The Index Investor
November 2020
Tactical Asset Allocation Implications of Our Forecasts
Recent Quantitative Indicators
Forecast Pre-Mortem Analysis
Combining and Extremizing Forecasts to Improve Predictive Accuracy
Feature Article: Guest Post: This Time It's Different - Avoiding the Costly Mistake of Not Taking Bitcoin Seriously
High Value Information Observed Last Month:
- Technology
- Energy and Environment
- Health and Disease
- Economy
- National Security
- Society
- Politics
- Financial Markets and Investor Behavior
Critical System Thresholds/Tipping Points Analysis
Appendix: Anticipatory Thinking and Forecasting Methodologies
Current Macro Forecast
The principal reasons for this change were:
(1) The announcement of a vaccine triggered what is likely an overreaction by financial markets. Over-optimism about the timing and size of its impact may also lead to behavior changes (e.g., less social distancing and masking) that further increase already fast rising COVID cases. Thus initial over-optimism may lead to another uncertainty shock that depresses economic activity.
(2) While the “Wall Street” economy may be buoyant, (e.g., the equity market) large parts of the “Main Street” economy are suffering. What most people don’t realize is the extent to which the latter economy dwarfs the former. In 2019 US corporate profits amounted to just $1.8 trillion, compared to total wage and salary income of $11.6 trillion (privately owned businesses’ proprietors income added a further $1.7 trillion). Many individuals and investors are very likely still underestimating the probability, severity, and length of the coming solvency crisis, which will very likely lead to another economic activity depressing uncertainty shock.
(3) As discussed in this month’s Evidence File, new research shows that: (a) the multiplier effect of fiscal stimulus is reduced when private sector leverage is high, as it is today, and (b) the impact of monetary stimulus on inflation has become weaker as intangibles (whose marginal cost of supply is close to zero) have become a larger part of our digitizing economy.
We have not made any changes to our 36-month regime forecasts. The probability of being in the High Uncertainty Regime remains 15% and the probability of being in the High Inflation Regime 30%. The estimated probability of being in the Normal Regime remained unchanged at 5%, and the probability the United States will be in the Persistent Deflation Regime remains 50%, as evidence continues to accumulate that the economy will very likely suffer a deeper and longer downturn than many investors expect.
With respect to the Persistent Deflation Regime, it is important to keep in mind that the headwinds that were restraining aggregate demand growth before the devastating arrival of the COVID-19 pandemic either have not improved or are worsening. These include slower population growth and faster aging, weak productivity growth (which will worsen if students’ widespread COVID learning losses are not recovered), declining labor share of GDP, rising levels of both inequality and debt, and the growing threat of job displacement as increasingly capable automation and artificial intelligence technologies are deployed.
The likelihood of the latter is now increasing, because COVID learning losses (which are very unlikely to be recovered) has further widened the gap between the rate at which AI and automation technologies are improving and the rate at which human capital is improving. This will only make it more difficult to meet the challenge of creating a digital economy that generates a large number of well-paid jobs.
Also, four years of divided US government (assuming the Democrats don’t win both Georgia Senate runoff elections on January 5th) increases the probability that fiscal stimulus will very likely be relatively small (which will worsen the coming insolvency crisis), and that critical structural headwinds will very likely not be meaningfully reduced.
Whether the High Inflation Regime comes to pass will depend on the interaction between COVID19’s shocks to demand and supply, along with even more important political factors.
In our view, the most logical cause of a return to the High Inflation regime over the next 36 months would be a severe crisis of confidence in the US government and/or economy that causes a flight from the US dollar and a sharp rise in the price of imported goods (and almost certainly gold as well), or the outbreak of open conflict between the China and the United States which severely disrupts global supply chains. Regarding the latter, key developments this month included:
(1) New evidence that, because of the headwinds facing its economy, China will struggle to escape the middle-income trap without must faster innovation to drive economic growth;
(2) Xi Jinping’s prioritization of faster innovation at the October Plenum of the Chinese Communist Party;
(3) The last minute cancellation of Ant Financial’s record breaking $37 billion IPO after its founder, Jack Ma, challenged government financial regulators (and thus the CCP and Xi). Along with reports of record long delays in collecting their accounts receivable, Ma’s treatment will very likely dampen the enthusiasm of the entrepreneurs who Xi is counting on to deliver faster innovation.
(4) Weak economic growth is also very likely to increase middle class frustration with Xi, which might further tempt him to deflect their ire by undertaking more external aggression towards Taiwan, and thus global uncertainty.
In reviewing the current situation, we again emphasize that when uncertainty is high people rely more heavily on social learning and copying what others are doing. Not only does this slow the diffusion of new information throughout social systems like economies and financial markets, but it also causes these systems to coalesce around a small number of increasingly fragile narratives.
Under these conditions, rapid, non-linear changes are very likely to occur.
Portfolio Allocation Implications of Our Forecast
We take two approaches to deriving the tactical asset allocation implications from our analyses (i.e., deviations from our "neutral" or base case model portfolio).
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 5% allocations 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.
Three 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.
Third, we continue to be deeply concerned by the distortion in asset class valuations that have been created by negative real interest rates on sovereign bonds, which are the foundation of most asset pricing models. In August, we decided to address this distortion by using in our asset class valuation models our estimate of the economically logical real yield on inflation protected US government bonds (TIPs). This brings our quantitative valuation conclusions much closer to those based on our qualitative analysis.
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:
To expand our comment about timber. WY’s dividend was suspended when the COVID pandemic arrived. Up to that point, timber had very likely been undervalued. We assume the dividend will resume; hence it is very likely still is undervalued. However, if the dividend suspension is permanent, then it is almost certainly overvalued.
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Forecast Logic: Quantitative Indicators
Implications of the Most Recent Three Month Asset Class Returns
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 belief in the probability that a given macro regime will develop in the future.
At the end of last month, rolling three-month returns on different asset classes implied that the marginal investor viewed the Normal and High Uncertainty regimes as the most likely to develop in the future. For the reasons noted above, we disagree with this assessment and the implicit assumptions on which it rests.
Asset Class Valuation and Momentum Indicators (@30Oct20)
| Asset Class (ETF) | Valuation | 1 Month Return | Conclusion |
| US Real Return Govt Bond (TIP) | Almost Certainly Overpriced* | (0.67)% | Decreasing Overvaluation |
| US Nom Return Govt Bond (GOVT) | Very Likely Overpriced* | (1.10)% | Decreasing Overvaluation |
| US Investment Grade Credit (LQD) | Likely Underpriced* | (0.51)% | Increasing Undervaluation |
| US High Yield Credit (HYG) | Almost Certainly Overpriced* | 0.40% | Increasing Overvaluation |
| US Commercial Property (VNQ) | Within Fairly Priced Range* | (3.00)% | Fairly Valued |
| US Equity (VTI) | Almost Certainly Overpriced* | (1.95)% | Decreasing Overvaluation |
| Foreign Devel Mkt Equity (VEA) | Likely Overpriced* | (3.55)% | Decreasing Overvaluation |
| Emerging Markets Equity (VWO) | Almost Certainly Overpriced* | 1.32% | Increasing Overvaluation |
| Timber (WY) | Almost Certainly Overpriced* (due to temporary dividend suspension) | (4.31)% | Decreasing Overvaluation |
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 (@30Oct20)
| Market Stress Indicator | This Month vs Last Month |
| Asset Class Returns Autocorrelation (this month versus last month). Higher autocorrelation is an indicator of more tightly coupled and fragile markets. | (.43) vs (.73) the previous month. This indicates a substantial decrease in the level of market stress. |
| Economic Policy Uncertainty Index (how many days over the last 30 was index in top quartile of values since 1985?). A higher number equals more market stress. | On 30 days last month the index was in the top quartile of daily values since 1985 (the 99th percentile of all rolling 30-day periods). |
| AAA Rated Bonds Spread over 10 Year Treasury Yield (month end). Higher spreads indicate rising concern about market liquidity. | 1.51% (64th percentile since 1983), vs 1.63% (71st) at the end of the previous month, indicating a decreasing level of market stress. |
| BB Rated Bonds Spread over 10 Year Treasury Yield (month end). High spreads indicate increasing credit risk. | 3.84%, (61st percentile) down from 4.00% last month, indicating a decreasing level of stress. |
| Gold Price per Ounce in US Dollars (month end). Rising gold prices are an indicator of increasing market uncertainty and stress. | $1,876 vs $1,883, essentially unchanged from the previous month. At the end of 2017, we estimated the “disaster premium” in the gold price was 47% (see our methodology in the Appendix). At the end of last month it was 92%. |
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?
- A number of world leaders – Xi Jinping 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.
- On the other hand, the removal from office Xi Jinping and/or the election of Joe Biden could lead to a reduction in the dangerously growing conflict between the two nations. This would somewhat reduce the probability of both the Persistent Deflation and High Inflation Regimes, and raise the relative probabilities of the High Uncertainty and Normal Times Regimes.
- 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 is a reduction in oil supplies due to a prolonged kinetic conflict between Iran and the US. An unlikely scenario could be major crop failures associated with the next solar cycle, which NASA forecasts will be the weakest in 200 years. McKinsey recently concluded that the probability of such a failure has increased due to changes in the environment, and now stands at about 10% over the next five years ("Will the World’s Breadbaskets Become Less Reliable?”).
- 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 increasing deficit monetization and policy paralysis if a severe downturn continues without meaningful policy reforms to address critical structural challenges. However, a sharp rise in import prices due to a collapse in the USD exchange rate also requires relatively higher confidence in another currency, with the Euro being the most likely candidate and cryptocurrencies an outside possibility. This currently seems unlikely, given both the Eurozone’s economic and political uncertainty, and the prospect of an intensifying conflict between the West and China. If confidence collapsed in all major currencies, the price of gold would rise, and price inflation would only occur in terms 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.
Guest Feature Article: This Time It's Different - Avoiding the Costly Mistake of Not Taking Bitcoin Seriously
by Ned Horsey
This month we’re doing something different. I’ve long wanted to write a series of articles exploring cryptocurrencies, like Bitcoin and its competitors. With the unprecedented amounts of sovereign debt being issued to fund the fiscal response to the COVID-19 pandemic, uncertainty about future confidence in fiat currencies has increased.
One result of this is increased recognition of the potential role of gold in a portfolio, as a hedge against widespread loss of confidence in “traditional” currencies. However, some analysts have argued that crytocurrencies can also play this role.
A number of subscribers have recommended Ned Horsey as a young analyst who can effectively make this case. He has graciously provided this article for our readers to consider.
In a future article, we will also present the other side of this discussion, and present the case for not (or perhaps not yet) including crypto in a portfolio.
But for now, it’s over to Ned.
$10,000 invested in APPL a decade ago would yield $115,000 in returns today, the same amount of money invested in Bitcoin would have returned $1.3bn. As of 9/4/2020 Bitcoin is up 44% for 2020, aside from the 6 weeks of Bitcoin’s 2018 blow off top (a few trades cleared at $20,000), the current price range of $10,400-$11,000 represents a decade long all time high. By any reasonable metric (energy usage, user base, valuation, ecosystem growth, institutional adoption or legal clarity) Bitcoin appears healthy, growing, and becoming more valuable on a daily basis.
Given these rosy facts, the question remains, why is Bitcoin absent from most large portfolios? Even a small allocation of an asset that goes from a $0 to $10,000 valuation could substantially change portfolio returns, more so if it uncorrelated with traditional financial assets. The answer is that Bitcoin is new, strange, associated with anti-social behavior, and did not have a clear regulatory status until recently.
