The Index Investor
September 2018

Asset Class Valuation and Momentum Indicators (@31Aug18)

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 (@31Aug18)

All four of our indicators point towards a high level of underlying stress in financial markets at the end of August 2018, and thus a high risk of substantial changes in asset class valuations.

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 are often preceded by subtle warning signs, as stress accumulates within them. While this research is not definitive, we believe that four 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 increased from (.48) in July to .96 in August. This indicates that financial markets are becoming more ordered and are very likely approaching a critical transition point.

The second market stress indicator we monitor is the Equity Market Related 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 our evolutionary past, when uncertainty increased our probability of survival was enhanced by staying close to our group. We 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.

At the end of August 2018, the Economic Policy Uncertainty Index stood at the 75th percentile of its values since the data series began in 1985 – to be clear, only 25% of values were higher over that 33.5 year period.
Our third market stress indicator is the spread between the yield on AAA rated bonds and the 10-year US Treasury. We interpret this as a proxy for the level of investor concern about financial system liquidity. At the end of August 2018, this spread stood at 1.05%, which was the 68th percentile of all observations since the index series began in 1983 – only 32% of observations have been higher.

Our fourth market stress indicator is what we term the implicit “political risk premium” that is embodied in the price of gold. Our starting point for deriving this premium is the three 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. This warranted a 33% increase in the price of gold to hedge potential 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.

We subtract this 21% increase (reflecting actual inflation over the period) from the 33% increase for gold as an inflation risk hedge to derive an apparent 12% increase in the gold price that reflected an inflation risk premium, rather than compensation for actual 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.

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.

To put this in further perspective, the actual gold price premium above our calculated “unit of account” price 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.

Through August 2018, the price of gold has fallen by about 8% since the start of the year, so, on a rough approximation, the political uncertainty premium now stands at about 40%.

Macro Regime Forecast and Implications for Asset Class Values

Forecast Discussion

The focus of our monthly macro forecast is twofold. First, the probability of a change in financial market regime that causes changes of 20% or more in asset class valuations over the next year. Second, contingent on such a change taking place, the probability of a subsequent transition to other regimes.

Our analysis focuses on four possible macro regimes: (1) Normal Times, where equity asset classes perform well; (2) a High Uncertainty regime that is usually short and transitory, where asset classes like short-term government bonds perform best; (3) High Inflation, where commercial property, real return bonds and other traditional hedges are favored; and (4) Persistent Deflation, which up to now has only been seen in Japan, and in which the relative performance of different asset classes remains most uncertain.

Our forecasting methodology is derived from our experience on the Good Judgment Project, as described in the book, “Superforecasting” by Gardner and Tetlock.

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%.

Quantitative Regime Predictors

As you can see in the following table, our 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.

We conclude that the balance of expectations over the past three months was for the normal regime to persist rather than switching to the high uncertainty regime. However, when and if the latter develops, the balance of contingent expectations was for it to be followed by the persistent deflation regime, rather than high inflation.

Qualitative Analysis

The following table highlights the key stocks that we monitor in the five areas that, individually and collectively, drive changes in financial market regimes.

As previously noted, the probability of regime change increases when one or more of these stocks exceed a critical threshold.

In this section, we will briefly discuss these stocks and potential critical thresholds that could be reached. We will conclude with our current assessment of how close we are to these critical thresholds, and the implications for financial market regime change probabilities.

Technology

Through invention, recombination, and organizational changes to make better use of them, the stock of our technological capabilities is constantly growing, and thus expanding both the range and cost effectiveness of potential applications.

We also regard our educational and healthcare systems as critical social technologies. In the United States, these technologies’ level of productivity is significantly lower than many other developed nations.

At least five critical thresholds could, if reached, contribute to a change in financial market regimes:

• Multiple technologies reach a level of capability that creates substantial military advantages versus China (the United States’ only peer competitor) and/or Russia and Iran. Of course, the opposite is also true, and such an advantage might tempt a nation hostile to the West to act far more aggressively than in the past (e.g., see the “Russian New Generation Warfare Handbook” by the US Army’s Asymmetric Warfare Group, or, on China, “Systems Confrontation and System Destruction Warfare” by RAND).

• One or more broadly applicable technologies reach a level of capability that creates a substantial new source of national economic advantage (e.g., quantum computing). This could tempt other nations to erect protectionist barriers, leading to significant economic disruptions (arguably, China’s mandatory technology sharing regulations, and the EU’s increasingly stringent data protection regulations are early examples of this).

