showed either an unchanged level of relatively high market stress, or a slight decline, and one showed an increase. However, these must be seen in the light of qualitative evidence, like repo funding problems and rising uncertainty, that indicate a very high level of underlying market tension and potential for sudden disruptive change.

Macro Regime Forecast Probabilities (@30Sep19)

The Current State of Quantitative Regime Predictors

Our quantitative forecast 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 (in this sense, our regimes can be regarded as macro factors). We assume that higher returns are associated with a higher underlying probability for the relevant macro regime.

• Technology: Indications that AI deployment may be faster and more powerful than previously expected, which could cause greater job losses, worsening inequality, and weaker aggregate demand.

• Economy: Further increases in uncertainty, which depress demand with a lagged effect, and more indications of weakening global aggregate demand.

• National Security: Continuing indicators (e.g., the military hardware displayed at the Chinese Communist Party’s 70th anniversary parade) that the conflict between China, the West, and some of its regional neighbors is more likely to intensify rather than moderate in the years ahead. Also the Iranian attack on Saudi oil facilities, and further worsening of the Honk Kong crisis.

• Society: New data showing accelerating economic growth divides between different US regions, and a widening values divide between different groups, both of which further undermine the nation’s capacity for collective action (in the face of internal challenges, if not external threats).

• Politics: Launch of the process to impeach, convict, and remove Donald Trump from office.

• Financial Markets: Repo market problems, revelation that 60% of high yield bonds and leveraged loans are held by retail collective investment vehicles promising daily liquidity to investors, Boston Fed president Rosengren’s comments on how the co-working boom (and the underlying maturity transformation of office leasing) may pose a systemic threat, the sharp increase in secondary private equity transactions, the negative impact of credit derivatives in the Thomas Cook bankruptcy (as an indicator of what lies ahead on a larger scale when the global economy turns down), and new research showing that 25% of US public sector pension assets are valued on an appraisal basis.

Tactical Asset Allocation Implications of Our Forecasts

Based on subscriber requests, this month we are re-introducing a feature from the previous version of The Index Investor: Tactical Asset Allocation Implications from our analyses.

These take two forms. The first takes a systematic approach, and is based on relative asset class valuations. Our starting point is our “neutral” model portfolio, which is equally weighted across nine broad asset classes, and also includes a 10% allocation to alpha strategies (equity market neutral and global macro) that are designed to have a low correlation to returns on broad asset classes. Based on asset class valuations, we systematically vary the asset class weights (but not the active strategy weight), increasing from 10% to 15% when an asset class is likely undervalued, and 15% when it is very likely undervalued. In the case of overvaluations, we go to 5% and then into cash, if there are no undervalued asset classes with room for an increase. In effect, this replicates the systematic rebalancing strategy we used for 15 years in our previous model portfolios.

The second tactical approach is based on our subjective view not only of current asset class valuations, but also of the implications of the broader macro trends and uncertainties that we analyze each month. Importantly, this subjective view reflects our primary goal of avoiding large downside losses, rather than seeking large upside gains.

Two final notes. First, with respect to US fixed income, we include credit products (investment grade and high yield) in the same asset class as government debt, and will shift into the former when their valuations become attractive. Second, we regard gold not as a separate asset class to be held long-term, but rather as a complement to cash, into which we shift in periods of substantial overvaluation across multiple asset classes.

Here are our tactical asset allocation views at the start of October:

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? Here are three possibilities:

• 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 a “kinetic” conflict develop between China and the United States, or between Russia and one or more European countries (e.g., due to a Russian incursion into the Baltics), it would generate a very sharp increase in uncertainty that would likely cause an equally sharp economic slowdown which, given high debt levels, would speed the arrival of the Persistent Deflation Regime.

• While the probability is 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 prolonged kinetic conflict between Iran and the US that produced a extended disruption in global oil supplies, or, less likely, an infectious disease pandemic or major crop failures (e.g., due to climate change and/or disease).

• Though we still believe it is unlikely, it is becoming easier envision a scenario in which both Xi Jinping and Donald Trump leave their current roles, and are replaced by leaders who are more committed to lessening the intensifying conflict between China and other nations. This would likely provide a strong boost to confidence (and thus lead to an equally strong reduction in uncertainty). However, a reduction in perceived external threats might also intensify already substantial internal divisions and conflicts in China and the United States, which could, ironically, also lead to the deflation regime.

System Tipping Points/Critical Threshold Analysis

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

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

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

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

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

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.

Note: Combining Our Forecasts with Others From Other Sources and Extremizing the Result Should Increase Your 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.

High Value Information Observed In September 2019

In our model of the complex macro change drivers are arrayed in a roughly chronological process (albeit one with many feedback loops), in which technological change precedes changes in the economy and national security, which in turn lead changes in society and politics, all of which produce the effects we observe in investor behavior and financial market valuations and returns.


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.