The purpose of this analysis is twofold:
(1) To explain the nature and properties of Bitcoin in a general, no technical way to give an investor confidence to start their own research
(2) To propose a specific portfolio strategy for a Bitcoin allocation, namely, a gold-type inflation and macro hedge
1. What is Bitcoin?
The emergence of Bitcoin is most analogous to the emergence of the internet and e-commerce in the 1990s. Both are built on new, poorly understood (outside of a few technical communities) technologies. Both rely on network affects that drive increasing adoption.
Because network effects are non-linear, it’s a difficult trend to catch. Come in too early and you invest in Pets.com, too late and you are buying FANG stocks at their 2020 highs. The only way to evaluate our current place on the adoption curve is to understand the technology, it’s capabilities, the context it exists in, and finally make an investment thesis.
Bitcoin is a token similar to the digital dollars or euros in your bank account. Bitcoins or fractions of a Bitcoin can be transmitted directly to any participant in the Bitcoin network through a communications channel.
Scarcity
There will only ever be 21 million Bitcoins. 90% will be issued by December 2021, but each coin is divisible into 100 million sub-units so there are enough units to fully represent the entire global economy.
Bitcoins are fundamentally scarce, there is literally no way to create additional Bitcoins as their cryptographic nature makes them unforgeably costly. If the price of Bitcoin rises to six figures, there will still be no way to mint additional units.
Bitcoins can, however, be lost if the secret key that controls a Bitcoin is misplaced or destroyed. This means that over time, the total number of usable Bitcoins will decrease, which transfers the purchasing power of lost Bitcoins to holders who have stored their Bitcoins carefully, via deflation.
Final Settlement
The technical innovation of Bitcoin is that transactions are final.
Similar to paying cash for an item, there are no charge backs, and no third parties can censor transactions. This means that Bitcoin is a trustless medium of exchange, unlike say a credit card, so two parties can exchange large amounts of value without the fear of a charge back or third party blocking the transaction.
Trustless systems are highly preferable when it comes to financial transactions. This means that two parties do not need to trust each other to transact, they don’t even need to know significant details about each other, merely the relevant information to perform a Bitcoin transaction.
The concept of trustlessness is very foreign in the context of traditional banking. The bank can change your balance, deduct fees, and in the case of bankruptcy, even fail to return a depositor’s money. So traditional finance runs on trust, KYC, and entails taking significant custodial risk. Bitcoin changes this.
Previous to the invention of Bitcoin, there was no such thing as an irreversible digital payment. Paypal, Visa, ACH and wire transfers are all ledger entries on a third parties balance sheet. In the case of a dispute, lawsuit, or other breakdown of trust, participants in any of these transactions can attempt to recover there funds. This means that most digital payment mediums are not suitable for large transactions since there is a possibility that a buyer can take possession of the asset and then initiate a charge back.
Bank payments, while harder to reverse than a credit card, are similarly subject to censor and reversal in the case of political crisis, regulatory encroachment, and bank failure. Prior to Bitcoin, the most final transaction possible was to exchange physical gold, which is still how sovereign nations settle some transactions.
Bitcoin as it exists today, creates transactions that are as final as central bank gold settlement transactions, but can be participated in by anyone and executed online for relatively small fees (much smaller than a wire transfer fee at the moment).
Validated and Genuine
Bitcoin transactions are public and can be validated, meaning that the entire supply of Bitcoin is known at every point in time and there is no ability to inflate the supply and tax holders of Bitcoin via inflation. It is also impossible to force a fake Bitcoin transaction to publish on the Bitcoin blockchain. Bitcoin is cryptographically verifiable, meaning that as long as participants in a Bitcoin transaction are both independently verifying the network (easily achievable with a basic personal computer), it is impossible to falsify Bitcoin transactions.
This overcomes a weakness of physical gold settlement, namely the incentive to counterfeit gold bars and adulterate the gold with tungsten or another heavy metal. Having metallurgical verification of gold transactions is prohibitively expensive, on top of the high cost of security and impracticality of using gold as a monetary instrument. Paper money backed by gold was a 19th century innovation to increase the usability of gold as a monetary medium but opened the door for monetary default and inflation.
Underlying Technology
Bitcoin is actually a distinct technology that uses a blockchain to create a decentralized network of trustless participants. The structure of the network and its rules incentivize cooperative transaction as breaking the rules is not profitable and can lead to loss of funds.
Since the creation of Bitcoin, there has been a ‘blockchain not Bitcoin’ narrative, which can be summarized as “Bitcoin’s core technology is interesting, but it’s similar to MySpace or Napster, a prototype for a business model that will eventually become successful.”
Nothing could be further from the truth.
In fact, blockchain is an old and simple technology. A blockchain is a distributed database that updates in regular intervals called ‘blocks’ and stamps each block with a time. If this sounds underwhelming, then the mass failures of ‘blockchain not Bitcoin’ startups from 2018 will not be a surprise to you. Blockchain is a solution in search of a problem, a way to create decentralized, slow, database systems that happen to be great platforms for decentralized money, but not much else.
Bitcoin’s technological innovation is not blockchain or decentralized networks. It’s not a single technological innovation that can be stolen and repackaged into a disruptive startup. The genius of Bitcoin is it’s design, a system that weaves together public key cryptography, blockchain, distributed networks, and game theory. The combination of these well-understood technologies has created something new: a way to hold value and transact via the internet without any authorities or intermediaries.
Network Structure
This general description of the Bitcoin network is intended as an introduction to the basic network structure and game theory. If you don’t want to learn what a ‘Node’ is, skip ahead.
Nodes are the fundamental unit of the Bitcoin network. A node is a computer that has a copy of the Bitcoin blockchain and connects to other nodes to share new transactions and receive new blocks.’
Nodes can be thought of as users, since a node can be run on basic computer hardware such as a laptop, cloud server in AWS, or even a cellphone. However, participating in a Bitcoin transaction does not require a node, but it is the most secure way to participate.
Some nodes are miners who attempt to win newly emitted Bitcoins by processing pending transactions into a new block with a timestamp that the miner tries to add the Bitcoin blockchain.
This newly created block connects to the previous block using a cryptographic signature, this creates the blockchain as all of these cryptographically frozen, time stamped blocks are connected together via cryptography. Changing a block in the past invalidates every single block that comes after it, meaning that the Bitcoin blockchain is frozen once a block is mined and then transmitted to all of the nodes in the network.
Miners validate transactions but nodes validate blocks. This creates a counterbalance to miners’ power on the network because if miners attempt to change the rules, then nodes will reject these rule-breaking blocks.
Changing the blockchain is impossible after a few blocks because an attacker would have to “dig down” into previous blocks of the chain, re-construct the target block, then reconstruct every block afterwards with new signatures (since changing any element of a block alters it’s cryptographic seal and every block afterwards in a cascading fashion), and then race to construct the next block before any other miner.
Bitcoin mining is a competition for receiving the next scheduled emission of Bitcoins. If you make a block quickly and win this competition, then you receive this compensation for processing transactions. Cheaters doing extra work to fake transactions cannot statistically win all of the new blocks and so once the non-cheating chain wins a block, all of the cheaters’ work is invalidated.
There is simply no way profit in attempting to cheat on the Bitcoin blockchain. There isn’t even a way to use more computing power to ‘cheat’ as the Bitcoin network uses more cryptographic compute than any network in the world, and far more than the entire supply of global supercomputers.
This emission of new Bitcoins is predictable and written into the code of the software, so this is not surprise inflation. Bitcoins need a route into the world, and since miners are providing a commodity service by processing new blocks for the network, they generally need to sell all of the coins they receive to pay for electricity and computer hardware.
The electricity that miners sacrifice to hash the blocks of the Bitcoin blockchain is directly proportional to the cryptographic security of the network. As more miners compete for Bitcoins, the network becomes more cryptographically secure, meaning the cryptographic signature on each Bitcoin block becomes harder to discover, while the rate of Bitcoin emission stays the same.
That was quite confusing, what’s the takeaway?
For a functional understanding of Bitcoin technology, it’s sufficient to understand that Bitcoin is a combination of several technologies including blockchain to create an adversarial network that incentivizes productive cooperation.
Nodes validate the blocks of the blockchain so that miners can’t just make up transactions. Nodes also store the entire chain so that the network can be resurrected from a single node if it is attacked.
Miners secure the chain, create a fee market for transactions, and spend real money in their operations, which ties Bitcoin activity to real world energy markets. They also distribute Bitcoins to pay their costs to secure the Bitcoin blockchain.
Most users don’t have a node, so it’s possible to use the network with little investment in hardware and understanding, but for users with large amounts of Bitcoin wealth, it’s possible to fully validate the chain and take full self-custody of their Bitcoin assets.
No one can steal a Bitcoin by attacking the Bitcoin blockchain, the cryptography is too costly to subvert, the network too distributed to fool, and the adversarial structure of Bitcoin makes it difficult to create an incentive for groups of users to collaborate to attack the network.
This system is unique in producing the only absolutely scarce asset in the world, which can also be self-custodied by the holder, transacted with no intermediaries, is distributed, robust and difficult to attack.
More gold is mined every year, more dollars created every minute, but there will never be more than 21 million Bitcoins.
Why is Bitcoin here to stay?
Better Macro Hedge than Gold
Gold has been the crisis hedge of choice for the last 5000 years of human history. However, during extreme economic crisis such as the Great Depression in the United States, privately held gold assets were confiscated. Gold holders were sold dollars at the official dollar to gold conversation price, then the US dollar was taken off the gold standard and the price of gold went parabolic. Investors were deprived of their inflation hedge and experienced a lost off purchasing power
Today, the proliferation of gold ETFs and centralized bullion vaults like the LBMA make seizure and manipulation of gold assets during a demonetization event easier than ever before in history.
For the minority of gold holders who take physical possession of their specie, securing these assets is expensive, dangerous, and significantly reduces the liquidity of their asset.
Bitcoin can be physically held by anyone who can remember 12 words in their head, it is not bound by capital controls and cannot be seized once physical possession is taken.
Network Effects Kill Competing Crypto Currencies
In 2017 it was not clear if Bitcoin was scarce, in the sense that competing cryptocurrencies also promised digital scarcity, immutability, and censorship resistant transactions; some even offered new features such as private transactions, or complex ‘smart’ contracts.
These competing projects resulted in Bitcoin dominance (the percentage of total cryptocurrency market cap attributed to Bitcoin) briefly falling to 40%. While market cap is a poor measure due to the ease of small float manipulation, this generally indicated some uncertainty as to the dominant cryptocurrency moving forward. Over the past three years, every other cryptocurrency has trended downwards, buoyed up in price only when Bitcoin rallied, and Bitcoin dominance is now 80%.
The analogy of Bitcoin network effects is similar to social networks: it is difficult to start a new social network in a world of Facebook, since the network effect of Facebook users gives it an insurmountable first mover advantage. A social network without users is worthless. Conversely as a social network gains users, it has more utility, attracting more users, resulting a virtuous cycle of adoption and value creation.
Illiquid small float cryptocurrencies with centralized development teams and VC backers can stave off the death of their blockchain by burning cash. A marketing budget can help buy adoption and run the network at a loss in the face of low organic demand. But network effects cannot be bought, beyond the small window at the beginning of an exponential adoption process were minute advantages compound over time. It’s now been 10 years and no cryptocurrency seems able to counter Bitcoin’s market dominance.
