The Index Investor
March 2020

Portfolio Allocation Implications of Our Forecast

We take two approaches to deriving the tactical asset allocation implications from our analyses. 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.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.

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.

More information about our investment beliefs, including our core philosophy, approach to asset allocation (including our model portfolios and their long-term track record), and views on various approaches to active and passive management can all be found here.

Here is our latest asset allocation view:

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 (@28Feb20)

• New evidence on how far AI development has to go – for example, the continuing lack of causal and counterfactual reasoning capabilities.

• New evidence regarding the obstacles to faster deployment of renewables. Also, the economic slowdown caused by the COVID-19 pandemic should slow CO2 emissions and pressures for faster deployment of renewable electricity generation. On the other hand, a key uncertainty is the extent to which fiscal stimulus to offset the economic impact of COVID-19 may be aimed at faster implementation of the “Green New Deal”, both in the EU and the US. In the latter, this will depend on how COVID-19 will affect the outcome of the November 2020 presidential election. A Democratic win that results in the party’s control of both the White House and both houses of Congress is likely to lead to faster deployment of renewables and more pressure to reduce the use of fossil fuels.

• Evidence of COVID-19’s potentially negative impact on the macro economy (e.g., Italy).
• A hopeful hypothesis that COVID-19’s transmissibility is highest in a relatively narrow band of temperature and humidity parameters.
• Sobering evidence about COVID-19’s disease process, which makes it more transmissible than SARS or MERS (see Evidence File).
• Key Uncertainties: the length and strength of immunity obtained by recovered COVID-19 patients and the rate at which the virus is evolving. These will affect the extent of “herd immunity” and the efficacy of vaccines, and will determine whether the COVID-19 pandemic subsides or evolves into an endemic, possibly seasonal, and more dangerous virus than influenza.

• COVID-19 will almost certainly an already weakening global macroeconomy that was already facing a range of increasing headwinds.
• The interaction of the sharp contraction in demand (and likely business and household cash flows) caused by the COVID-19 pandemic with very high levels of debt will likely be a flashpoint. For example, the use of “helicopter money” – checks sent by the government to taxpayers to support demand – is very likely to generate political conflict if creditors insist that it be used for debt service payments rather than consumption. Many voters are still bitter about how bankers were bailed out in 2008 while many households suffered. I doubt that they will meekly accept the same treatment again in 2020.

• Before the explosion in COVID-19 cases, the EU was deadlocked in budget negotiations. Failure to support Italy (the EU member thus far hardest hit by COVID-19), and even worse, nations choosing to “go it alone” in the absence of a coordinated EU response, would very likely further weaken the EU, and perhaps put it on the path to further “Brexits”.
• Failure of the Iranian leadership to effectively respond to COVID-19 will almost certainly lead to further popular unrest – but also to further repression as the government fights to remain in control.
• China’s attempt to blame the United States for COVID-19 suggests that Xi Jinping’s faction has been significantly weakened by its poor management of the epidemic. This increases the potential for leadership change in China, and thus the level of uncertainty in the international system.
• The same is true for the Trump administration’s poor handling of COVID-19, and its potential impact on the November 2020 election.

• A number of new pieces of research provided more evidence of increasing social strains in the United States, even before the full-force arrival of COVID-19. This suggests that the electorate is highly volatile, which makes the outcome of the 2020 election more uncertain.

• A new poll found that the Trump impeachment experience further eroded the public’s trust in and “anger at and disillusionment with” government.
• Following the Super Tuesday primaries, the Democratic field was reduced to two: the relatively moderate Joe Biden, and progressive Bernie Sanders. Whether the Democratic Party can unify between either candidate remains to be seen, as does the impact of COVID-19 on their respective popularity.

• A new analysis found that private debt fund assets now exceed $800 billion. How these firms react to the sharp economic downturn triggered by COVID-19 is a critical uncertainty. If they play hardball with debtors, the political backlash could be fast and furious. new analysis found widespread investor underestimation of the embedded risks in many structured yield enhancement products.

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?