• As is increasingly evident in China, the rapid advance and integration of various surveillance, social sensing, and artificial intelligence technologies is rapidly improving the ability of a nation state to control the lives of people within its borders. This also suggests that a critical capability threshold exists that will make future authoritarian governments much more difficult to dislodge than they have been in the past.

• Rapid improvements in “labor substituting technologies” like automation, robotics, and artificial intelligence create the potential for a critical threshold where their deployment will sharply increase (as is already happening at Chinese electronics suppliers), and with it unemployment (which in turn will depress aggregate demand). This critical threshold will also be affected by the rate at which education system productivity improves, with slower improvement making labor-substituting technologies more attractive.

• A significant improvement in technologies that extended average lifetimes will also produce a critical threshold if the level of healthcare system productivity doesn’t substantially improve. The result would be much more pressure on government budgets, unless technology improvement also leads to a substantial increase in the level of total factor productivity growth in the economy, and hence government revenues.

The Economy

In the economic realm, key stocks include the level of total factor productivity, the stock of production capacity, the size and quality of the labor force, the level of inequality, the level of economic profit (i.e., returns in excess of the cost of capital) and its degree of concentration, and the stock of debt.

Four critical economic thresholds could, if reached, contribute to a change in financial market regimes:

• According to Bank for International Settlements data, since 2001 world non-financial (household, corporate, and government) debt as a percentage of GDP has risen from 191% to 244% (and note that this does not include trillions in unfunded public pension liabilities). In the US, the latest projections show that federal government deficits and outstanding debt will substantially increase in the coming years, having already grown from 185% to 251% between 2000 and 2017). Since 2001, real global GDP growth has also slowed, from an average of 2.88% per year between 1985 and 2001, to 2.30% between 2001 and 2017. To prevent a further increase in global debt/GDP ratios, interest rates must remain below GDP growth, which itself is under downward pressure from slowing labor force growth, weak productivity, increasing inequality, widening disparities in business model profitability, and potentially the greater use of labor-substituting technologies. This implies that there will be increasing pressure on central banks to hold down interest rates, which also constrains their scope for reducing rates to cushion the economic consequences of the next downturn. However, the already high debt/GDP ratio many also constrain the scope for fiscal policy. At some point, rising uncertainty about debt rollovers and repayment will reach a critical threshold, and investors’ focus will shift to the four ways a debt crisis can be resolved: through default, conversion to equity, extended austerity, or extreme inflation.

• Significant monetization of rising government deficits, which throughout history has led to exponentially increasing inflation in many nations.

• In many industries, production capacity has steadily increased, either for political reasons (e.g., investment by China’s state-run companies to maintain national growth rates) or due to the order of magnitude productivity impact of automation technologies. However, the growth rate of aggregate demand has not kept pace, due to a combination of slowing labor force growth and weakening productivity growth. Along with growing debt/GDP ratios, this has created an overhang of deflationary pressure on the world economy. As Japan has shown for nearly 30 years, once deflation takes root in an economy, it can be very difficult to escape.

• Due to pressures from globalization, rapid technology changes, the rising marginal cost of producing energy, and evolving customer needs and wants, many business models have faced increasing profitability pressures for three decades or more (e.g., see Deloitte’s Shift Index on the long term decline of average Return on Assets). More recently, economic profits (i.e., profits in excess of the cost of capital) have become highly concentrated in a limited number of firms (many in the technology industry) that have benefited from network effects and much faster technology innovation than competitors. Up to now, the principal effect of these pressures appears to have been on labor incomes. However, if education system productivity continues to stagnate, and the capability of labor substituting technologies continues to improve, we could reach a critical threshold where profit pressures lead a significant change in business models and far higher levels of structural unemployment.

National Security

In this area, there are two key indicators to monitor: Different nation’s relative stocks of military capabilities (e.g., via the Net Assessment methodology), and different nations’ and alliances’ level of motivation to pursue conflict – or, at minimum, not back away from it.

We see three potential critical national security thresholds that could lead to financial market regime change:

• The first is obviously “kinetic” conflict (likely accompanied by cyber attacks on Western infrastructure and/or the financial system) between a Western ally and China (e.g., in the South China Sea), Russia (e.g., in Eastern Europe), or Iran (e.g., involving attempted closure of the Strait of Hormuz).

• The second is a precursor to kinetic conflict: an increasing in the level of willingness to pursue such conflict, due to some combination of a significant increase in domestic pressure and/or perception of a igrave threat to vital national interests.

• The third is also a precursor to kinetic conflict: a significant change in the perceived military balance that substantially raises an aggressor’s belief of the probability of success in a kinetic and/or significant cyber conflict with the West.