Actual Utility
The first uses of Bitcoin were to purchase illegal goods on dark web markets in 2011. No other payment mechanism in the world was capable of transacting in such an illegal, untrustworthy, unregulated environment. While this was initially seen as evidence of Bitcoins ‘illegitimacy’ it actually highlights the technological innovation of exchanging value digitally, without fear of buyer chargeback, payment processor censorship, and requiring very little trust between participants.
Fast forward to 2020 and Bitcoin is a means of avoiding US financial sanctions, Chinese capital controls, and Wirecard COO Jon Marsalek used Bitcoin to flee with his ill gotten gains to Russia, a situation where traditional money transmission would have prevented his financial exit while under investigation.
Other than black and grey market utility, Bitcoin is the primary reserve asset of MicroStrategy, which invested over $400 million, and will likely show up on the balance sheet of more global companies as central bank monetary expansion increases currency risk and inflation expectations. Because Bitcoin is independent of the entire legacy financial system its an ideal uncorrelated asset to reduce tail end financial risk.
Security and Decentralization
Bitcoin is provably secure. The Bitcoin blockchain, if compromised, would be the most profitable hacking target in the world. It’s never been hacked and the mining hash rate that secures the Bitcoin blockchain is the most powerful cloud compute network in the world. In 2013 Bitcoin mining exceeded the capacity of the world’s top 500 super computers and has grown from 61,000% since.
Bitcoins have only been stolen in 2 situations: when an exchange or centralized custodian of Bitcoins mismanages their IT infrastructure and allows hackers to control their Bitcoin withdrawal system. Or when users have used compromised hardware or software to store their Bitcoin private keys.
While securely storing a private key is mentally complex (the discipline to write down a secret and then never allow that secret onto the internet is the core problem), it is achievable without monetary investment, i.e. a skill that can be developed as opposed to a security flaw.
Bitcoin security, because it involves private keys (large alphanumeric secrets) can also be distributed in complex multi-signature schemes, for example a broker, a client, and her lawyer might all have a key. Any two keys can initiate a transaction, but the client’s key is a ‘master’ key that can transact on it’s own. This security technology will develop into ever secure, ever distributed models that contrast the naivete of the legacy financial model: absolute trust in third party custodians, banks, and brokers.
Bitcoin is a decentralized network, meaning that banning Bitcoin or restricting its use is only possible by restricting existing regulated financial entities. Physically attacking the network is impossible as there are over 10,000 Bitcoin nodes world wide (and as many as 45,000 based on some estimates) and even if 99% could be destroyed a single surviving node preserves the entire transaction history and thus can ‘resurrect’ the network. This makes Bitcoin an anti-fragile network in contrast with the legacy financial system that is vulnerable to shocks that can wipe out whole countries worth of financial history (can anyone confirm the ownership of plots of land in Caracas or the trading accounts of Lehman Bros clients).
But what about quantum?
Quantum computing is still in a proof of concept phase as evidenced by the two most publicized projects disagreeing on simple benchmarks of quantum test performance. Whether or not quantum is a field that will eventually disrupt traditional O(N) cryptographic problems, there is not yet irrefutable proof that current quantum computer prototypes can even theoretically outperform current silicon electric computers.
Furthermore, cryptography is not a fast moving, disruptive industry, because of the sensitivity of data privacy and the huge risks to making changes to cryptography, as quantum develops it will be slowly integrated into the cryptographic literature and then the standard cryptographic software libraries. It is very unlikely that some breakout startup or inventor will crack quantum computing and then immediately break all global cryptography (this would also probably end civilization as it currently exists). Given that the major players in this space are IBM and Google; they will need to quantum harden their own systems, which will be a massive infrastructure upgrade involving closed and open source software.
Quantum may be an issue in the future, but it will be an issue for all computer systems and if it does deliver, it will be a slow rolling technological upgrade as opposed to a sudden disruption.
The Bitcoin Investment Thesis
1) Scarcity – Inflation Hedge and Currency Risk
There are 3500 publicly traded companies and there’s $5Tr in their treasuries and it’s all melting and at some point, you have a fiduciary obligation to not lose the money.
– Michael Saylor
MicroStrategy CEO Steven Saylor recently made a $250 million allocation into Bitcoin as the company’s new ‘primary reserve asset.’ His corporate treasury of $500m was not creating any value premium to his stock while large corporations are awash in cheap liquidity.
Simultaneously, traditional cash management strategies such as money market accounts and T-bills yield nearly zero interest. Mr Saylor described this situation as ‘holding a melting ice cube.’
Inflation expectations are ticking up while yields on safe assets are suppressed and equity markets are increasingly volatile. Holding dollars is also less attractive as government fiscal interventions increase money supply at historic multipliers. Bitcoin offers the inflation hedge of gold with more upside as well as assurances to it’s scarcity.
The most surprising element of MicroStrategy’s Bitcoin allocation, was not that it happened, but that Steven Saylor opted to go all in with his treasury. A portfolio manager might more reasonably start with a 1% allocation in order to taste asymmetric upside price movements but not experience the high volatility.
The huge corporate balance sheet allocation of MSTR makes more sense, if you consider that MSTR generates large amounts of cash, so in an environment of low yields for safe assets, making a large speculative bet on Bitcoin is rational, given MSTR’s ability to rebuild it’s balance sheet.
2) Network Effects – Adoption and Value Accrual
Like the internet of the 90s, all the pieces for Bitcoin to accrue massive global value and utility are visible. It has clear dominance in terms of cryptocurrencies, it can scale as a network for handling global payments, and it has already made 130,000% returns for early investors All this at a market cap of ~$200bn, which is less than Microsoft Inc. Clearly Bitcoin offers more global value than Office 365 so it is an asymmetric bet on future price accrual.
Bitcoin hashrate, the cryptographic security purchased with actual dollars using electricity and computer hardware, is orders of magnitude greater than the next largest chain, an indication of the strong network effect of Bitcoin relative to other cryptocurrencies.
Similarly Bitcoin activity is also dominant relative to the next largest chain, which together represent over 90% of total cryptocurrency activity. This winner take all dynamic of Bitcoin vs other cryptocurrencies is a corollary to the current USD world order, where the USD represents 79% of global transactions. Money has always been a network effect driven phenomenon, and Bitcoin follows the traditional model of a single currency accruing 80% of the total market.
3) The Stock to Flow Model and Low Cross Asset Correlation
The anonymous Dutch quant PlanB has created and back tested models to explain the price of gold, silver, and Bitcoin as a function of their various stock to flow ratios. Because Bitcoin’s issuance is front loaded, more than 18 million of the total 21 million Bitcoins have already been emitted, further demand/monetary inflows chases an ever fewer number of units. This creates a price feedback loop that results in blow off top bull markets that then calm to a new, higher, price floor.
PlanB’s model co-integrates and explains over 90% of historical Bitcoin variance. It also predicts a six figure Bitcoin price in 2021. While it is not clear if this bullish model will track future Bitcoin cycles, the emission schedule of Bitcoin, coupled with it’s absolute scarcity suggests that new monetary inflows highly stimulate price while a larger user base creates ever higher price floors.
From a macro perspective the importance of PlanB’s work is that there is a modeled, analytical framework to evaluate Bitcoin’s future price action. This is no longer a seed-stage project, rather a growing asset class under the microscope of institutional analysts.
Bitcoin’s low correlation with the DXY, S&P500, and other traditional asset classes, along with it’s asymmetric upside potential make it a candidate for a small portfolio allocation.
An interesting recent development is Bitcoins weakly negative correlation with VIX, making it a potential component of volatility based strategies.
The Risks
After 10 years of software development and 99.99% network uptime, the base Bitcoin technology is stable and secure. The likelihood of a catastrophic technological bug destroying Bitcoin value is low at this point and reducing over time as the network develops.
The largest price risk to Bitcoin investors is regulatory. Restrictions on financial institutions’ ability to trade and custody Bitcoin could temporarily reduce the dollar price appreciation of Bitcoin. However, regulatory momentum seems to be moving towards legitimizing Bitcoin as a financial asset in the United States. The OCC has clarified that banks and financial institutions can provide custodial services for cryptocurrencies and related businesses.
The other major price risk to Bitcoin is that the US Federal Reserve somehow manages to reflate the US stock market and property bubbles. If these markets get sufficiently frothy, this may attract speculative money that would have otherwise been directed towards Bitcoin.
The Next Decade: Bitcoin and Inflation
The Bitcoin bull in 2018 was capitalized on by a large number of ICO (initial coin offering) scams that obscured the value proposition of Bitcoin due to the sudden proliferation of blockchain projects. Bitcoin’s recovery of network share since could be a sign of its long-term value proposition.
This time, as Bitcoin breaks out to statistically significant historical upside, it is different. Increasing integration with financial institutions and legal clarity means that this bull market has a wider and deeper pool of potential participants. Furthermore, this bull market coincides with stress in traditional markets and levels of speculation hereto unseen, suggesting significant potential upside.
Bitcoin is perhaps the best inflation hedge that currently exists. Its absolute scarcity combined with its digital nature make it ideal for securing value as currency and political risk increases. Both gold and Bitcoin accrue value in bull runs that never fully unwind, setting successive price floors with each market cycle.
There is no realistic path for the US Government to reduce its budget deficits, which means that increasing fiscal and monetary cooperation will result in debt monetization. Inflation hedges work best when put on before inflation hits the tape. That’s why Bitcoin is an option.
High Value Information Observed In October 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, health, 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 (albeit with multiple feedback loops) the effects we observe in investor behavior and financial market valuations and returns.
To generate alternative future scenarios and critical forecasting questions,we use this framework to identify multiple paths across these issue areas, including alternative outcomes for critical uncertainties.
In our methodology, we take a Bayesian approach, and 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, try to systematically search for high value indicators that disconfirm our prior views.