Feature Article: Macro and Asset Allocation Implications of Environmental Trends and Uncertainties

Context

We first wrote about the potential relationships between the evolution of environmental uncertainties and future asset class valuations in 2006 (“Climate Change and Asset Allocation”), shortly after the Stern Review (“The Economics of Climate Change”) was published by the UK Treasury. Thirteen years later, this column will review key macro trends and uncertainties related to the environment, alternative scenarios their interactions could produce, and the implications for asset class valuation and portfolio allocation.

Before delving in, it is critical that we place environmental issues in their proper, and much broader context.

In “The Natural Science Underlying Big History”, the astrophysicist Eric Chaisson concludes that, “A wealth of observational data supports the hypothesis that increasingly complex systems evolve unceasingly, uncaringly, and unpredictably from big bang to humankind. These are global history greatly extended, big history with a scientific basis, and natural history broadly portrayed across ∼14 billion years of time…”
“Quantitative models and experimental tests imply that a remarkable simplicity underlies the emergence and growth of complexity for a wide spectrum of known and diverse systems. Energy is a principal facilitator of the rising complexity of ordered systems within the expanding Universe; energy flows are as central to life and society as they are to stars and galaxies…Rising energy expenditure per capita has been a hallmark in the origin, development, and evolution of humankind…In particular, energy rate density [energy flow per unit of time per unit of mass or area] is an objective metric suitable to gauge relative degrees of complexity among a hierarchy of widely assorted physical, biological, and cultural systems observed throughout the material Universe. Operationally, those systems capable of utilizing optimum amounts of energy tend to survive, and those that cannot are non-randomly eliminated…”

In sum, environmental issues are inextricably bound up in the larger context of energy issues, which are deeply embedded in fundamental technological, economic, national security, social, and political dynamics.

Starting Points

According to NASA, crude global temperature records have only been available since around 1880. And since even today temperature sensors are not evenly distributed around the globe, even today’s global average temperature data remain somewhat noisy estimates. Yet even taking that into account, the evidence indicates that average global temperature has been increasing, at an accelerating rate.

NASA estimates that between 1880 and 2018, average global temperature increased by 0.8 degrees Celsius, or 1.4 degrees Fahrenheit. Moreover, two thirds of this increase has occurred since 1975.

Two hypothesis have been suggested to explain this increase. The first is solar cycles, the most recent of which is coming to its end. It is speculated that the next cycle may be a multicycle minimum (e.g., with low sunspot activity), which, all else being equal, could reduce average global temperatures by 0.3 degrees Celsius.

While solar cycles clearly had an impact, their impact is not sufficient to fully account for the observed rise in average temperature. For example, examination of extremely old ice cores, and other paleohistory techniques, have demonstrated an association throughout the earth’s history of increases in atmospheric and ocean CO2 levels (e.g., due to volcanic activity) with increases in temperatures. However, the operation of the earth’s climate system is extremely complex, with multiple feedback loops and non-linearities at work. Put differently, modeling the world climate system is extremely challenging, and all conclusions contain a degree of uncertainty (which the International Panel on Climate Change – the IPCC – is now expressing systematically).

The inability of solar cycles to fully explain the observed temperature change, and the findings from studies of the earth’s history led to the second hypothesis, that human actions have also had a substantial impact. In particular, this hypothesis has strongly focused on the burning of fossil fuels as a major contributor to the significant increase in carbon dioxide (CO2) and other so-called “greenhouse gases” in the earth’s atmosphere. Specifically, from a pre-industrial age estimated level of 280 parts per million, atmospheric CO2 had reached 407 ppm by 2018. Again, this has been increasing at an accelerating rate, from about 1.0 ppm per year in 1969, to 2.0ppm by 2005, and 2.5ppm most recently. This has been caused not only by accelerating production of CO2 and other GHG emissions, but also a slowdown in the rate at which oceans absorb CO2 from the atmosphere (and in the process become more acidic).

This “anthropogenic” hypothesis has led to calls for restricting CO2 and other GHG emissions, largely by reducing the burning of fossil fuels to generate energy. Most recently, the IPCC has urged policymakers to limit the observed rise in temperature to 1.5 degrees Celsius above the pre-industrial level.

Critical Uncertainties

• First, what are the most important potential macro impacts if GHG emissions and average global temperature levels continue to increase?

• Extreme heat days are 4.0c hotter rather than 3.0c hotter, and associated reductions in human morbidity and mortality from extreme heat;

• Higher frequency and severity of drought in some regions and extreme precipitation events in others;

• Potentially lower climate-driven global migration flows;

• A reduction of .1 meter (4 inches) in average sea level rise versus the .26 - .77 meter rise (10” to 30”) predicted if average temperatures increase by 2.0 degrees Celsius;

• Reduced intensity of wildfires;

• Less permafrost thawing;

• Reduced spread of invasive species and vector born diseases from hot climates (e.g., malaria and dengue fever);

• Reduced increase in ocean acidity and an estimated reduction of 2% rather than 4% in the aggregate annual fisheries catch; and

• A lower reduction in global crop yield declines (forecast at 2% - 6% per decade at a 2 degree increase).