• The leaders of the world’s three major powers – Xi Jinping and Donald Trump, and to a lesser extent Vladimir Putin and Ali Khamenei are all facing sharply weakened 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 Iran and Israel, Saudi Arabia, and/ or the US, 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 accelerate the already sharp COVID-19 economic slowdown. Given given high debt levels, this would very likely accelerate the arrival of the Persistent Deflation Regime.

• A supply side shock of some type – beyond the disruption of global supply chains caused by COVID-19 -- 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. A less likely cause could be major crop failures.

• Another route to the high inflation regime (repeatedly noted by Bridgewater’s Ray Dalio) would be a sudden loss of confidence in the US dollar relative to other currencies (driving up import prices), perhaps because of deficit monetization and policy paralysis if a severe downturn continues without meaningful policy reform to increase productivity and growth and reduce inequality. However, this would also require that there was relatively more confidence in another currency, with the Euro being the most likely candidate. This currently seems unlikely, given both the Eurozone’s economic and political situation, and the prospect of an intensifying conflict between the West and China. However, if confidence collapsed in all major currencies, a sharp increase in inflation would lead to a dramatic rise in the price of gold.

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.

Feature Article: Why Did Markets Take So Long to React to COVID-19?

On 31 December, 2019, the S&P 500 closed at 3,230.78. That same day, the government in Wuhan, China, confirmed that health authorities were treating multiple cases of pneumonia.

On 6 January 2020, the Wall Street Journal reported that, in China, “medical authorities are racing to identify the cause of a mystery viral pneumonia that has infected 59 people in central China, seven of whom are in critical condition, and triggered health alerts in Hong Kong and Singapore.” The next day, the Financial Times reported that “health authorities are working to identify the outbreak of viral pneumonia that has infected at least 59 people in Wuhan [China]. Officials have ruled out Severe Acute Respiratory Syndrome, Middle East Respiratory Syndrome and certain types of flu.”

On 8 January, the FT reported that, “The world is already grappling with its first emerging disease of the decade. Dozens of people in Wuhan, a city in central China, have been hit by an unexplained pneumonia. There are no recorded deaths but, among 59 who have fallen sick, seven are reported to be in a critical condition with breathing difficulties. The authorities have ruled out seasonal flu, bird flu, severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS). Singapore and Hong Kong are now screening air passengers for fever. The outbreak, which began in December, has been traced back to a market selling seafood and live animals such as bats and marmots. It has since been closed and disinfected. The possibility that yet another malign microorganism has hurdled the species barrier to infect humans is likely to boost calls for a global catalogue of animal pathogens.”

On 9 January, the FT reported that, “A pneumonia outbreak that has infected more than 50 people in the Chinese city of Wuhan was caused by a coronavirus, which is the same kind of pathogen involved in the deadly SARS outbreak in 2003, Chinese state media said on Thursday. The outbreak, which comes ahead of the lunar new year holidays in late January when millions of Chinese will be travelling to see their families, has caused alarm in the region. The virus has prompted widespread concern on Chinese social media and triggered memories of the 2003 outbreak of severe acute respiratory syndrome, or SARS, that infected more than 8,000 people worldwide and killed more than 700, including almost 300 in Hong Kong. The World Health Organization said, in a statement issued on Thursday, the Chinese authorities believed the disease ‘does not transmit readily between people’, but noted that it could cause severe illness in some patients.”

On 15 January, the FT ran a story headlined, “How Dangerous is China’s Latest Viral Outbreak?” This was its first paragraph: “An outbreak of a new kind of viral disease in China has led to widespread concern about the risks involved and fears of an official cover-up. A 2002-03 outbreak of severe acute respiratory syndrome (SARS) killed more than 800 people after Chinese officials covered up new cases for months, greatly worsening its spread. That has raised questions over Beijing’s handling of the latest outbreak that began in Wuhan, capital of Hubei province.”

On 14 January, there was a Democratic Candidate Debate.

On 16 January, Donald Trump’s impeachment trial began.