| New Technology Information: Indicators and Surprises | Why Is This Information Valuable? |
| “Why Modeling the Spread of COVID is So Damn Hard” by Matthew Hutson | The author begins by acknowledging the elephant in the room: “Too many of the COVID-19 models have led policy makers astray.” He then presents a clear explanation of the shortcomings of two common modeling approaches. “A SEIRS model puts people into categories: susceptible (S), exposed (E), infected (I), removed from the susceptible population (R), and potentially back to susceptible (S) again, depending on whether a recovered person has immunity from the disease. The modeler’s job is to define the equations that determine how people move from one category to the next. Those equations depend on a wide variety of parameters drawn from biology, behavior, politics, the economy, the weather, and more.” Unfortunately, this system is evolving over time. If the parameters aren’t accurately updated at the same speed the system is evolving, the model becomes progressively less accurate. Machine learning doesn’t start with a causal model; rather it starts with a large amount of training data for multiple variables, and a set of target variables to predict, and then uses techniques like neural networks to maximize predictive accuracy. However, these models suffer from three shortcomings: (1) a lack of training data about pandemics; (2) a complex system that is constantly evolving; and (3) the lack of a clear causal explanation of how the model predicted the future results from the input data. Hutson is most encouraged by agent-based models. “These are much like the video game The Sims. Each individual in a population is represented by their own bit of code, called an agent, which interacts with other agents as it moves around the world. “One of the most successful agent-based models was designed at the University of Sydney. The model has three layers, beginning with a layer of demographics. ‘We’re essentially creating a digital twin for every person represented in the census,’ said Mikhail Prokopenko, a computer scientist at the university. He and his colleagues built a virtual Australia comprising 24 million agents, whose distribution matches the real thing in terms of age, household size, neighborhood size, school size, and so on. “The second layer is mobility, in which agents are assigned to both a household and a school or workplace. On top of demographics and mobility, they add the [third layer] disease, including transmission rates within households, schools, and workplaces, and how the disease progresses in individuals… “When set in motion, the model ticks twice a day: People come in contact at school or work in the daytime, then at home at night. It’s like throwing dice over and over. The model covers 180 days in a few hours. The team typically runs tens or hundreds of copies of the model in parallel on a computing cluster to generate a range of outcomes. “The biggest insight was that social distancing helps very little if only 70 percent of people practice it, but successfully squashes COVID-19 incidence if 80 percent of people can manage it over a span of a few months…Agent-based models look ideal for simulating possible interventions to guide policy, but they’re a lot of work to build and tricky to calibrate.” |
| “Heterogeneous Multi-Agent Reinforcement Learning for Unknown Environment Mapping”, by Wakilpoor et al | This paper reports important progress in combining reinforcement learning (a common machine learning method) with agent based modeling, to automatically develop situation awareness in an evolving complex environment. Specifically, unmanned aerial surveillance platforms constantly collect and share data about the evolving environment, and use a novel reinforcement learning algorithm to update their collective understanding of the situation and then use it to adjust their behavior to accelerate their learning. The authors note that from these agent interactions more efficient, cooperative behaviors emerge, without human intervention. |
| “Deep Generative Modeling in Network Science with Applications to Public Policy Research”, by Hartnett et al from RAND “Network data is increasingly being used in quantitative, data-driven public policy research. These are typically very rich datasets that contain complex correlations and inter-dependencies. This richness both promises to be quite useful for policy research, while at the same time posing a challenge for the useful extraction of information from these datasets - a challenge which calls for new data analysis methods. In this report, we formulate a research agenda of key methodological problems whose solutions would enable new advances across many areas of policy research. We then review recent advances in applying deep learning to network data, and show how these methods may be used to address many of the methodological problems we identified. We particularly emphasize deep generative methods, which can be used to generate realistic synthetic networks useful for microsimulation and agent-based models capable of informing key public policy questions.” | It is increasingly clear that improvements in agent based modeling technologies, especially when they are combined with machine learning, are very likely to have a substantial impact on our ability to understand and predict the behavior of complex adaptive systems. This new paper is an excellent overview of the significant progress that has been made in this area in recent years, as well as the critical obstacles that have not yet been overcome and key indicators to monitor. |
| “Less Than One-Shot Learning: Learning N Classes from M “Deep supervised learning models are extremely data-hungry, generally requiring a very large number of samples to train on. Meanwhile, it appears that humans can quickly generalize from a tiny number of examples. “Getting machines to learn from ‘small’ data is an important aspect of trying to bridge this gap in abilities. “Few-shot learning (FSL) is one approach to making models more sample-efficient. In this setting, models must learn to discern new classes given only a few examples. “Further progress in this area has enabled a more extreme form of FSL called one-shot learning (OSL); a difficult task where models must learn to discern a new class given only a single example of it. “In this paper, we propose ‘less than one’-shot learning (LO-shot learning), a setting where a model must learn N new classes given onlyM < N examples, less than one example per class… “We show that this is achievable.” | SURPRISE Today, improvements in both the capabilities of machine learning technologies and their application are held back by three broad constraints. First, they have required large amounts of training data. Second, they have been based on associative reasoning, rather than more powerful causal and counterfactual reasoning. Third, they have required large amounts of computing power (“compute”). This paper shows that the first constraint is on its way to becoming much less binding. |
| “Algorithms for Causal Reasoning in Probability Trees”, by Genewein et al from DeepMind | SURPRISE This paper is an example of progress towards relaxing the second constraint noted above. “A probability tree is one of the simplest models for representing the causal generative process of a random experiment or stochastic process The semantics are self-explanatory: each node in the tree corresponds to a potential state of the process, and the arrows indicate both the probabilistic transitions and the causal dependencies between them… “Our work is the first to provide concrete algorithms for (a) computing minimal representations of arbitrary events formed through propositional calculus and causal precedences; and (b) computing the three fundamental operations of the causal hierarchy, namely conditions, interventions, and counterfactuals.” |
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| New Energy and Environment Information: Indicators and Surprises | Why Is This Information Valuable? |
| “Shale Binge Has Spoiled US Reserves, Top Investor Warns” (Financial Times, 12Oct) and “Harold Hamm Hits Back at ‘Crazy’ Shale Claims” (Financial Times, 15Oct) The first column noted the claim by Will van Loh, CEO of Quantum Energy Partners (one of the largest energy focused private equity firms) that drilling wells too close together and fracking them too aggressively has significantly reduced reservoir pressures, and thus the future production capacity of many shale basins. In the second column, Harold Hamm, CEO of Continental Resources, a shale oil pioneer, denied van Loh’s claim. | SURPRISE This spat is actually about something much larger and more important than the future productivity of different shale oil basins. The valuation of energy companies by financial markets misses the forest for the trees. Energy is the economy, because the economy runs on energy. Technology only converts energy into work. Ultimately money and debt are both claims on energy. Increasing energy density (energy per unit of volume) over time (e.g., from wood to coal to oil to nuclear), has led to accelerating economic growth that enabled the world to support both a growing population and rising living standards. “Energy Return on Energy Investment”, or EROEI measures the difference between the amount of energy needed to produce a unit of fuel (e.g., coal, oil, gas, renewables, etc.) and the amount of energy that unit contains. Economic growth is ultimately constrained either by a shift to less dense energy sources and/or a decline in EROEI. Today, the world is experiencing both, which is an underlying and usually unacknowledged fundamental cause of slowing global growth. First, to limit greenhouse gas emissions and slow climate change, policies are encouraging a shift away from fossil fuels and towards sources like solar and wind with much lower energy density. While solar energy’s density has been improving, it still orders of magnitude lower than fossil fuels and uranium (nuclear). Second, over time EROEI is a race between the increasing difficulty of producing the next unit of a diminishing resources and the rate at which improving technology reduces the cost of doing so. In this race, Mother Nature has been winning in recent years, as the discovery of new cheap “monster” oil and gas fields that are cheap to produce has given way to much more expensive methods (e.g., horizontal drilling and fracture stimulation). This is the underlying cause of the narrowing gap between the minimum price producers need to receive to profitably produce different types of energy, and the maximum price consumers can pay for it if they have high levels of debt and their incomes are stagnant or falling in a weak economy. Today, there are two obvious solutions: Expanded use of nuclear and substantial improvements in solar energy’s density. The first faces political obstacles, and the latter technical ones. Until either or both are resolved, worsening energy economics will very likely impose a growing drag on economic growth. |
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| New Economic Information: Indicators and Surprises | Why Is This Information Valuable? |
| The IMF released its semi-annual World Economic Outlook (along with its Fiscal Monitor and Global Financial Stability Report) | The WEO painted a sobering picture. “The medium-term projections also assume that economies will experience scarring from the depth of the recession and the need for structural change, entailing persistent effects on potential output. “These effects include adjustment costs and productivity impacts for surviving firms as they upgrade workplace safety, the amplification of the shock via firm bankruptcies, costly resource reallocation across sectors, and discouraged workers’ exit from the workforce. “The scarring is expected to compound forces that dragged productivity growth lower across many economies in the years leading up to the pandemic— relatively slow investment growth weighing on physical capital accumulation, more modest improvements in human capital, and slower efficiency gains in combining technology with factors of production.” |
| Far more revealing than the careful language in the official reports were interviews with top officials from the World Bank and IMF | In “Many Businesses Are In Sectors That Aren’t Going To Recover” by Hulsen and Bidder from Der Spiegel, Carmen Reinhart, the World Bank’s chief economist, discussed why she considers rapid economic recovery to be an illusion. “The longer the lockdowns, the uncertainties, the more damage done to the balance sheets of governments, households and firms. That is where this crisis begins to compare to historical ones. “The issue of financial fragility and bankruptcies becomes much more compelling. People who lose their jobs and do not quickly regain employment will have difficulty servicing their debts. Many businesses are in sectors like entertainment, restaurants and retail that are not going to recover”… “Right now, we’re in a state of suspension. So many countries have either directly by government decree or through the initiative of the banks given grace periods to firms and households. But that will eventually be over, and that is a source of concern when I look at the next year… “I'm talking about a period of high non-performing loans that require more recapitalization from governments that also make institutions very leery about new lending, so that we'll have a credit crunch. That environment can occur without drama and can last a long time... “Historically, in the last 160 years, the average time it took to really recover - meaning you get back to the pre-crisis level of income per capita – is eight years… “The key lesson in terms of policy for countries is: The earlier you tackle the restructuring of private debt, the earlier you start the recognition of non-performing loans, the need for write-offs, the quicker you can clean the balance sheet for banks, and the quicker the banks can start moving toward new lending.” “Global Liquidity Trap Requires A Big Fiscal Response”, by Gita Gopinath, Chief Economist of the IMF, in the Financial Times “Now we are in a global liquidity trap where monetary policy has limited effect… Solvency risks now predominate... The ascent back from what I have called “the great lockdown” will be long and fiscal policy will need to be the main game in town… “Before the pandemic, there was a worrying consensus that low-for-long interest rates had promoted excessive risk-taking that heightened financial stability risks. The striking disconnect of financial markets from real activity in the recovery from the Covid-19 crisis reinforces these notions… “The importance of fiscal stimulus has probably never been greater because the spending multiplier — the pay-off in economic growth from an increase in public investment — is much larger in a prolonged liquidity trap.” |
