A bit like love, eh?

This article offers some good insights into monetary manipulation. The one thing I see missing is the recapitalization of assets based on depressed long-term interest rates, which is a result of Quantitative Easing and Zero Interest Rate Policy (ZIRP). So we have massive asset bubbles across many real asset classes as a result. No one seems to have any idea how this unwinds, but unwind it must.

‘Quantitative Easing’ Isn’t Stimulus, and Never Has Been

By Ken Fisher, RealClearMarkets

(AP Photo/Jose Luis Magana)

Upside down and backwards! Nearly 13 years since the Fed launched “quantitative easing” (aka “QE”), it is still misunderstood, both upside down and backwards. One major camp believes it is inflation rocket fuel. The other deems it essential for economic growth—how could the Fed even consider tapering its asset purchases amid Delta variant surges and slowing employment growth, they shriek! But both groups’ fears hinge on a fatal fallacy: presuming QE is stimulus. It isn’t, never has been and, in reality, is anti-stimulus. Don’t fear tapering—welcome it.

Banking’s core business is sooooooo simple: taking in short-term deposits to finance long-term loans. The spread between short- and long-term interest rates approximates new loans’ gross profit margins (effectively cost versus revenue). Bigger spreads mean bigger loan profits—so banks more eagerly lend more.

Overwhelmingly, people think central banks “print money” under QE. Wrong. Very wrong. Super wrong! Under QE, central banks create non-circulating “reserves” they use to buy bonds banks own. This extra demand boosts bond prices relative to what they would be otherwise. Prices and yields move inversely, so long-term interest rates fall.

Fed Chair Jerome Powell and the two preceding him wrongheadedly label QE stimulus, thinking lower rates spur borrowing—pure demand-side thinking. Few pundits question it, amazingly. But economics hinges on demand … and supply. Central bankers almost completely forget the latter—which is much more powerful in monetary matters. These “bankers” ignore banking’s core business! When short-term rates are pinned near zero, lowering long rates shrinks spreads (“flattening” the infamous yield curve). Lending grows less profitable. So guess what banks do? They lend less! Increase demand all you want—if banks lack incentive to actually dish out new loans, it means zilch.  Stimulus? In any developed world, central bank-based system, so-called “money creation” stems from the total banking system increasing net outstanding loans. QE motivates exactly the opposite.

Doubt it? Consider recent history. The Fed deployed three huge QE rounds after 2008’s financial crisis. Lending and official money supply growth shriveled. In the five pre-2008 US expansions, loan growth averaged 8.2% y/y. But from the Fed’s first long-term Treasury purchases in March 2009 to December 2013’s initial taper, loan growth averaged just 0.8% y/y. After tapering nixed the nonsense, it accelerated, averaging 5.8% until COVID lockdowns truncated the expansion. While broad money supply measures are flawed, it is telling that US official quantity of money grew at the slowest clip of any expansion in history during QE.

Now? After a brief pop tied to COVID aid, US lending has declined in 12 of the last 14 months. In July it was 4.7% above February 2020’s pre-pandemic level—far from gangbusters growth over a 17-month span.

Inflation? As I noted in June, it comes from too much money chasing too few goods and services worldwide. By discouraging lending, QE creates less money and decreases inflation pressure. You read that right: QE is disinflationary. Always has been. Wherever it has been tried and applied inflation has been fried. Like Japan for close to …ah…ah…ah….forever. Demand-side-obsessed “experts” can’t see that. But you can! Witness US prices’ measly 1.6% y/y average growth last expansion. Weak lending equals weak real money growth and low inflation—simple! The higher rates we have seen in recent months are all about distortions from lockdowns and reopenings—temporary.

The 2008 – 2009 recession was credit-related, so it was at least conceivable some kind of central bank action might—maybe kinda sorta—actually help. Maybe! But 2020? There was zero logic behind the Fed and other central banks using QE to combat COVID. How would lowering long rates stoke demand when lockdowns halted commerce?

It didn’t. So fearing QE’s wind-down makes absolutely no sense. Tapering, other things equal, would lift long-term rates relative to short rates—juicing loans’ profitability. Banks would lend more. Growth would accelerate. Stocks would zoom! Almost always when central banks try to get clever they wield a cleaver relative to what they desire.  A lack of FED action is what would otherwis be called normalcy.

Fine, but might a QE cutback still trigger a psychological freak-out, roiling markets? Maybe—briefly. Short-term volatility is always possible, for any or no reason. But it wouldn’t last. Tapering is among the most watched financial stories—has been for months. Pundits over-worry about it for you. Their fretting largely pre-prices QE’s end, so you need not sweat it. This is why Powell’s late-August Jackson Hole commentary—as clear a statement that tapering is near as Fed heads can make—didn’t stoke market swings. The ECB’s September 9 “don’t call it a taper” taper similarly did little. Remember: Surprises move markets materially. Neither fundamentals nor sentiment suggest tapering is bear market fuel.