Society

Key social stocks include the relative size and satisfaction of the middle class (both of which are shrinking, particularly in the United States), the dependency ratio (retirees and children relative to employment age adults), recent immigrants as a percentage of the population (arguably with adjustments for the extent of cultural difference between them and natives, and a nation’s historical capacity to absorb immigrants), and the level of social capital (e.g., as defined in Robert Putnam’s work).

We track four potential critical social thresholds:

• A substantial increase in immigration, due to environmental or economic causes, that could increase social tension by an order of magnitude, particularly if unemployment and government fiscal stress were already high.

• A substantial fall in the fertility (birth) rate, which could have significant negative consequences for economic growth and fiscal pressures on government budgets (due to a rising dependency ratio), particularly if health care system productivity remains low. Further ahead, changes in fertility could potentially change the size of different voting blocs and thus the political environment. For example, much has been written about the broad range of effects that China’s one child policy has had on that country.

• An order of magnitude increase in demands on social safety net programs (e.g., due to a significant rise in structural unemployment caused by a combination of technological and education system factors) and therefore government deficits, fiscal policy, and policy priorities (e.g., the sharp cutback in European defense spending in the years after the fall of the Soviet Union, which in turn changed the security balance vis-à-vis Russia).

• While most difficult to assess, the history of societal collapse suggest that the most important critical threshold in the social realm is the loss of a society’s capacity for effective collective action in the face of existential threats, when polarization and divergent group interests pass a tipping point.

Politics

Along with technological, economic, security, and social developments, the level of three stocks has a powerful effect on the political environment, and the probability of reaching critical thresholds in this area. These include the extent of political polarization between different groups, the level of confidence in key political institutions, and the amount of government debt relative to GDP.

Changes in these stocks can lead to two critical thresholds:

• If they increase beyond a critical point, polarization and mutual contempt between political factions have led to violence in many nations.

• Having lived through it in both Argentina and Venezuela, I can attest that a critical threshold exists where a sufficient number of people, in the military and security services in particular, have lost enough confidence in democracy to create the potential for other alternatives to emerge (e.g., “temporary” governments of “national salvation”).

How Close is the Macro System to These 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.

We use the UK Met Office model to communicate our assessment of how close the macro system is to the critical thresholds we have described. 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.

The following chart gives our estimate, at 31 August 2018, of the likelihood that various critical thresholds will be reached.

Conclusion

As described in our August 2018 issue, at the highest level, we believe the global macro system can be in one of four states, based on its degree of order versus disorder, and degree of social cooperation versus conflict. From that perspective, we judge the macro system to be in its most uncertain state, with a high degree of disorder and conflict in many areas. For that reason, we believe that there is a 90% probability that financial markets will shift from the normal regime to the high uncertainty regime within the next 12 months, which will trigger a fall of 20% or more in the value of all equity asset classes. It is unlikely that any other asset class will experience a gain of 20% or more. For example, for the 3 year US Treasury, the yield would have to decline from 2.82% to negative (3.24%). There is a roughly even chance that gold could be the exception, and see a 20% or higher price increase. While the apparent inflation and political uncertainty premia in the gold price today are high relative to the last 25 years, they are still below the peak reached in 2012, and it is possible that herding in the face of higher uncertainty could produce 20% price gains.

Of greater importance to investors is the financial markets regime that will emerge after the period of high uncertainty. We conclude that there is a 70% probability that it will be the persistent deflation regime. As we have noted, there are many deflationary forces at work in the macro system today, including growing excess production capacity, weakening aggregate demand, economic profits that are under growing pressure and increasingly concentrated, worsening income inequality, and dangerously rising debt levels.

As far back as our May 2001 issue (“What is a Liquidity Trap and Why Should I Worry About It?”) and November 2002 issue (“Are We Headed Toward Global Deflation?”) we have been concerned about the macro system shifting into a persistent deflation regime, like the one Japan has been in since multiple bubbles collapsed there more than 25 years ago. And central banks have worked enormously hard – and creatively – to prevent this from happening in other countries. But they could not forever forestall the arrival of the deflationary regime on their own – and the fiscal and structural reforms that should have accompanied their monetary efforts have largely been blocked by polarized and gridlocked political systems. As a result, debt/GDP has continued to increase, and real deflationary forces have grown stronger by the year.

We thus believe that the next transition from the high uncertainty regime will be to one of persistent deflation, whose asset class valuation consequences we will discuss in depth next month.

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 the high uncertainty regime has not materialized; instead, we are still in the normal regime and equity markets have continued to deliver positive returns.

How did this happen? What didn’t we anticipate happening?