| “The Fiscal Multiplier of Public Investment: The Role of Corporate Balance Sheet”, by Espinoza et al from the IMF | SURPRISE While Gopinath is correct about the need for large, coordinated fiscal stimulus by governments, this new paper highlights that the multiplier impact of this spending will be constrained by high private sector debt levels. “This paper explores whether public investment crowds out or crowds in private investment. To this aim, we build a database of about half a million firms from 49 countries. We find that the effect of public investment on corporate investment depends both on leverage and financial constraints. Public investment boosts private investment for firms with low leverage. However, for firms with high leverage, private investment does not react to an increase in public investment… the effect of public investment on corporate investment is much weaker for firms that are financially constrained.” |
| “Intangible Investment and Low Inflation: A Framework and Some Evidence”, by Lall and Zeng from the IMF | SURPRISE The increasing importance of investment in intangible as opposed to fiscal assets in the digitalizing (or “dematerializing”) economy may also constrain the price (inflation) impact of more fiscal and monetary stimulus. The IMF finds that, “the rise of intangible investment across advanced—and increasingly emerging—economies can plausibly explain many macroeconomic relationships observed over the past decade... “The underlying structural changes that intangible investments embody, while in train since at least the 1990s, are still at an early stage of transforming economies… Its distinguishing characteristics are generally greater scalability and lower marginal costs than tangible investment…This may have contributed to more elastic aggregate supply in recent years, which is consistent with lower inflation and a flattening of the Phillips curve. This framework highlights the channels through which technological change, a large constituent of intangible investment, may be leading to wage stagnation and greater market concentration. |
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| New National Security Information: Indicators and Surprises | Why Is This Information Valuable? |
| In “What will Xi’s China Do Next?” the Financial Times’ James Kynge reviews "The Emperor’s New Road "by Jonathan E Hillman — a book about how China is projecting its power across the world. | SURPRISE China’s Belt and Road Initiative is often portrayed as a foreign policy success story. This is not the whole story. “The title refers to China’s Belt and Road Initiative (BRI), a programme launched in 2013 to build roads, railways, bridges, ports, networks of power cables and other forms of infrastructure costing in excess of $1tn in more than 100 countries. The aim of this grand endeavour is to boost China’s international influence and win overseas markets for Chinese companies… “It becomes clear that the wheels are falling off the BRI. Corruption is rife. Fiascos are piling up. A China so vaunted for planning its own extraordinary development is revealed as largely unable to pull off the same feat abroad… “Overall, the book points up a central, unresolved paradox of China in the world. While Chinese companies are now at the forefront of global technology and its construction giants lead the world, its governance models have progressed little since the Ming dynasty. BRI projects are conceived in secrecy, bankrolled mostly by big state banks and subjected to little or no social, environmental or financial scrutiny by the people of recipient countries… “Hillman’s book highlights a glaring reality: China has yet to find a way to project its influence beyond its borders in a way that enhances its national prestige. For all its grand ambition, the BRI so far has succeeded in demonstrating to the world that its governance model does not travel.” |
| The CCP giveth, and the CCP taketh away… In “Beijing And Wall Street Deepen Ties Despite Geopolitical Rivalry”, the Financial Times describes how China is increasingly allowing large US financial services firms to enter its financial markets. For the players involved, if perhaps not for US foreign policy, it seems a good deal. More profits from China for the companies, and more powerful advocates for the CCP in the halls of power in the US. However, some of those same firms lost out on enormous fees last month when at the last minute Chinese regulators forced Ant Financial to pull what would have been the largest initial public offering in history, with an estimated value of $37 billion. The official reason given was regulators growing concern with the threat to financial stability posed by Ant’s growing book of unsecured loans to small Chinese borrowers (banks require more collateral). The unofficial reason was Xi Jinping’s anger at an October 24th speech by Jack Ma, Ant’s (and Alibaba’s) founder, in which he criticized China’s financial system. | SURPRISE The Ma episode has broader, and potentially much more important implications. As Bloomberg New Economy observed, “The treatment [Ma] received illustrates not just the perils of appearing to cross Beijing, speaking one’s mind and challenging orthodoxy. It raises questions about the future of innovation at a critical moment, one where China is counting on internal dynamism rather than external demand to drive its economy. More broadly, it touches upon China’s place in the world. Can it ever compete with the West in the realm of “soft power,” which is so necessary to global influence?” Speaking on a Hoover Institution webinar, Elizabeth Economy noted how the actions taken against Ma could increase Xi Jinping’s vulnerability, by increasing frustration with his leadership among China’s entrepreneurial middle class. RAND’s Michael Mazaar makes a similar point in a new paper, “The Essence of Strategic Competition with China”. “There remains a question of precisely what sort of challenge China poses—and, by extension, the true essence of the emerging competition. This article argues for one answer to that question: At its core, the United States and China are competing to shape the foundational global system—the essential ideas, habits, and expectations that govern international politics. It is ultimately a competition of norms, narratives, and legitimacy; a contest to have predominant influence over the reigning global paradigm.” Mazaar “contends that, despite its massive investments in propaganda tools and economic statecraft, China remains starkly ill-equipped to win such a competition—but the United States could, through self-imposed mistakes, lose it.” |
| “China’s Growth Outlook: Is High-Income Status in Reach?” by Matthew Higgins of the Federal Reserve Bank of New York | This new analysis highlights the fundamental challenge facing the Chinese Communist Party, and the logic that underlies Xi Jinping’s relentless emphasis on increasing the rate of innovation in China. “Decades of rapid economic growth have propelled China out of poverty and into middle-income status. Now the country faces a new challenge: escaping the so-called “middle-income trap.” “The results [of this analysis] are stark: Given an aging population and diminishing returns to capital, China can only achieve high-income status in the coming decades by sustaining productivity growth at the top end of the range attained by its Pacific Rim neighbors. “Productivity gains on that scale would likely require extensive institutional development, including a marked reduction in state direction of the economy.” |
| “Chinese Companies Waiting Twice As Long For Payments As In 2015”, by Sun Yu in the Financial Times | SURPRISE The Chinese economy may be weaker than official data portray. “Official data show it took an average of 54 days for Chinese private manufacturers to get paid in the first three quarters of this year. That is up from 45 days in 2019 and 27 days five years ago. “The delay in debt collection has taken a toll on China’s post-virus economic recovery as private companies, an important employer, trimmed their growth plans for fear of late payments… The growing difficulty in collecting funds has prompted many businesses to cut back despite an influx of orders.” |
| Two new articles highlighted India’s growing role in the evolving alliance to contain China. | “New Delhi quietly dispatched a frontline warship on an unusual voyage to the South China Sea…New Delhi is shedding its reticence to unleash India’s maritime power and strengthen it security partnerships as it seeks to counter what is considers Chinese aggressions on its land border (“India Takes Its Tussle With China to the High Seas”, Financial Times). “US And India Sign Defence Agreement To Counter China”, Financial Times. The two countries agreed to “share sensitive military intelligence that will eventually enable the US and Indian militaries to co-operate more closely on the ground.” “The deal will include sharing geospatial data, giving New Delhi access to US maps and satellite data, as well as topographical, nautical and aeronautical information. This will improve the accuracy of Indian weapons, such as missiles and drones.” |
| The election of Joe Biden raised questions about the future direction of American foreign policy. | Speaking at a Financial Times webinar, Ann Marie Slaughter, former Director of Policy Planning at the US Department of State under Barak Obama said she believes that Biden’s foreign policy will be “driven by three Ds” – (1) Domestic renewal, including the tighter integration of industrial and foreign policy; (2) Democracy, including the formation of a “League of Democracies” to confront China”, and (3) Deterrence, focused on China, which will also include military modernization. During the campaign, other articles described the key plans of a more progressive foreign policy, including: (A) Closer integration between domestic economic and foreign policy; (B) Opposition to authoritarian capitalism regimes; support for traditional US alliances; (C) Identification of climate change as the nation’s top national security priority; and (D) a high degree of skepticism about the use of military force, which logically leads to a reduction in defense spending to fund domestic priorities. See also, “The Emergence of Progressive Foreign Policy”, by Ganesh Sitaraman, “The Real Progressive-Centrist Divide on Foreign Policy” by Thomas Wright, and “Why America First is Here to Stay” by Michael Lind. |
| “Why American Strategy Fails”, by Winnefeld, Morell, and Allison | SURPRISE The authors publicly confront a central issue that policymakers have for years discussed in private: the “growing imbalance among four classic variables of grand strategy: ends, ways, means, and the security landscape. Left unrecognized and unaddressed, gaps between U.S. ambitions and the U.S. ability to fulfill them will generate increasingly unacceptable strategic risks.” They claim that, “the current loss of equilibrium that characterizes US foreign policy is driven by two of the four variables. First, changes over the last two decades in the global landscape, including major shifts between the relative power of the United States and its major competitors, present an immense challenge. “Second, American voters are signaling their desire for more attention and resources to be used for domestic issues… “Correcting this imbalance is easy to say, but hard to do. The policy community resists setting priorities, mostly reacts to ongoing events, and uses the term ‘vital’ promiscuously… The solution the authors propose is a “new kind of national security strategy” that is “short and succinct, not all things to all people. It needs to lay out an understanding of ends through the lens of a list of generic, prioritized security interests, as well as guidance on using the list to both resource and employ national power.” The authors propose that the interests of the United States fall into a hierarchy of five tiers. At the top is “survival of the country as a free democracy.” This is followed by: “Prevention of catastrophic attacks on the country and its citizens.” “Protection of the global ‘operating system’ [international order] and a US leadership role within it.” “The security and support of US allies and partners.” “The protection and, where possible, the extension of universal values.” Critically (as anyone who has attempted to turn around large organizations can attest), the authors caution that “changing ways may be even more difficult than adjusting ends…in large systems, internal and external investments in the status quo make it hard to break out existing practices.” The alternative, however, is “to risk advancing unsustainable, or outdated, tactics in service of muddled priorities – with the result that the United States will struggle more and more.” |
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| New Health and Disease Information: Indicators and Surprises | Why Is This Information Valuable? |
| A new COVID-19 vaccine was announced, with a claim that it is 95% efficient at protecting against infection, after two doses, three months apart. However, it must be kept at extremely low temperature from the time it is manufactured until the time shots are administered. A remaining and as yet unknown uncertainty is how long the protection against infection conferred by the vaccine will last. | SUPRRISE As analyzed in a previous feature article, many challenges stand between this announcement and any return to “normal” social and economic conditions. The announcement also poses a risk that has largely gone unreported. Consider what happened in the case of the large Black Lives Matter demonstrations following the death of George Floyd in Minneapolis on May 25, 2020. Up to that point, controversy over wearing facemasks to prevent COVID inflection has mostly been confined to debate about its effectiveness. Resistance on ideological/political grounds existed, but was still confined to the fringe. The appearance of large demonstrations of people not wearing masks, and, much worse, the refusal of many epidemiologists and others to condemn this behavior (because the cause was just) rather quickly led to the mass politicization of mask wearing, and buried stories about its effectiveness in preventing infection. History will tell the impact this had on the intensity of subsequent waves of infections. In most complex adaptive systems, the effects of a perturbation are often both delayed and non-linear. The interaction of the BLM demonstrations, mask wearing, and infection rates are likely to be similar. Which brings us to the new vaccine. A key risk is that it will provide people with a false sense of security, and in so doing cause many to relax effective measures like mask wearing and social distancing, which in turn will increase infections and the scale of the challenge vaccination must meet. |