On 18 January, in a story headlined “Scientists Warn Over China Virus Outbreak”, the FT wrote that, “Concerns are rising over an outbreak of a virus that originated in China as leading scientists suggested that more than 1,700 people may already have been infected, far more than had been thought. Chinese health authorities said this weekend that they had discovered 21 more suspected cases in the central city of Wuhan, bringing the total number of suspected cases of the pneumonia illness in the city up to 62. But experts warn that there is significant uncertainty about the severity and spread of the illness, which has already killed two people and evoked memories of the SARS outbreak that killed hundreds of people more than 15 years ago. A study by the respected MRC Centre for Global Infectious Disease Analysis concluded that a total of 1,723 people in Wuhan City would have had onset of symptoms by January 12, the last reported onset date of any case. Neil Ferguson, a public health expert from Imperial College London, who founded the centre, told the BBC he was “substantially more concerned than I was a week ago”.

Later in the same story, it was noted that, “on Sunday, Li Gang, director of the Wuhan Center for Disease Control and Prevention, told state broadcaster CCTV that the information available ‘does not rule out the possibility of limited human-to-human transmission.’ ‘The infectivity of the new coronavirus is not strong,’ he added, referring to how rapidly the virus may spread between individuals. ‘The risk of continuous human-to-human transmission is low’. Most patients have presented relatively mild symptoms, Mr. Li said, and no cases had been found in more than 700 people who came into close contact with infected patients.”

On 20 January, the FT reported that “China Confirms Human-to-Human Transmission of SARS-like Virus”.

On 21 January, the World Health Organization issued its first Situation Report on the “Novel Coronavirus”. The same day, the FT reported that Asian stocks had fallen after Beijing confirmed human-to-human transmission.

On 22 January, the US confirmed its first case, a patent in Washington State who had returned from Wuhan. On the same day, the FT ran a story headlined, “How China’s Slow Response Aided Coronavirus Outbreak.”

On 23 January, Chinese authorities began their quarantine of Wuhan.

On 27 January, The Index Investor (@indexllc) posted its first multipart tweet about the new coronavirus, including initial estimates of its Case Fatality Rate, and key uncertainties for investors to monitor. Between then and today (16 March) we have posted 11 more (often multipart) tweets, covering new information that reduced uncertainties about the virus and its potential impact.

On 30 January, the WHO declared a global health emergency.

On 31 January, the US restricted travel to China. That evening, the UK officially left the European Union.

3 February: Iowa Democratic Caucuses.

On 5 February, Donald Trump was acquitted at the end of his impeachment trial. That same day, the Diamond Princess cruise ship was quarantined in Japan with 3,600 passengers on board.

On 7 February, there was another Democratic Candidate Debate. Earlier that day, in China, Li Wenliang, the Wuhan doctor who tried to raise the alarm about the new coronavirus (and was accused by the police of “rumormongering”) died after contracting it.

11 February: New Hampshire Democratic Primary.

On 12 February, Dr. Nancy Messonnier, Director of the US Center for Disease Control’s Respiratory Disease program, noted on a press conference call that, “the goal of the measures we have taken to date are to slow the introduction and impact of this disease in the United States but at some point, we are likely to see community spread in the U.S.”

On 18 February, in our new issue of The Index Investor, we wrote, “The Wuhan coronavirus will almost certainly depress global economic growth, by an amount that is highly uncertain at this point. Global aggregate demand has already been weakening. A worsening slowdown (or growth turning negative) will very likely be reinforced by mounting debt servicing problems in our highly leveraged global economy.”

On 19 February, the S&P 500 closed at 3,386.15, thus far the 2020 high. That night there was a Democratic Candidate Debate, the first one to include Michael Bloomberg.

On 20 February, the FT’s Gillian Tett titled her column, “Share Prices Look Sky High Amid Coronavirus Fears.”

On 23 February Italian authorities limited travel to 10 towns in the Lombardy region after a sudden increase in coronavirus cases.

On 25 February, Larry Kudlow, Director of the National Economic Council, said, “We have contained this. I won’t say [it’s] airtight, but it’s pretty close to airtight”. He added that, while the outbreak is a “human tragedy,” it will likely not be an “economic tragedy.” That night, there was another Democratic Candidate Debate.

At a 26 February press conference, president Trump said that, the current number of COVID-19 cases in the U.S. is “going very substantially down, not up.” He also claimed that “the U.S. is “rapidly developing a vaccine” for COVID-19 and “will essentially have a flu shot for this in a fairly quick manner.”