• Perhaps because of an intensifying domestic debt crisis (and its own fear of Japanese-style deflation), or a belief that it had not yet achieved sufficient advantages to pursue more intense conflict with the United States, China reached a new trade agreement with the US and EU to support continued economic growth. This reverses (at least in the short-term) the growing tension in the US/China relationship, providing a strong confidence boost to the world economy and financial markets.

• A renewed focus on bipartisanship in Washington following John McCain’s funeral, along with Donald Trump’s removal from office and his replacement by Mike Pence, as well as divided party control of the US Congress after the 2018 mid-term elections led to new bipartisan initiatives to improve the productivity of the US healthcare system, address stagnant middle class incomes, and reduce high levels of concentration in many industries. All of these increase global confidence, and forestall a shift into the high uncertainty regime.

Note: Combining this Forecast with Others and Extremizing the Result Should Increase Predictive Accuracy

Research has found that three steps can improve forecast accuracy. The first is seeking forecasts based on different forecasting methodologies, or prepared by forecasters with significantly different backgrounds (as a proxy for different mental models and information). The second is combining those forecasts (using a simple average if few are included, or the median if many are). The final step, which significantly improved the performance of the Good Judgment Project team in the IARPA forecasting tournament, is to “extremize” the average (mean) or median forecast by moving it closer to 0% or 100%.

Forecasts for binary events (e.g., the probability an event will or will not happen within a given time frame) are most useful to decision makers when they are closer to 0% or 100% than the uninformative “coin toss” 50%. As described by Baron et al in “Two Reasons to Make Aggregated Probability Forecasts More Extreme”, forecasters will often shrink their probability estimates towards 50% to take into account their subjective belief about the extent of potentially useful information that they are missing.

When you average multiple forecasters’ estimates, you are including more information, which should increase forecast confidence and push the mean estimate closer to 0% or 100%. However, this doesn’t happen when you use simple averaging. For this reason, forecast accuracy is increased when you employ a structured “extremizing” technique to move the mean estimate closer to 0% or 100%.

You can download such a model from our website to extremize combined forecasts. 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: Our Asset Class Valuation Methodologies

Our asset class valuation analyses are based on the belief that financial markets are complex adaptive systems, in which prices and returns emerge from the interaction of multiple rational, emotional and social processes.

We further believe that while the financial system is attracted to equilibrium, it is generally not in this state. We believe it is possible for the supply of future returns a market is expected to provide to be higher or lower than the returns investors logically demand, resulting in over or underpricing relative to fundamental value.

The attraction of the system to equilibrium means that, at some point, these prices are likely to reverse in the direction of fundamental value. However, the very nature of a complex adaptive system makes it hard to forecast when such reversals will occur.

It is also the case that, in a constantly evolving complex adaptive system like a financial market, any estimate of fundamental value is necessarily uncertain. Yet this does not mean that valuation analyses are a fruitless exercise – far from it. For an investor trying to achieve a multiyear goal (e.g., accumulating a certain amount of capital in advance of retirement, and later trying to preserve the real value of that capital as one generates income from it), avoiding large downside losses is mathematically more important than reaching for the last few basis points of return.

Investors who use valuation analyses to help them limit downside risk when an asset class appears to be substantially overvalued can substantially increase the probability that they will achieve their long term goals. This is the painful lesson learned by too many investors in the 2001 tech stock crash, and then learned again in the 2007-2008 crash of multiple asset classes.

We also believe that the use of a consistent quantitative approach to assessing fundamental asset class valuation helps to overcome normal human tendencies towards over-optimism, overconfidence, wishful thinking, and other biases that can cause investors to make decisions they later regret.

Finally, we stress that our monthly market valuation update is only a snapshot in time, and says nothing about whether apparent over and undervaluations will in the future become more extreme before they inevitably reverse. That said, when momentum is strong and quickly moving prices far away from their fundamental values, it is usually a good indication a turning point is near.

Equities

In the case of an equity market, we define the future supply of returns to be equal to the current dividend yield plus the rate at which dividends are expected to grow in the future. We define the return investors demand as the current yield on real return government bonds plus an equity market risk premium. Given that unique local market factors have, in some countries, resulted in negative yields on real return (i.e., inflation indexed) government bonds, we use the yield on 10 year US real return Treasury bonds (TIPS) in all our equity market valuation calculations.

While this approach emphasizes fundamental valuation, it does have an implied linkage to the investor behavior factors that also affect valuations. On the supply side of our framework, investors under the influence of fear or euphoria (or social pressure) can deflate or inflate the long-term real growth rate we use in our analysis.

Similarly, fearful investors bump up our long-term equity risk premium, while euphoric investors may use a lower one.