| “Projected COVID-19 Epidemic In The United States In The Context Of The Effectiveness Of A Potential Vaccine And Implications For Social Distancing And Face Mask Use”, by Mingwang Shen et al “The degree to which the US population can relax social distancing restrictions and face mask use will depend greatly on the effectiveness and coverage of a potential COVID-19 vaccine if future epidemics are to be prevented” … “Without a vaccine, the spread of COVID-19 could be suppressed in these states by maintaining strict social distancing measures and face mask use levels. But relaxing social distancing restrictions to the pre-pandemic level without changing the current face mask use would lead to a new COVID-19 outbreak, resulting in 0.8-4 million infections and 15,000- 240,000 deaths across New York, Texas, Florida, and California states over the next 12 months. “In this scenario, introducing a vaccine would partially offset this negative impact even if the vaccine effectiveness and coverage are relatively low.” However, there is likely to be a dynamic relationship between vaccine strength and face mask use that will have a significant impact on pandemic control. “If face mask use is reduced by 50%, a vaccine that is only 50% effective (weak vaccine) would require vaccination of 55-94% of the population to suppress the epidemic in these states. “A vaccine that is 80% effective (moderate vaccine) would only require 32-57% coverage to suppress the epidemic. “In contrast, if face mask usage stops completely, a weak vaccine would not suppress the epidemic, and further major outbreaks would occur. A moderate vaccine with coverage of 48-78% or a strong vaccine (100% effective) with coverage of 33-58% would be required to suppress the epidemic.” | SURPRISE Based on this analysis, and given the reported strength (95% effective) of the new vaccine, if its introduction leads to a 50% drop in face mask use, somewhere between 33% and 60% of the population would have to be vaccinated to bring the pandemic under control. |
| “Covid-19 Herd Immunity Theory Dealt Blow By UK Research”, Financial Times “The proportion of people in Britain with antibodies that protect against Covid-19 declined over the summer, according to research that adds to evidence that natural immunity can wane in a matter of months. “The number of people with antibodies fell by a quarter, from 6 per cent of the population in June to 4.4 per cent in September, according to a study of hundreds of thousands of people, one of the largest of its kind to date.” | SURPRISE This is an important finding that partially reduces a critical uncertainty: How long does naturally acquired immunity to COVID last? It is only partial because it does not address the length of time that immunity based on T-Cells lasts. It also does not address how long immunity acquired through vaccination will last. However, it does raise the likelihood that annual vaccinations against the SARS-CoV-2 virus may be required, as they are for influenza. |
| “Robust SARS-Cov-2-Specific T-Cell Immunity Is Maintained At 6 Months Following Primary Infection”, by Zuo et al “The immune response to SARS-CoV-2 is critical in both controlling primary infection and preventing re-infection. However, there is concern that immune responses following natural infection may not be sustained and that this may predispose to recurrent infection. “We analysed the magnitude and phenotype of the SARS-CoV-2 cellular immune response in 100 donors at six months following primary infection and related this to the profile of antibody level against spike, nucleoprotein and RBD over the previous six months… “Median T-cell responses were 50% higher in donors who had experienced an initial symptomatic infection indicating that the severity of primary infection establishes a ‘setpoint’ for cellular immunity that lasts for at least 6 months. “The T-cell responses to both spike and nucleoprotein / membrane proteins were strongly correlated with the peak antibody level against each protein… “In conclusion, our data are reassuring that functional SARS-CoV-2-specific T-cell responses are retained at six months following infection although the magnitude of this response is related to the clinical features of primary infection.” | SURPRISE Based on the responses of patients who have recovered from a COVID infection, this analysis finds that the T-Cell response is correlated with the antibody response and proportional to the severity of the initial infection. The study also found that immunity lasts at least six months. It is not yet clear if these findings will also apply to people who acquire immunity via vaccination rather than infection. |
| “Selective And Cross-Reactive SARS-Cov-2 T Cell Epitopes In Unexposed Humans”, by Jose Mateus et al “Many unknowns exist about human immune responses to the SARS-CoV-2 virus. SARS-CoV-2–reactive CD4+ T cells have been reported in unexposed individuals, suggesting preexisting cross-reactive T cell memory in 20 to 50% of people. “However, the source of those T cells has been speculative. Using human blood samples derived before the SARS-CoV-2 virus was discovered in 2019, we mapped 142 T cell epitopes across the SARS-CoV-2 genome to facilitate precise interrogation of the SARS-CoV-2–specific CD4+ T cell repertoire. “We demonstrate a range of preexisting memory CD4+ T cells that are cross-reactive with comparable affinity to SARS-CoV-2 and the common cold coronaviruses… “We find that variegated T cell memory to coronaviruses that cause the common cold may underlie at least some of the observed reactivity to SARS-CoV-2.” | SURPRISE Exposure to the coronaviruses that cause common colds may be responsible for the T-Cell reactivity to the SARS-CoV-2 virus that has been observed in people who have not had COVID-19. This raises intriguing questions about whether this T-Cell mediated resistance is higher in people who get more common colds, like students and teachers. See also, “Preexisting and De Novo Humoral Immunity to SARS-CoV-2 in Humans”, by Ng et al |
| “Emergence And Spread Of A SARS-Cov-2 Variant Through Europe In The Summer Of 2020”, by Emma Hodcroft et al “A variant of SARS-CoV-2 emerged in early summer 2020, presumably in Spain, and has since spread to multiple European countries. The variant was first observed in Spain in June and has been at frequencies above 40% since July. Outside of Spain, the frequency of this variant has increased from very low values prior to 15th July to 40-70% in Switzerland, Ireland, and the United Kingdom in September… “Sequences in this cluster (20A.EU1) differ from ancestral sequences at 6 or more positions, including the mutation A222V in the spike protein and A220V in the nucleoprotein… It is currently unclear whether this variant is spreading because of a transmission advantage of the virus or whether high incidence in Spain followed by dissemination through tourists is sufficient to explain the rapid rise in multiple countries.” | SURPRISE A key uncertainty regarding SARS-CoV-2 is the rate at which it changes, via mutation and recombination. A related uncertainty is whether these changes will follow a common path for other viruses, increasing their transmissibility while reducing the severity of the illness they cause. In so far as this is the case, then it increases the probability that control of SARS-CoV-2 will require ongoing vaccine research and development as well as annual vaccination, as is the case with influenza. See also, “Spike mutation D614G alters SARS-CoV-2 Fitness”, by Jessica Plante |
| “Improving Survival of Critical Care Patients With Coronavirus Disease 2019 in England: A National Cohort Study, March to June 2020”, by Dennis et al | “There has been a substantial improvement in survival amongst people admitted to critical care with coronavirus disease 2019 in England, with markedly higher survival rates in people admitted in May and June compared with those admitted in March and April.” |
| “SARS-Cov-2 Seroprevalence And Transmission Risk Factors Among High-Risk Close Contacts: A Retrospective Cohort Study”, by Ng et al “Between Jan 23 and April 3, 2020, 7770 close contacts (1863 household contacts, 2319 work contacts, and 3588 social contacts) linked to 1114 PCR-confirmed index cases were identified”... “Among 7518 (96·8%) of the 7770 close contacts with complete data, the secondary clinical attack rate was 5·9% (95% CI 4·9–7·1) for 1779 household contacts, 1·3% (0·9–1·9) for 2231 work contacts, and 1·3% (1·0–1·7) for 3508 social contacts”… “Sharing a bedroom and being spoken to by an index case for 30 min or longer were associated with SARS-CoV-2 transmission among household contacts. “Among non-household contacts, exposure to more than one case, being spoken to by an index case for 30 minutes or longer, or sharing a vehicle with an index case were associated with SARS-CoV-2 transmission. “Among both household and non-household contacts, indirect contact, meal sharing, and lavatory co-usage were not independently associated with SARS-CoV-2 transmission.” | SURPRISE This study provides very useful data about the rates of COVID transmission among close contacts. With the holiday season approaching, this study, along with new findings about the importance of indoor air quality, can be very helpful in reducing anxieties. |
| “Association Between Living With Children And Outcomes From COVID-19: An Opensafely Cohort Study Of 12 Million Adults In England”, by Harriet Forbes et al “Among adults ≤65 years, living with children 0-11 years was not associated with increased risks of recorded SARS-CoV-2 infection, COVID-19 related hospital or ICU admission, but was associated with reduced risk of COVID-19 death (HR 0.75, 95%CI 0.62-0.92). “Living with children aged 12-18 years was associated with a small increased risk of recorded SARS-CoV-2 infection (HR 1.08, 95%CI 1.03-1.13), but not associated with other COVID-19 outcomes… “We observed no consistent changes in risk following school closure.” | SURPRISE This very large study provides further support for keeping schools open to minimize students learning losses. |
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| New Social Information: Indicators and Surprises | Why Is This Information Valuable? |
| “Secularization and the Tribulations of the American Working-Class”, by Brian Wheaton | SURPRISE Wheaton notes that, “Over the past several decades, working-class America has been plagued by multiple adverse trends: a sharp increase in social isolation, an even sharper increase in single parenthood, a decline in male labor force participation rates, and a decline intergenerational economic mobility – amongst other things”, and that “Material economic factors have been unable to fully explain these phenomena.” He focuses on the potential impact of declining religiosity as an explanation, and finds that it has a strong effect, the bulk of which is driven by declining religious attendance rather than weakening beliefs. |
| In the middle of the COVID pandemic, the financial burdens imposed on individuals by the US healthcare system still weigh heavily on their decisions. | A new survey by HealthCare Insider and YouGov found that “56% of U.S. adults said they were either somewhat or very concerned that a health situation in their household could lead to bankruptcy or debt”, and 46% US adults said they postponed healthcare services in the past year.” |
| The primary, secondary, and tertiary (university) education sectors continue to be strongly affected by COVID-19. | Arguments between school leaders, teachers, parents, and employers over primary and secondary school openings are increasingly acrimonious, particularly as evidence accumulates that: (1) inflection rates in schools are lower than in their surrounding communities, particularly among children (e.g., “What’s the Evidence for COVID-19 Transmission by Children in Schools?”; (2) There are effective means to further limit that risk through better management of indoor air quality (e.g., “Are We Ready to Close School Windows?”); (3) Learning losses due to closed schools and ineffective remote instruction are unlikely to be recovered (e.g., “COVID Learning Losses: Dark Days Lie Ahead”); and increasing criticism of the quality of the decision making processes for school reopening that are being used by government authorities (e.g., “When Should Schools Reopen?” by the Wharton School). Increasing acrimony is very likely creating a window for substantial changes in these systems. The same is true in the tertiary system, where COVID-19 has placed enormous strain on university’s economic models (e.g., due to the loss of foreign students). See, for example, “The Failing Business Model of American Universities”, by Eric Jansen). |
| “Rethinking AI Talent Strategy as Automated Machine Learning Comes of Age”, by Hurtgen et al from McKinsey “In recent years, as the promise of artificial intelligence (AI) crystallized across industries, organizations revamped their talent strategies to gain the skills necessary to deploy and scale AI systems. They hired legions of data scientists and other data experts to build AI applications, trained analytics translators to connect the business and technical realms, and upskilled frontline staff to use AI applications effectively… “One role in particular, the data scientist, has been especially difficult for leaders to fill as competition for its illusive knowledge increased… “But there are also new tools that have the potential to fill the data-science talent gap and increase the efficiency of analytics teams. Automated machine learning (ML) tools, commonly called AutoML, are designed to automate many steps in developing machine learning models. Business experts armed with AutoML can build some types of models that once would have needed a trained data scientist.” | This article is a specific example of a larger problem: AI and automation technologies are improving at a faster rate than labor skills are improving (a situation that unrecovered COVID learning losses will only worsen). This is why the World Economic Forum’s recent Future of Jobs report put so much emphasis on the need for better approaches to reskilling employees. Unfortunately, that is no easy task, as the education sector in many countries (with notable exceptions like Switzerland) is riven by turf battles between well-entrenched interest groups across the secondary and tertiary systems. This leads to calls for greater reskilling investments by employers. However, many companies face thin margins in an intensely competitive global economy. Along with the fear of losing their investment in reskilled employees if they leave, this is incentivizing a growing number of companies to minimize reskilling in favor of greater investment in automation and AI technologies that will not walk out the door. To be sure, there is an argument that, as was the case in the Industrial Revolution, job destruction will eventually be offset by the creation of new jobs, many of which don’t exist today. However, especially if real wages remain stagnant, this argument says nothing about how to successfully deal with the medium term social and political problems that will arise from rising unemployment as more workers find themselves displaced by technology and lacking the skills to find a new job with comparable pay to the one they lost. |
| Evidence continues to accumulate that COVID-19 is producing social changes that will very likely be difficult, and/or take a long time to fully reverse after the pandemic ends. | For example: COVID-19 is putting further downward pressure on birth rates, which, all else being equal, further dampen aggregate demand growth and intensify intergenerational conflicts (“The Pandemic May Be Leading To Fewer Babies In Rich Countries” in The Economist). School closures and the shift to remote learning is pushing more women out of the workforce, increasing pressure on family finances and consumption spending (“Sitting It Out? Or Pushed Out? Women Are Leaving the Labor Force in Record Numbers” by Kathryn Edwards from RAND). Despite higher rates of working from home, “individuals with higher education experienced a greater increase in depressive symptoms and a greater decrease in life satisfaction from before to duringCOVID-19 in comparison to those with lower education” (“Socioeconomic Status And Well-Being During COVID-19”, by Wannberg et al). |
