The Index Investor
November 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 (@31Oct19)

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 (@31Oct19)

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 relatively higher returns are associated with more widely held investor beliefs in the probability that a given macro regimes will develop in the future.

Over the last three months, there has been a relative strengthening in investors’ beliefs that either the high inflation or normal times regime lies ahead.


Qualitative Considerations

As covered in more detail in this month’s Evidence File (see below), here are the most important new indicators and surprises that influenced this month’s changes to our regime probability forecast:

Technology: Results on the US National Assessment of Educational Progress were again disappointing, and have been flat for a decade. At a time when automation and artificial intelligence technologies are rapidly improving, continued scarcity of talent will continue to support “winner take all” economics in many industries, which in turn contributes to income inequality. Also, growth in the number of working age adults whose education does not enable them to succeed in the 21st century economy will increase future demand for higher social safety net spending.

Environment: The latest International Energy Outlook by the US Energy Information Administration forecasts continued growth in coal fired electric generation to meet rising consumption demand. Another new report that analyzed uncertainty in climate forecasts found that parameter uncertainty, rather than model uncertainty, had the greatest impact, with future productivity growth particularly important.

Economy: The IMF’s semi-annual flagship reports (the World Economic Outlook and Global Financial Stability Report) presented a gloomy picture of weakening demand and an overleveraged global economy, with negative feedback loops driven by adverse trends in trade, politics (which affect the likelihood of an effective policy response when the downturn arrives), and demographics, all of which contribute to rising uncertainty and further weaken demand.

National Security: Trump’s impulsive abandonment of the Kurds has increased uncertainty in the Middle East, and in foreign relations among Western nations. At the same time, more violent unrest in Hong Kong seems increasingly likely to provoke Chinese repression, which will intensify the developing New Cold War between China and the West (even as there were other indications of mounting internal problems in China). Meanwhile a new report from the Heritage Foundation presented a sobering picture of the ability of America’s military to meet some of the contingencies it may face in the future.

Society: New data showed that median priced homes are now out of reach for average wage earners in 74% of US housing markets. Also, Pew Research found that partisan antipathy has grown more intense and more personal. Finally, another new report found that Job Retraining programs have badly underestimated the time required to make displaced workers employable in new roles.

Politics: The UK election has raised uncertainty, given the very different futures that would be likely under a Johnson or Corbyn government. In the US the release of more evidence about Trump’s dealing with the Ukraine has significantly moved poll numbers in favor of impeachment, while intensifying his and his defenders attacks on the process in the House (including witnesses testifying against him). Meanwhile, concerns continued about whether the split between US Progressives and Traditional Democrats can be healed in time to win the November 2020 election. Finally, continuing riots in Chile provided more evidence of the consequences of unresolved and growing gaps between elites and the economic circumstances (and attendant fears and anger) of increasingly populist and interconnected (due to social media) mass protest movements.

Financial Markets: Financial markets continued to display schizoid behavior, with yields rising in the lower reaches of the credit markets as potential problems become more apparent (e.g., WeWork’s cash shortage), while equity markets grasped at whatever bit of good news (e.g., rumors of a potential US-China trade deal) they could find to push prices a bit higher.

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 for November:

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).

• Another route to the high inflation regime 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 as a severe downturn continues. 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, the price of gold would sharply increase.

• Though we believe it is still 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.

Conclusion

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 October 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 and environmental changes precedes 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.

.

.

.

.

.

.

.

Feature Article: Will the New Flows of Assets into ESG Funds Produce Alpha for Investors?


Investing in funds based on the rating of companies on environmental, social, and governance metrics are undoubtedly growing in popularity. What is much less clear is whether ESG metrics provide a signal that leads either to increased relative returns (alpha) or reduced risk of loss.

In this article, I take a Bayesian approach, and start by describing two sets of prior views about the potential effectiveness of ESG information as a source of alpha. The first is deductive, based on investment theory, and the second is inductive, based on my experience as a corporate executive.

I then review recent research on ESG investing, and then weigh this evidence in light of the priors to reach a conclusion about the likely alpha generation potential of ESG-based investing.

Prior View

In general, in order to justify investing in an active strategy, like ESG, instead of a broad asset class index fund, one must believe one of these propositions is true:

(1) The active fund will deliver higher returns than the asset class fund, but with higher risk.
(2) The active fund will deliver lower returns than the asset class fund, but with lower risk.
(3) The active fund will deliver higher returns than the asset class fund, but with lower risk.