As you can see, euphoric investors will overestimate long-term growth, underestimate long-term risk, and consequently drive prices higher than warranted. In our framework, this depresses the dividend yield, and will cause stocks to appear overvalued. The opposite happens under conditions of intense fear. To put it differently, in our framework, it is investor behavior and overreaction that drive valuations away from the levels warranted by the fundamentals.

Recognizing this, we use four valuation scenarios for an equity market, based on different values for three key variables. First, we use both the current dividend yield and the dividend yield adjusted upward by .50% to reflect the long-term level of share repurchases as a percent of equity market value. Second, we define future dividend growth to be equal to the long-term rate of total (multifactor) productivity growth (TFP). For this variable, we use two different values, 0.5% or 2%. Third, we also use two different values for the equity risk premium required by investors: 2.5% and 4.0%. Different combinations of all these variables yield high and low scenarios for both the future returns the market is expected to supply (adjusted dividend yield plus growth rate), and the future returns investors will demand (real bond yield plus equity risk premium). We then use the dividend discount model to combine these scenarios, to produce four different views of whether an equity market is over, under, or fairly valued today. The specific formula is (Current Adjusted Dividend Yield x 100) divided by (Current Yield on Real Return Bonds + Equity Risk Premium - Forecast Productivity Growth). Our valuation estimates are shown in the following tables, where a value greater than 100% implies overvaluation, and less than 100% implies undervaluation.

For emerging markets, we use basically the same approach, with these changes: (1) We do not add buybacks to the current dividend yield, as they are relatively rare in these markets. (2) We adjusted equity market risk premia to reflect the facts that Emerging Markets, over the past twenty years, have been about twice as volatile as developed markets, with which their returns have had about a .75 correlation.

In our view, the greater the number of equity market scenarios that point to overvaluation or undervaluation, the greater the probability that is likely to be the case.

Real Return Bonds

In keeping with our basic approach, we will start by looking at the theoretical basis for determining the rate of return an investor should demand in exchange for making a one-year risk free investment. The so-called Ramsey equation tells us that this should be a function of a number of variables.

The first is our “time preference”, or the rate at which we trade-off a unit of consumption in the future for one today, assuming no growth in the amount of goods and services produced by the economy. The correct value for this parameter is the subject of much debate. For example, this lies at the heart of the debate over how much we should be willing to spend today to limit the worst effects of climate change in the future. In our analysis, we assume the long-term average time preference rate for institutional investors 0.75% per year – that is, we assume that institutional investors have a long time horizon.

The risk free rate we require also should reflect the fact that there will be more goods and services available in the future than there are today – that is, the economy will grow. Assuming investors try to smooth their consumption over time, the risk free rate should also contain a term that takes the growth rate of the economy into account. Broadly speaking, this growth rate is a function of the increase in the labor supply and the increase in labor productivity. However, the latter comes from both growth in the amount of capital per worker and from growth in “total factor productivity” (TFP), which is due to a range of factors, including better organization, technology and education. Since capital/worker cannot be increased without limit, over the long-term it is growth in total factor productivity that ultimately drives the increase in productivity. Hence, in our analysis, we assume that future economic growth reflects the growth in the US labor force (0.5% per year) and TFP (which we assume will average 1.0% per year).

However, future economic growth is not guaranteed; there is considerable element of uncertainty involved. We assume a standard deviation of economic growth of 0.25%. Assuming the long-term labor force growth rate is relatively fixed, this SD assumption yields a 95% probability range for annual TFP growth of 0.50% to 1.50%. This roughly covers the most pessimistic forecasts for long-term TFP growth (e.g., by Robert Gordon) and the most optimistic (by those who believe that we have yet to see the full productivity benefits of artificial intelligence and other advanced technologies).

Finally, we need to take institutional investors’ aversion to risk and uncertainty into account when estimating the risk free rate of return they should require in exchange for letting others use their capital for one year. There are many ways to measure this, and unsurprisingly, many people disagree on the right approach to use. In our analysis, we have used Constant Relative Risk Aversion with an average value of 1.5 – which assumes that institutional investors are generally quite tolerant of risk.

The following table brings all these factors together to determine our estimate of the risk free rate investors should logically demand in equilibrium:

The specific formula is this: The risk free rate demanded in equilibrium equals time preference plus (risk aversion times growth) less (.5 times risk aversion squared times the standard deviation of growth squared).

The next table compares this long-term equilibrium real risk free rate with the real risk free return that is currently supplied in the market (we use the 10 year US real return bond (TIP) for this).