| “America is Having a Moral Convulsion”, by David Brooks in The Atlantic “Social trust is a measure of the moral quality of a society—of whether the people and institutions in it are trustworthy, whether they keep their promises and work for the common good… “Levels of trust in this country—in our institutions, in our politics, and in one another—are in precipitous decline. And when social trust collapses, nations fail. Can we get it back before it’s too late?” | SURPRISE This is an extremely thought provoking and definitely not optimistic essay that is well worth a read. Among many other interesting observations, Brooks notes that, “The Baby Boomers grew up in the 1950s and ’60s, an era of family stability, widespread prosperity, and cultural cohesion. The mindset they embraced in the late 1960s and have embodied ever since was all about rebelling against authority, unshackling from institutions, and celebrating freedom, individualism, and liberation.” In stark contrast, “the emerging generations today enjoy none of that sense of security. They grew up in a world in which institutions failed, financial systems collapsed, and families were fragile. Children can now expect to have a lower quality of life than their parents, the pandemic rages, climate change looms, and social media is vicious. Their worldview is predicated on threat, not safety. Thus the values of the Millennial and Gen Z generations that will dominate in the years ahead are the opposite of Boomer values: not liberation, but security; not freedom, but equality; not individualism, but the safety of the collective; not sink-or-swim meritocracy, but promotion on the basis of social justice.” “Once a generation forms its general viewpoint during its young adulthood, it generally tends to carry that mentality with it to the grave 60 years later. A new culture is dawning. The Age of Precarity is here.” Brooks concludes that, after the COVID pandemic arrived, “we had a chance, in crisis, to pull together as a nation and build trust. We did not. That has left us a broken, alienated society caught in a distrust doom loop… the events of these past six years, and especially of 2020, have made clear that we live in a broken nation. The cancer of distrust has spread to every vital organ.” |
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| New Political Information: Indicators and Surprises | Why Is This Information Valuable? |
| Joe Biden defeated Donald Trump in the US presidential election. And Trump is challenging Biden’s win. No surprises there. But what was a surprise was the narrowness of Biden’s win. | SURPRISE While much will be written about this election, at this point I have two observations about what could have produced the surprisingly strong vote for Trump. The first is the often under-appreciated swing segment of the US electorate. The 2x2 matrix first proposed by Maddox and Lillie in their 1984 paper “Beyond Liberal and Conservative” is still quite useful. It divides voters along two dimensions, based on their attitude towards social and economic issues. “Libertarians” are favor low government involvement in both areas. “Liberals” favor more government in the economic realm, and less in the social realm. “Conservatives favor the opposite. And the fourth group favors more government in both areas. This group, sometimes called “communitarians”, sometimes “populists” and sometimes “traditional Democrats”, are the orphans of American politics, with no party clearly representing their interests, and both fighting to win their votes. In this election, a surprising number of this segment appeared to be more turned off by the social views of Progressives (e.g., see, “The Platform the Democrats are Too Scared to Publish”, by Aaron Sibarium) than they were turned off by Trump’s handling of the pandemic, his personal foibles, or his relative indifference to their economic needs. As Peter Beinart noted in “Why Trump Lost”, “If he’d governed as he ran in 2016, as an economic populist, Trump would likely have been reelected. Instead, he reverted to the same old Republican playbook”. The second observation was that a significant segment of the Trump vote was likely driven by deeper factors that Martin Gurri has explored in his analyses. In “Character as Politics”, he claims that, “Elite behavior, in brief, is perceived by the public as that of an entitled if not decadent class. Elite rhetoric appears both disdainful and false…The political consequences extend beyond gossip or scandal touching a few individuals to questions about the legitimacy of the democratic system…Protest movements around the world are often driven by contempt for political and business elites… “Almost by definition, and certainly by intent, populists [like Trump] don’t act or sound like the elites who fill the ranks of professional politicians. “Questions of character, in this context, turn on the pervasive mood of repudiation. When populists are accused of being offensive or unethical, we must keep in mind that their accusers—establishment media and political actors—have already been judged and found guilty by the public. “For this to change, the elites have to change. Somehow, those at the top have to regain their good character in the eyes of the public. They must discover ways to win respect in the chaotic theater of the web, and they must develop a rhetorical style adapted to the digital age…So far, they appear utterly unwilling to try.” Whether Joe Biden and the Democratic Party, perhaps with the help of a few centrist Republicans will be up to this challenge remains to be seen. |
| Control of the US Senate is still up for grabs, and depends on the results of two Georgia runoff elections on January 5th. The current balance is 50-48. If Democrats win both Georgia seats, that split changes to 50/50, with Vice President Kamala Harris the tiebreaker, this would give Democrats control. | Republicans retaining control of the Senate will theoretically limit the influence on policy of the progressive wing of the Democratic Party. However, as both Presidents Obama and Trump have shown, the lack of a legislative majority can, to a surprising extent, be overcome through the aggressive use of Executive Orders. As a practical matter, this means that the US is evolving towards something approaching a parliamentary system, in which the party winning the White House can use EO’s to implement its platform, which also makes it easier for the next president to repudiate them and implement their own. Unfortunately, polarized congressional gridlock that leads to rule by Executive Order also weakens the legislative branch of government relative to the judicial and executive branches, and in so doing accelerates the nation’s growing crisis of political legitimacy. |
| After what the Financial Times’ Ed Luce called “A Bitter Election that Resolves Little”, expect to see more comparisons between today’s US and Weimar Germany. In “The Weimarization of the American Republic”, Aaron Sibarium gets this theme off to an impressive start, concluding that, “America is not Weimar Germany. But there are troubling historical echoes in our politics today.” | “Though Trump’s America looks nothing like Nazi Germany, it has developed echoes of the Republic from which Nazism arose—echoes that implicate the left no less than the right… “Weimar had no shortage of radicals; centrists, on the other hand, were a dying breed… “With few prospects in the existing system, the intellectual class saw little reason to defend it, and had an easy time rationalizing its destruction…Beyond their shared anti-Republicanism, all that the two sides had in common was their contempt for one another… “What united the right was not a particular political program, but a general sense of grievance against the far left and republican center, distinct groups it would often synonymize. Marxism, modernism, liberalism—these were all shades of the same thing, corruptions of the old order. “An equal but opposite elision occurred on the left, with the communists calling everyone to their right—including the Social Democrats—fascists, an unfair charge that many leftwing intellectuals nonetheless echoed.” The result was threefold: “First, it exacerbated Weimar’s crisis of legitimacy…Second, each side came to resemble, and thus to justify, the other’s caricatures… Third, by coding everyone as either a Nazi or a commie, the political culture laid the groundwork for political violence…and because violence had become idologized, there was no non-polarizing way to address it…It became inevitable that one pole would win.” |
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| New Financial Markets and Investor Behavior: Indicators and Surprises | Why Is This Information Valuable? |
| Another month, another burst of articles about how active managers have yet again underperformed index funds. I’ve had growing doubts about these stories, which will obviously strike some as rather strange, coming from “The Index Investor.” | Let me channel the ghost of Jack Bogle for a minute… Back in the dark ages (1997) when we were launching the Index Investor and iShares and SPDRs were broadening their product lines, we had a few conversations with him in which he bemoaned the rise of ETFs, and warned that they would lead to excess trading (compared to index mutual funds) and an explosion of ever narrower indexes. In effect, they would become just a cheaper form of automated active management, whose virtuous index investor label would fool the punters and attract their cash. He worried this would ultimately sully the concept of passive investment in broadly defined index funds. As was often the case, Jack was prescient; this is exactly what has happened. Which raises a critical question: Have we reached the point where we should be comparing the performance of "old fashioned" active managers not just with each other, but also with that of ETFs that track narrowly defined indexes (e.g., "the dog food 20")? Of course, that raises the question of where to draw the line between truly passive and automated active funds. Here’s our take: At one extreme, investment products that track broad asset class indexes (e.g., the total stock market) should be considered passive. "Semi-Active" or "smart beta" products track recognized factors and contain a wide range of underlying assets. Beyond here lie various approaches to de-facto active management based on narrowly defined indexes. What I would love to see one day is a comparison of “active managers” that includes the universe of narrowly defined index products. To be sure, because of the cost difference, traditional active managers may still come up short versus “indexed active” products. But at least it would be a fair fight. |
| “Measuring Voters’ Knowledge of Political News”, by Angelucci and Prat | We have frequently written about research on the way the interaction and evolution of narratives drive global macro, including economics, social change, politics and financial markets, including: “News And Narratives In Financial Systems: Exploiting Big Data For Systemic Risk Assessment”, by Nyman et al; “The Politics of Crisis: Deconstructing the Dominant Narratives of the Housing Crisis”, by Heslop and Ormerod; “Monetary Policy and the Management of Uncertainty: A Narrative Approach”, by Tuckett et al; and Robert Shiller’s work on narrative economics. One of the uncertainties in this area is how long narratives, and the information on which they are based, actually persist in investors’ minds. This paper makes an important contribution in this area. The authors report that the 33% most informed people “are 97% more likely than people in the bottom 33% to know the main story of the month.” The authors also find significant confirmation bias, with voters 10% to 30% less likely to know stories unfavorable to their political party and current views. Finally, with respect to the rate at which narratives decay/weaken, “each month passing lowers the probability of knowing a story by 3-4 percentage points.” See also, “Volatility Expectations and Returns” by Lochstoer and Muir, who find short-term underreaction and long-term overreaction to volatility shocks. |
| “Interdependent Diffusion: The Social Contagion Of Interacting Beliefs”, by Houghton and Shou | SURPRISE “With good reason, the overwhelming majority of social contagion research over the last 50 years has assumed that diffusants spread independently of one another. Independence is an extremely useful and generative simplification. By assuming that diffusants do not interact, we can study the effects of social network structure, homophily, social reinforcement, or demographics on each contagion process in isolation… ““Interdependent diffusion” describes any social contagion process in which individuals’ likelihood of adopting diffusant A is a function of their current state of adoption of B (C, D, …) and in which their likelihood of adopting B (C, D, …) is a function of their state of adoption of A. In the social contagion literature, only a few studies explicitly allow for this type of interaction between diffusants… “This paper asks how much does interdependence matter?” … It “uncovers two new social processes that are unique to interdependent diffusion and which cannot be reduced to the familiar influences of network structure, homophily, social reinforcement, or demographics. “First, when beliefs support one another’s adoption, they can “snowball” through a population to reach a broader audience than any could have reached on its own. “Secondly, when individuals have similar belief sets, they are more likely to respond in the same way to new beliefs to which they are exposed, and so become yet more similar. “Simulations in this paper predict that shared “worldviews” will emerge spontaneously from the process of interdependent diffusion. Specifically, subsets of a population will come to share a set of interconnected beliefs (and reject others that are equally available) without reference to any ground truth. Interdependent diffusion is also predicted to foment polarization by increasing similarity within ideological camps and difference between camps, and aligning the population along a “left-right” political axis.” Logically, the same process can also lead to the development of market narratives that are both very different and, in many cases, surprisingly durable, even in the presence of information that casts doubt on their accuracy. |
| “The Persistence of Miscalibration”, by Boutros et al | “Using 14,800 forecasts of one-year S&P 500 returns made by Chief Financial Officers over a 12- year period, we track the individual executives who provide multiple forecasts to study how their beliefs evolve dynamically. While CFOs’ return forecasts are systematically unbiased, their confidence intervals are far too narrow, implying significant miscalibration. “We find that when return realizations fall outside of ex-ante confidence intervals, CFOs’ subsequent confidence intervals widen considerably. These results are consistent with a model of Bayesian learning, which suggests that the evolution of beliefs should be impacted by return realizations. However, the magnitude of the updating is dampened by the strong conviction in beliefs inherent in the initial miscalibration and, as a result, miscalibration persists.” |