The first two propositions are consistent with mainstream investment theory. Whether then third proposition is true is the eternal question about active management. One’s belief about the likelihood that an active manager will deliver better than market returns with lower risk (after expenses) must rest on a set of further assumptions:

(1) The active manager has (legal) access to superior information; and/or,
(2) The active manager has a superior model for deriving investment insights from publicly available information; and,
(3) These advantages will last (at least for a period of time); and,
(4) You can identify these managers either because you have access to superior information about active managers and/or a superior model for making sense of public information about them.

As a starting point, we know from the evidence that the base rate of active managers who deliver higher returns (after expenses) with less risk than broad asset class index funds is historically small, declines as the time horizon lengthens (i.e., superior performance is hard to sustain), and in recent years has declines (e.g., because of the increased use of algorithmic trading strategies, and historically low price volatility in many asset classes).

So the question becomes, is there any reason to expect that an active manager would have access to superior ESG information about potential investments, or a superior model for deriving alpha generating insights from publicly available ESG information about them (e.g., their scores on various ESG metrics)?

My corporate experience helps to answer these questions. At a public company I ran, the goal we sought to maximize was total shareholder return relative to an appropriate comparable benchmark (our sector index). However, our pursuit of this goal was subject to a number of constraints, including environmental stewardship, our legal and ethical behavior, our safety record, employee, customer, host community, and supplier satisfaction, and the like.

In practice, the distinction between the objective we were trying to maximize and the constraints we faced was not cut and dried, because on some of the constraints we could act in ways that would, sometimes through multiple channels, produce a competitive advantage for us, that would drive superior value creation.

For example, a superior environmental and safety record meant that we were put on some customers’ preferred supplier list, which resulted in higher revenues. Another example was our choice to increase employees’ pay as they acquired more certified competences rather than wait for a promotion slot to open up, which reduced both employee turnover and operating costs and improved service quality, all of which drove better returns.

The key point to recognize is that we took actions that would have given us a higher rating on some of today’s ESG metrics (had they existed back then) because we believed that they would strengthen our value proposition and business model, and thus generate higher shareholder returns. Put differently, we undertook these actions because they were a part of our business strategy, not in an attempt to boost our composite score on someone’s ESG metric.

In theory, an active manager who understood all this (and we did discuss it on earnings calls) and had a model which accurately incorporated these initiatives into an earnings forecast could have invested in our stock and generated alpha (we outperformed our index benchmark by 43% while I was CEO). But this insight would have been driven by the analysis of our strategy and implementation capacity, not by our composite score on ESG metrics (which was unlikely to provide much information about whether our shares were over or undervalued).

I’ll now move on from environmental and social actions to the issue of governance. I have no doubt that high quality governance contributes to shareholder value creation. That said, I don’t believe that current governance metrics capture the interacting causes from which this advantage emerges (apart, perhaps, from whether a board has separated the chair and CEO roles). There is no metric that measures the quality of a board’s non-executive directors, the issues on a board’s agendas over the course of a year, the information it receives from management and its interaction with them, and the quality of the processes it uses to carry out its fundamental duties of setting direction, allocating resources, hiring and evaluating the CEO, monitoring strategic risk, and evaluating and fairly reporting results to stakeholders.

On balance, my prior view is that investing in companies that have high composite scores on various (and often conflicting) ESG metrics is unlikely to generate sustained risk and expense-adjusted alpha for an investor. However, there is an important caveat – it may be the case that companies that score highly on ESG indexes are less likely to suffer severe losses due to environmental, safety, and other reputational disasters that, in the worst case, could threaten their right to continue operating.

What Does the Research Say?

Conclusions

The weight of recent research evidence on ESG investing is generally consistent with my prior view, and leads me to these conclusions:

1) Simply investing in a portfolio of companies with high ESG ratings is unlikely to generate risk and expense adjusted alpha relative to a broad asset class index return. In fact, as ESG based index funds accelerate their accumulation of assets under management, it may have just the opposite effect. As Ibbotson and his co-authors note in a new research paper (“Popularity: A Bridge Between Classical and Behavioral Finance”, which is covered in this month’s Evidence File) investors tend to bid up the price of assets that have characteristics to which they are attracted, and in so doing reduce their expected returns.

2) However, this relationship is likely not symmetrical. While assets with very low ESG ratings will have their prices bid down and thus their expected future returns increased, research indicates that these low ratings are signals of significant ESG risks of uncertain magnitude that, historically, have led to realized underperformance versus the asset class benchmark.

3) Consideration of ESG issues is most likely to lead to alpha generation when, as part of an active investment management process, they are analyzed in the broader context of a company’s business environment and value creation strategy. However, as is true of all active management, this type of ESG related alpha will likely be very difficult for investment managers to sustain over longer periods of time.