A negative spread indicates the real return bond is currently overvalued, and its price must fall in order for its yield (i.e., returns supplied) to rise.

Nominal Return Government Bonds

In this case, the supply of future fixed income returns is equal to the current nominal yield on the ten-year government bond. The demand for future returns is equal to the current real bond yield plus historical average inflation since 1933 (when the gold standard ended) plus a 0.25% premium for inflation uncertainty. We use the latter two variables as a proxy for the average rate of inflation likely to prevail over a long period of time.

To estimate of the degree of over or undervaluation for a bond market, we use the rate of return supplied and the rate of return demanded to calculate the present values of a ten year zero coupon government bond, and then compare them. If the rate supplied is higher than the rate demanded, the market will appear to be undervalued. This information is contained in the following table, which shows our valuation estimate at 31 August 2018:

It is important to note some important limitations of this analysis. Our bond market analysis uses historical inflation as an estimate of expected future inflation over the long-term. This may not produce an accurate valuation estimate, if the historical average level of inflation is not a good predictor of future inflation levels.

This risk is especially acute today, when the world economy is operating in unchartered waters, and faces both deflationary pressures (from falling demand relative to productive capacity, and significant debt servicing problems in the private sector) and inflationary pressures (from unprecedented peacetime government deficits, that are largely being financed by central banks under the “quantitative easing” programs).

Under these circumstances, one could argue that many nominal return government bonds might in fact be underpriced today, over a shorter time horizon (more likely to experience deflation), while overpriced over a longer time horizon (that is more likely to see higher levels of inflation – e.g., see the IMF study, “Fiscal Deficits, Public Debt, and Sovereign Bond Yields” by Baldacci and Kumar).

As we like to point out, in the absence of public policy interventions, over-indebtedness on the part of private borrowers typically results in widespread bankruptcies and deflation caused by the accelerating liquidation of collateral. In contrast, over-indebtedness on the part of governments more often results in some combination of inflation and exchange rate depreciation (e.g., look at the history of Argentina, or, more recently, Venezuela).

Credit Spreads

We assess the valuation of both investment grade (BBB) and sub-investment grade (BB) bonds.

The difference between the yields on BBB rated corporate bonds and 10-year US Treasury bonds, indicates the level of compensation required by investors for bearing relatively high quality credit risk. Research has also shown that credit spreads on longer maturity intermediate risk bonds has predictive power for future economic demand growth, with a rise in spreads signaling a future fall in demand (see “Credit Market Shocks and Economic Fluctuations” by Gilchrist, Yankov, and Zakrajsek).

At 31 August 2018, the average BBB spread was 1.92%. This put it in the 35th percentile of all BBB spreads since 1986 (i.e., at the low end). We assume that credit spreads follow a long-term mean reverting process. As such, we conclude that BBB bonds are likely overvalued today (i.e., BBB credit spreads will increase, and prices fall).

The difference between the yields on “high yield” BB-rated corporate bonds and 10-year US Treasury bonds indicates the compensation investors require for bearing lower quality credit risks. Problems in credit markets usually first show up here.

At 31 August 2018, the average BB spread was 2.27%. This put it in the 16th percentile of all BB spreads since 1996 (the start of the data series). We assume that credit spreads follow a long-term mean reverting process. As such, we conclude that BB bonds are very likely overvalued today (i.e., BB credit spreads will increase, and prices fall).

Commercial Property

Our approach to valuing commercial property securities as an asset class is also based on the expected supply of and demand for returns, utilizing the same mix of fundamental and investor behavior factors we use in our approach to equity valuation.

Similar to equities, the supply of returns equals the current dividend yield on an index covering publicly traded commercial property securities, plus the expected real growth rate of net operating income (NOI). A number of studies have found that real NOI growth has been basically flat over long periods of time (with apartments showing the strongest rates of real growth). This is in line with what economic theory predicts, with increases in real rent lead to an increase in property supply, which eventually causes real rents to fall.

Our analysis also assumes that over the long-term, investors require a 2.86% risk premium above the yield on real return bonds as compensation for bearing the risk of securitized commercial property as an asset class (based on studies by NAIC and NCREIF of realized returns).

Last but not least, there is also significant research evidence that commercial property markets are frequently out of equilibrium, due to slow adjustment processes as well as the interaction between fundamental factors and investors’ emotions (see, for example, “Investor Rationality: An Analysis of NCREIF Commercial Property Data” by Hendershott and MacGregor; “Real Estate Market Fundamentals and Asset Pricing” by Sivitanides, Torto, and Wheaton; “Expected Returns and Expected Growth in Rents of Commercial Real Estate” by Plazzi, Torous, and Valkanov; and “Commercial Real Estate Valuation: Fundamentals versus Investor Sentiment” by Clayton, Ling, and Naranjo).