3 March: Super Tuesday Democratic Primaries.

On 15 March, there was another Democratic Candidate Debate, this time just between Joe Biden and Bernie Sanders.

On 16 March, after multiple trading stops, the S&P 500 closed at 2,386.13, down 29.5% from its February peak.

In the future, many people will ask the same question the Queen asked in the aftermath of the 2008 global financial crisis: “Why didn’t anyone see this coming?”

Our preliminary answer to that question is that it is very likely that multiple interacting factors were at work, including the following:

• We naturally resist the cognitive dissonance that is produced by evidence that severely contradicts our current system of interrelated and mutually supporting beliefs. One aspect of this is our tendency to avoid information that challenges our existing beliefs in a negative way, and to give more attention to evidence that supports our existing views (see “How People Decide What They Want to Know” by Sharot and Sunstein). Another is the way we subconsciously try to fit new discordant evidence into our existing world view by subtly adjusting our beliefs to incorporate it. As Daniel Kahneman noted in his book, “Thinking Fast and Slow”, it is only when this automatic adjustment fails that we note our feeling of surprise at a new piece of information and consciously reason about its potential significance.

• But as Tali Sharot’s research has repeatedly found, even our conscious reasoning is flawed because we often fail to fully incorporate negative information into our beliefs and our memories. Sharot finds that this is the root cause of our natural over-optimism bias.

• Another factor is that as uncertainty increases, so too does our natural human desire to conform to the views and behavior of our group, and to engage in more social learning (i.e., copying). In our evolutionary past this was undoubtedly adaptive; today it is not. When uncertainty increases, the number of diversity of narratives about the future by different members of a population tends to decline, making the system of beliefs more fragile, and susceptible to changes that are both sudden and large.

• As uncertainty increases, people are also more willing to discount conclusions based on their private information when those conclusions disagree with the dominant view in their group. This can allow an increasingly dangerous state to persist for long periods of time, until a strong public signal that is consistent with group members’ private information causes them to quickly and substantially update their beliefs all at once. However, if public signals are confusing or contradictory, the dominant narrative can remain in place, despite accumulating evidence that it is wrong.

• Another relevant shortcoming of human reasoning is our tendency to form beliefs by unconsciously matching the features of a current situation to similar ones that are stored in our memory (this is sometimes called “Retrieved Context Theory”). Our strongest memories are those formed by events that triggered high levels of emotional arousal and negative feelings (or “valence”). In this case, many people may have initially associated early reports about COVID-19 with vague memories of the relatively benign way the SARS epidemic played out in 2003. In the future, early reports of new respiratory viruses are almost certain to be associated with people’s much more negative COVID-19 memories.

• Another factor that may have contributed to assessment failure is that most people struggle to grasp the future implications of processes characterized by time delays and non-linearity, such as those that underlie infectious disease dynamics.

• It is also the case that when our mental energy has been depleted, we exhibit poor control over the allocation of our attention, and are therefore likely to miss important signals. In this regard, the early development of the Wuhan coronavirus crisis coincided with the Trump impeachment trial, multiple Democratic Candidate Debates, the Iowa Democratic primary caucuses, and the New Hampshire Democratic primary.

• Finally, in October 2019, the Economist estimated that 35% of public equities are now managed using quantitative processes. This presents a number of fundamental challenges when disruptive changes like the arrival of COVID-19 roil the financial markets. Most of these processes cannot detect, early on, changes that are not contained in the set of historical data on which they were trained, and/or which have not yet manifested in the signals they track (e.g., changes in sentiment or momentum). Because they are far better at causal and counterfactual reasoning (which quantitative techniques still can’t handle), human beings are still far more effective at making sense of the uncertainties that are inherent in highly complex, evolving systems like global macro.

High Value Information Observed In February 2020

In our model of the complex global macro system, change drivers are arrayed across a roughly chronological process (albeit one with many feedback loops), in which technological and environmental changes precede changes in the economy and national security, which in turn lead to 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 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 either the range of potential values for a parameter or the structure of our model. With respect to indicators, the higher our priori probability is for a regime, the more we look for indicators that it will not occur, and the lower our prior probability for a regime, the more we look for indicators that it will occur. Put differently, we tend to look for high value indicators that disconfirm our prior views.

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.