| “The Fallacy of ESG Investing” by Robert Armstrong in the Financial Times | As we have in past issues, Armstrong points out some of the contradictions of the increasingly popular ESG investing. Unfortunately, not enough investors will likely read his wide words before parting with their money. “A single phrase sums up the appeal of environmental, social and governance investing: Doing well by doing good”. ESG strategies, we are told, promote the greater good and provide superior long-term financial performance… “There are good reasons for investors to own portfolios that align with their values. This supposed win-win proposition is not one of them however. Not only is the evidence that ESG outperforms over long periods inconclusive; the win-win argument doesn’t even make sense… “It is true that at some point in the indefinite future, the social good and financial interests must converge. There are no investment returns at all on a planet left uninhabitable by climate change. But that is not the time horizon individual investors operate over (they might have just 20 years between acquiring significant assets to invest and retiring). And it is far beyond any corporation’s planning horizon… “There are two ways investments outperform: either they generate greater than expected cash flows over time (growth), or they are bought at a cheap price (value). Putting aside the question of growth, to argue that (say) a carbon, tobacco, and gun heavy portfolio cannot outperform over the long term is to argue that it will never be bought cheap…But of course it is the goal of the ESG movement to push investors away from “wicked” portfolios — making their prices cheap, and setting them up to outperform “virtuous” portfolios over time! The win-win pitch is a fallacy... “Illogic is not the only problem with the win-win story. Another is performance attribution. “ESG funds have had a nice run lately. Since its inception in late 2018, for example,Vanguard’s US ESG exchange traded fund return of 28 per cent has whipped its broad market ETF’s 17 per cent. Look, however, at the holdings of the ESG fund. The top seven holdings, accounting for a quarter of the funds’ value, are Apple, Microsoft, Amazon, Facebook, Google and Tesla. Tech has led the market this year. But has ESG, really? And if tech stocks become overpriced and their prices crash, does that mean ESG is suddenly a bad strategy? … “None of this suggests that investors should not put their savings behind the things that they care about. It means only that they should not think of this as a wealth maximising strategy.” |
| “Some Perspectives on Gold in the New Paradigm”, by Jensen et al from Bridgewater | “Gold is one of the few effective diversifiers against the depreciation of paper currencies (and assets denominated in paper currency), as they all compete with gold as a storehold of wealth. And with interest rates at zero and the money supply increasing at warp speed, paper currencies are offering the worst deal ever, providing little incentive to hold them relative to gold. “So far, this printing hasn’t produced too much in the way of inflation that erodes the real value of the currency, and it has succeeded in supporting financial assets. But given how much ongoing printing and spending will be needed, and given that replacing lost incomes is inherently more inflationary than replacing credit (as it doesn’t replace the supply those incomes were paying for), we could very well see inflationary pressures mount while the economy remains weak. “A stagflationary outcome would put policy makers in a tough spot and leave paper currency assets vulnerable, while gold would likely be a valuable source of diversification. Stay too easy and risk further inflation (like after WWII, or most famously, the ’70s). Tighten too soon and risk plunging the world back into a deflationary downturn, like in 1937… “In the case of a deflationary downturn without enough stimulus, defaults and bankruptcies lead to terrible returns on equities and corporate credit. Gold fares relatively better as an asset that is no one else’s liability that could be defaulted on. And while nominal bonds maintain their value under such circumstances, today there is much less potential upside going forward than there was in past cases when nominal bonds had more room to rally. “In a successful reflation, financial assets do well as the central bank stays easy and supports a recovery, but gold is generally buoyed as well. “Stagflation eats away at real returns of paper currency assets, while gold tends to shine as a real storehold of value.” |
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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.
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
Appendix: Anticipatory Thinking and Forecasting Methodologies
Our process is based on methods and tools developed over the past seven years at our affiliate, Britten Coyne Partners, which provides consulting services and education courses to executive teams and boards on strategic risk governance and management.
At The Index Investor, we engage in both anticipatory thinking to identify what could happen (e.g., different macro regimes and related events), and forecasting, to estimate the probability that events and regimes will happen, and the impact they will have if they do (e.g., on macro variables and broad asset class returns).
With respect to what could happen, we are acutely conscious of the conclusion reached by a 1983 CIA study of failed forecasts: "each involved historical discontinuity, and, in the early stages…unlikely outcomes. The basic problem was…situations in which trend continuity and precedent were of marginal, if not counterproductive value."
When it comes to forecasting, we know that in complex socio-technical systems that are constantly evolving, the accuracy of statistical or machine learning based forecasting methods declines exponentially as the time horizon lengthens, since the historical data set on which they were trained will (depending on the speed and effectiveness of any retraining cycle) bear less and less resemblance to the distribution of outcomes the system is likely to produce in the future.
Under these circumstances, forecast accuracy over longer time horizons depends on causal and counterfactual reasoning about the possible future effects of multiple interacting trends and uncertainties that are hard to quantify.
And we are acutely aware of the economist Rudi Dornbusch's famous warning: "Crises take a much longer time coming than you think, then happen much faster than you would have thought."
Our forecasting process also draws on lessons Tom Coyne learned from spending four years as a member of the Good Judgment Project team, which won the Intelligence Advanced Research Projects Activity’s forecasting tournament with forecast accuracy that was more than 50% better than the tournament's control groups (the team's experience is described in Professor Philip Tetlock's book, “Superforecasting").
Our analysis focuses on the probability of the global macro system being in four possible macro regimes 12 and 36 months from the date of our forecast: (1) Normal Times, where equity asset classes perform well; (2) a High Uncertainty regime that is usually short and transitory, where asset classes like short-term government bonds perform best and equities suffer significant declines; (3) High Inflation (which we deem 5% or more, year-on-year), where commercial property, real return bonds and other traditional hedges are favored; and (4) Persistent Deflation (a year-on-year decline in the US CPI), which up to now has only been seen in Japan, and in which the relative performance of different asset classes remains uncertain, but will likely favor high quality bonds and the consumer staples equity sector.
In response to subscriber requests, we have added a 36-month regime forecast to our existing 12 month forecast. The logic is that, in a complex evolving system like global macro, a longer forecast horizon gets beyond the “detection range” of algorithmic forecasting approaches, and therefore raises probability that a manager/investor can gain an edge in identifying emerging threats and opportunities.
That said, because evolving (i.e., “non-stationary”) complex systems populated by highly connected human agents are also capable of sudden non-linear changes (with which are hard for algorithmic approaches to predict), we are also keeping our 12 month forecast.
Our forecasting methodology starts 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%.
Market Stress Indicators Methodology
We view financial markets as a complex adaptive system. The size of changes generated by such a system follows a power law rather than a normal (Gaussian) distribution. The critical point is that large changes are much more common in complex adaptive systems than most people’s intuition leads them to believe.
While predicting the behavior of complex adaptive systems remains far more art than a science, various researchers have found that large changes in such systems are often preceded by subtle warning signs, as stress accumulates within them. While this research is not definitive, we believe that five warning signs are worth monitoring as potential indicators of growing stress within financial markets that could suddenly give rise to large changes in asset class valuations.
Our first indicator is the month-to-month autocorrelation of broad asset class returns (i.e., the relationship of this month’s returns to last month’s). A system under increasing stress loses resiliency, causing it to take longer to recover from perturbations; hence, autocorrelation increases as it approaches a critical transition (see, “Early Warning Signals for Critical Transitions” by Scheffer, et al).
The second market stress indicator we monitor is the Economic Policy Uncertainty Index published by the Federal Reserve Bank of St. Louis (via its FRED economic database), which is based on research by Baker, Bloom, and Davis (see their paper, “Measuring Economic Policy Uncertainty”). The index is based on automated text analysis of leading newspapers and magazine publications, to identify the frequency with which words and phrases are used that indicate uncertainty.
In humans’ evolutionary past, when uncertainty increased the probability of survival was enhanced by staying close to a group. All of us still have that instinct. Research has found that as uncertainty increases, we have an unconscious bias towards higher conformity of our own views with those of a larger group (i.e., reduction in cognitive diversity). Behaviorally, heightened uncertainty induces more “social copying” of others, likely due to both conformity bias and the rational belief that others may be acting on the basis of superior information. This increase in conformity and copying makes a social system more ordered as uncertainty increases, and also reduces its responsiveness to perturbations (i.e., increases autocorrelation) because of delays in the social copying process.
The key point is that increasing uncertainty induces more, not less order in social systems, and in so doing primes them for sudden non-linear change.
Our third market stress indicator is the spread between the yield on AAA rated bonds and the 10-year US Treasury. This is a proxy for the level of investor concern about financial system funding liquidity.
Our fourth market stress indicator is the yield spread between speculative BB rated bonds and the ten-year US Treasury. Throughout history, excessive credit growth has been a root cause of many financial crises. An indicator of such growth is falling credit spreads, particularly in the case of riskier borrowers. In contrast, rising BB spreads indicate growing investor concern about the consequences of such growth, and the financial distress lower rated companies could experience in an economic downturn.
Our fifth market stress indicator is what we term the “political risk premium” that is implicit in the price of gold. Our starting point for estimating this premium is the three different roles that gold plays. First, gold is a store of value in a world of fiat currencies. When the rate of money supply growth exceeds the growth of nominal GDP, gold’s price should increase to maintain its purchasing power. Between 2007 and 2017, the US money supply (M2) grew by about 86%, while nominal US GDP grew by 35%. The stock of gold grew by 18%, based on mine production over this period. We therefore infer that 33% of the increase in the price of gold represented the maximum potential gold price change that could be attributed to a desire to hedge inflation risk (86% less 35% less 18%).
Second, gold is a unit of account. We take this to mean that the annual change in GDP expressed in terms of physical gold (i.e., nominal GDP divided by the price of gold) should equal the change in real GDP calculated using the GDP price deflator to account for actual inflation over the period. A key challenge is the point at which to start this calculation.
We chose the price of gold in 1995/1996. In that period, the change in real global GDP measured using the IMF’s price deflator just about equaled the change in GDP measured in terms of physical gold. We interpret that coincidence as indicating that at that point in time, concerns about future inflation and political risk were minimal, and the change in the price of gold was mostly driven by its role as a unit of account. We calculated a subsequent series of gold prices that would produce the same change in “gold GDP” as the actual real GDP as calculated by the IMF. Between 2007 and 2017, “gold as a unit of account” warranted a 21% increase in its price.
Gold’s third role is as a hedge against inflation and what we term “political disaster” risk. We subtract the 21% estimated compensation for actual inflation from the 33% “gross” inflation risk hedge to derive an apparent 12% increase in the gold price that reflected the true risk premium to hedge against possible future inflation. However, between 2007 and 2017 the price of gold actually increased by 81%. This implies that 48% of this (81% less 21% less 12%) represented a premium for some other type of uncertainty at the end of 2017. The interesting question is the nature of the uncertainty for which gold is believed by some investors to be a superior hedge than traditional ports in a storm like short-term US government securities, or similar securities issued by other developed countries.
The logical inference is that the uncertainty in question must reflect a situation in which short term US Treasuries would be a less effective hedge than gold. This could be a world of widespread hyperinflation, capital controls, and/or radical changes in nations’ governments (of course, this would also imply a preference for investing in gold coins rather than bullion, as while the latter may be a store of value, it is far less convenient as a means of paying for transactions).
To put this in further perspective, this gold price “disaster risk” premium sharply increased from 2008 to 2012, then declined before sharply increasing again after 2016. Arguably, a significant part of the former increase reflects concerns about the potential inflationary consequences of dramatic quantitative easing by central banks. But this is not likely to be the case after 2016.