Hence, it is extremely hard to forecast how long it will take for any over or undervaluations we identify to be reversed. The following table shows the results of our valuation analysis as of 31 August 2018. We use the dividend discount model approach to produce our estimate of whether a property market is over, under, or fairly priced today, assuming a long-term perspective on property market valuation drivers. The specific formula is (Current Dividend Yield x 100) divided by (Current Yield on Real Return Bonds + Property Risk Premium - Forecast NOI Growth).

Our estimate is shown in the following tables, where a value greater than 100% implies overpricing, and less than 100% implies underpricing.

Timber

The underlying diversification logic for investing in timber is quite simple: the key return driver is biological growth, which has essentially no correlation with factors driving returns on other asset classes. That said, the correlation of timber returns with other asset classes should be different from zero, as it also depends on the price of timber products (which depends, in part, on GDP growth) as well as changes in real interest rates and investor behavior – factors affect returns on other asset classes as well as timber.

However, in valuing timber as a global asset class, we face a number of significant challenges.

First, the underlying assets are not uniform – they are divided between softwoods and hardwoods, at different stages of maturity, located in different countries, face different supply conditions (e.g., development, harvesting, and environmental regulations and pest risks), and different demand conditions in end-user markets.

Second, the majority of investment vehicles containing these assets are illiquid limited partnerships, and the few publicly traded timber investment vehicles (e.g., timber REITs) provide insufficient liquidity to serve as the basis for indexed investment products.

Finally, the two indexes that attempt to measure returns from timberland investing (the NCREIF Index in North America, and IPD Index in Europe) are regional in coverage and utilize an appraisal based valuation methodology based on timber limited partnerships, which tends to understate the volatility of returns and their correlation with other asset classes. Given these challenges, the result of any valuation estimate for timber as a global asset class must be regarded as, at best, a rough approximation.

Our valuation approach is based on Weyerhauser (WY), the largest timber REIT that in 2015 merged with Plum Creek, the second largest. Because WY is publicly traded, our valuation approach avoids many of the problems created by appraisal-based approaches such as the NCREIF and IPD indexes. That said, for the reasons noted above, this approach is still far from a perfect solution to the asset class valuation problem presented by timber.

As in the case of equities, we compare the returns that WY is expected to supply (defined as its current dividend yield plus the expected growth rate of those dividends) to the equilibrium return investors should rationally demand for holding timber assets (defined as the current yield on real return bonds plus an appropriate risk premium for this asset class).

We note that, since WY is a listed security, investors should not demand a liquidity premium for holding it, as they would in the case of an investment in a TIMO Limited Partnership (Timber Management Organization). Two of the variables we use in our valuation analysis are readily available: WY’s dividend yield and the yield on real return bonds. The other two variables, the future rate of dividend growth and the appropriate risk premium both have to be estimated. The former presents a particularly difficult challenge.

In broad terms, the rate of timber dividend growth results from the interaction of physical, economic, and regulatory processes.

Physically, trees grow, adding a certain amount of mass each year. The exact rate depends on the mix of trees (e.g., southern pine grows much faster than northern hardwoods), on silviculture techniques employed (e.g., fertilization, thinning, etc.), and weather and other natural factors (e.g., fires, drought, and beetle invasions).

Another aspect of the physical process is that a certain number of trees are harvested each year, and sold to provide revenue to the timber REIT.

A third physical process is that, through photosynthesis, trees sequester a portion of the carbon dioxide that would otherwise be added to the earth’s atmosphere.

In the economic area, four processes are important.

First, as trees grow, they can be harvested to make increasingly valuable products, starting with pulpwood when they are young, and sawtimber when they reach full maturity. This value-increasing process is known as “in-growth.” The speed and extent to which in-growth occurs depends on the type of tree; in general, this process produces greater value growth for hardwoods (whose physical growth is slower) than it does for pines and other fast-growing softwoods. At the level of individual timber investments, the rate of in-growth is a key driver of returns; however, at the asset class level, we have decided to assume a constant mix of grades over time.

The second economic process (or, more accurately, processes) is the interaction of supply and demand that determines changes in real prices for different types and grades of timber. As is true in the case of other commodities, there is likely to be an asymmetry at work with respect to the impact of these processes, with prices reacting more quickly to more visible changes in demand, while changes in supply side factors (which only happen with a significant time delay) are more likely to generate surprises. In North America, a good example of this may be the eventual supply side and price impact of the mountain pine beetle epidemic that has been spreading through the northwestern forests of the United States and Canada.