• Second,to what extent are we theoretically able to reduce GHG emissions and limit the increase in average global temperature?

• Relatively low power density for wind and solar. In “The Spatial Extent of Renewable and Non-Renewable Power Generation”, van Zalk and Behrens show that given current renewable technologies’ power density (watts generated per square meter of space utilized), implementing the National Renewable Energy Laboratory (NREL) 80% renewable energy scenario by 2050 would require an additional 15 million hectares of land to be used for renewable power generation (an area roughly the size of the state of Illinois). In another study, “Observation-Based Solar and Wind Power Capacity Factors and Power Densities”, Miller and Keith found an average on-shore wind power density of .50 watts/square meter, which has been decreasing as wind generation is installed in more marginal locations. The average power density of solar plants was 5.4 watts/square meter, which has been increasing with time as the technology has improved (by comparison, the average power density of a natural gas generating station is 482 watts/square meter).

• The need for additional transmission lines to serve wind farms. Optimal locations for wind generation are mostly located far from the highest demand for electricity, which necessitates building more high voltage transmission lines. However, approval of these almost always runs into opposition.

• Increased use of variable renewable generation triggers escalating grid control challenges. Current electrical distribution systems were designed on the basis of central (e.g., coal fired) generating stations that would run continuously. When variable renewable generating sources (like wind and solar) are added to the grid at multiple locations, power quality and grid control challenges exponentially increase. While technical solutions are under development, these challenges remain serious obstacles to rapid deployment of wind and solar generation.

• Electricity storage technologies that could smooth power supply from wind and solar generation are still immature. Today, fluctuations in the supply of electricity from wind and solar generators is most often met by a combination of fast-start gas fired “peaker” generation stations, and increased output from coal, gas, and other “baseload” plants. Moreover, when the cost of battery storage to compensate for their intermittent generation is added to the cost of wind and solar generation, their cost per delivered kilowatt hour increases, shrinking their advantage over gas fired generation (and when additional grid control costs are added in, the cost of variable renewable energy increases even more).

• Third, to what extent will different parties (including governments and companies) actually undertake initiatives to reduce GHG emissions on a meaningful scale?

• The rate at which the adverse effects of rising global temperatures appear, including their frequency, severity, and global distribution;

• The rate at which key technologies develop, especially those related to power generation, direct removal of CO2 from the atmosphere at scale (“Direct Air Capture”), and mitigation of food supply related consequences of increasing global temperatures.

• Economy: Slower growth, especially if food prices substantially increase, partially offset by higher government spending on consequence mitigation.

• National Security: More disorder and higher conflict driven by battles to control food resources and control rising migration flows. May reduce current China-West conflict.

• Society: Increased perception of common threat leads to reduced polarization and higher capacity for collective action.

• Politics: Higher levels of international conflict and strengthening of blocs. Increased defense and infrastructure spending.

• Asset Returns: Increased demand for gold, government bonds, infrastructure (including HVAC), and food supply chain equities. Commodity profits will be limited because of price controls to deter speculation. Property could do well as a defensive domestic investment.

• Economy: Potentially faster growth, due to both investment stimulus and reduced perceived uncertainty/increased optimism. But emissions reduction investments likely to be lower than scenario with high consequence mitigation.

• National Security: Lower levels of climate related disorder and conflict. However, current conflicts (e.g. China vs West) will continue.

• Society: Current levels of polarization will remain and possibly intensify as a result of splits over environmental spending.

• Politics: Current domestic and international conflicts continue.

• Asset Returns: Continued struggles with inadequate demand, high leverage, and deflationary pressures. Equities should benefit from new wave of tech opportunities and rising optimism.

• Economy: Slower growth, especially if food prices substantially increase, partially offset by higher government spending on consequence mitigation.

• National Security: More disorder and higher conflict driven by battles to control food resources and control rising migration flows. May reduce current China-West conflict.

• Society: Increased perception of common threat leads to reduced polarization and higher capacity for collective action.

• Politics: Higher levels of international conflict and strengthening of blocs. Increased defense and infrastructure spending.

• Asset Returns: Increased demand for gold, government bonds, infrastructure (including HVAC), and food supply chain equities. Commodity profits will be limited because of price controls to deter speculation. Property could do well as a defensive domestic investment.

• Economy: Continued struggles to escape secular stagnation.

• National Security: Current conflicts (e.g. China vs West) will continue.

• Society: Current levels of polarization will remain, but environmental concerns could become less muted as secular stagnation drags on and focus on redistribution gains traction.

• Politics: Current international conflicts continue, with increasing domestic conflicts driven by continuing secular stagnation.

• Asset Returns: Continued struggles with inadequate demand, high leverage, and deflationary pressures. Gold as an alternative to negative yielding governments and rising concern over potential inflation as more debt is monetized by central banks. Property will benefit. Equities will depend on nature of fiscal stimulus to attack secular stagnation.