The IMF produces a global timber price index that captures the net impact of demand and supply fluctuations. The compound (i.e., geometric) average annual change in nominal (not real) prices over the past 37 years was 2.23%, but with a significant standard deviation of 10.2% -- i.e., it is normal for timber prices to be quite volatile from year to year.

The third set of economic processes that affects the growth rate of dividends includes changes in a timber REIT’s cost structure, and in its non-timber related revenue streams (e.g., proceeds from selling timber land for real estate development or conservation easements). For example, if wood prices decline, and non-timber sources of revenue dry up (as happens during the recessions), a timber REIT (or timber LP) will have to either cut operating costs and/or distributions to investors, or increase the physical volume of trees that are harvested.

Regulatory processes also affect the future growth rate for timber REIT dividends. In the past, the most important of these included restrictions on harvesting or land development. In the future, the most important regulatory factor is likely to be the imposition of carbon taxes to limit carbon emissions. Such new environmental regulations could provide an additional source of revenue for timber REITs in the future (for an early attempt at establishing the CO2 sequestration value of timberland, see “Economic Valuation of Forest Ecosystem Services” by Chiabai, Travisi, Ding, Markandya and Nunes. For a review of similar studies, see “Estimates of Carbon Mitigation Potential from Agricultural and Forestry Activities” by the U.S. Congressional Research Service).

The following table summarizes the assumptions we make about these physical and economic variables in our valuation model:

This leaves the question of the appropriate return premium that investors should demand to compensate them for bearing the risk of investing in timber as an asset class.

Historically, the difference between returns on the NCRIEF timberland index and those on real return bonds has averaged around six percent. However, since the timber REITS are much more liquid than the properties included in the NCRIEF index, and since timber has displayed a very low correlation with returns on other asset classes (particularly during the worst of the 2008 crisis, even in the case of less liquid timber vehicles), we use three percent as the required return premium for investing in liquid timberland assets.

Arguably, because part of timber’s return generating process (physical growth) has zero correlation with the return generating processes for other asset classes, we should use an even lower risk premium. Again, we believe our approach is conservative in this regard.

Given these assumptions, our assessment of the valuation of the timber asset class at 31 August 2018 is shown in the following table. We use the dividend discount model approach to produce our estimate of whether timber is over, under, or fairly valued today. A value greater than 100% implies overvaluation, and less than 100% implies undervaluation.

We stress that this is a long-term valuation estimate that contains a higher degree of uncertainty that valuation estimates for larger and more liquid asset classes.

What About Other Commodities?

Assuming they are not going to hold large stocks of physical commodities (which is quite expensive) investors wishing to diversify into commodities face a problem. Commodity index funds invest not in physicals, but rather in a mix of commodity futures contracts that match the weights of different commodities in the index being tracked.

The return on these funds comes from three sources: (1) the return on the collateral they must deposit when they buy the futures contracts. Generally, this “collateral yield” is close to the return on short-term US Treasury Bills. (2) Unanticipated changes in the spot price of the commodities (as expected changes are reflected in the purchase price of the futures contract). And, crucially, (3), the gain or loss on the “roll yield” when the index fund sells maturing futures contracts and replaces them with new ones. 

The Roll Yield is positive when the price for the maturing contract is higher than the price of the longer-maturity contract that replaces it. Technically, this is called “backwardation” (the opposite situation is “contango”). In theory (technically, the Theory of Storage), commodities for which supply is constrained, storage is expensive, and demand is high should be backwardated.
 
However (and this is a critical however), the Theory of Storage logic assumes no change over time in the demand by investors willing to purchase futures relative to the supply of contracts sold by commodity producers. This assumption has been violated in recent years, which have seen a dramatic increase in the amount of investment committed to long-only commodity futures based index funds. 

Some observers have argued that this increase in demand for commodity futures has overwhelmed any changes that have taken place on the supply side that are driven by the Theory of Storage. They conclude that this has resulted in a permanent change in the structure of many commodity futures markets that has made contangoed conditions, and hence negative roll returns, much more likely. The data on commodity index fund returns in recent years has persuaded of the logic of this argument.

This raises serious questions about the wisdom of continuing to include futures-based commodity index products in a portfolio, in the absence of new products that do a better job of controlling for negative roll yields. Moreover, in so far as investors are allocating funds to commodity index funds as a hedge against a high inflation regime, we note that there are other alternatives – like real return bonds and commercial property – that do not suffer from the negative roll yield problem.

If you have any questions about anything we have written in this issue, please don’t hesitate to get in touch, at contact@indexinvestor.com.