Not buying it? Look, again, at history. The entrenched mythological mindset paints 2013’s “Taper Tantrum” as a game-changer for markets. Untrue! After then-Fed Chairman Ben Bernanke first hinted at tapering back in May 2013, long-term Treasury bond prices did sink—10-year yields jumped from 1.94% to 3.04% by that yearend. But for US stocks, the “tantrum” amounted to a -5.6% decline from May 21 through late June—insignificant volatility. After that, stocks shined. By yearend, the S&P 500 was up 12.2% from pre-taper-talk levels. Stocks kept rising in 2014 after tapering began. 10-year yields slid back to 2.17%. My sense is even tapering’s teensy impact then is smaller this time because, whether people consciously acknowledge it or not, we all saw this movie before.

Taper terror may well worsen ahead of each coming Fed meeting until tapering actually arrives. Any disappointing economic data will spark cries of “too soon!” Tune them down. History and simple logic show QE fears lack the power to sway stocks for long.

Ken Fisher, the founder, Executive Chairman and co-CIO of Fisher Investments, authored 11 books and is a widely published global investment columnist.

Risk-Free? No Such Thing.

‘There is no such thing as a risk-free world’

Excellent interview. Some excerpts:

There are sections of the public health and scientific community that have become infected with the general level of fear and anxiety in the population. You see that very clearly in the recent open letter signed by teaching unions and various behavioral scientists – more or less a coalition of the anxious – demanding that face masks be mandatory in schools until at least 21 June. These people are looking for a risk-free world. Anybody who understands the nature of risk knows that there is no such thing. The eradication of risk comes at unacceptable social and economic costs wherever you try to do it.

The propagation of fear is visible not just in advertising but also in the constant reiteration of certain symbols. You will find public health specialists say that they know masks don’t achieve very much, but they do remind people that there’s a pandemic going on. They address the question of how to keep people in a constant state of fear. 

Democracy is messy, it’s uncontrolled, it can be disruptive. But all of those things are actually really important for a good society. It’s out of the messiness that creativity and change and innovation come. We are erring too far towards elitist control, which is what edges us closer to the Chinese model of the party and the party scientists decreeing what a good life for citizens is and devising systems and structures to enforce it. That should concern us.

Why We Can’t Handle Pandemics

We find ourselves approaching Month Nine of a world-wide pandemic shutdown with only a few isolated exceptions across countries. There seems to be no end in sight. This should suggest that the particular nature of a virus pathogen defies a rational, measured social response, especially for a free democratic society.

This nature is defined by an unmeasurable risk shrouded in a fog of uncertainty that is reflected in the emotional fear incited by the coronavirus. That fear is amplified by several scientific realities: there is yet no sure treatment cure, there is yet no effective, preventive vaccine, and there is yet little verified knowledge about how the virus behaves and what effects it may have on long-term human health. These truths create the impenetrable fog of uncertainty that incites our fears.

On a societal level, fear is manifested in a loss of trust among fellow citizens who harbor different tolerances for risk and uncertainty, which leads them to question whether others share the same risk profiles and associated safety protocols and what those protocols should be.

All these factors taken together present a formidable challenge to those charged with messaging and managing an effective policy response, from politicians and agency bureaucrats to scientists and medical practitioners.

We’ve seen historic cases of how this plays out. There was the Black Death, smallpox, and frequent outbreaks of the plague during the Middle Ages. We have the Spanish flu and the polio epidemic a century ago and, in more recent times, the Avian and Swine flus, AIDS, SARS and Ebola. We have discovered, despite our interventions, that most of these pandemics run their course before dying out. The coronavirus that afflicts us today appears to have certain unique characteristics that distinguish it from other, more familiar virus pathogens. One is that it seems to spread easily and effectively, despite strict hygienic practices. Second, the health and fatality risks seem to skew more seriously against the old and the infirm.

But what is far more salient to our fates with this virus are not the epidemiological factors, but the psychological effects on society at large. The costs of these effects are largely ignored because they are based on unknown probabilities. What we need to know is what is going on inside the human brain that influences mass social behavior, with its potential for hysteria.

Over the past fifty years we’ve accumulated a wealth of psychological research that addresses behavior under extreme uncertainty. The foundational research was produced by the collaboration of two Israeli psychologists, Amos Tversky and Daniel Kahneman. Much of their work helped shape our understanding of behavior and decision-making under uncertainty and led to a Novel Prize in economics for Prof. Kahneman in 2002.

What is interesting is that some of the experimental study scenarios they developed closely approximate what we are experiencing today in real time and help to illuminate why we mismanage such uncertainty. In particular, there was an experiment then became widely known as the Asian Disease Problem. 

This experiment asked subjects to imagine that the U.S. is preparing for the outbreak of an unusual Asian disease, which is expected to kill 600 people. Two alternative programs to combat the disease have been proposed, assuming that the exact scientific estimate of the consequence of the programs is as follows:

If Program A is adopted, 200 people will be saved.

If Program B is adopted, there is a 1/3 probability that 600 people will be saved, and a 2/3 probability that no people will be saved. Which of the two programs would you favor?

An overwhelming majority chose Program A, and saved 200 lives with certainty rather than take a gamble where 600 might die. They chose the certainty over the risk of trying to save all.

A second group of subjects got the same setup but with a choice between two other programs:

If Program C is adopted, 400 people will die.

If Program D is adopted, there is a 1/3 probability that nobody will die and a 2/3 probability that 600 people will die.

When the choice was framed this way, an overwhelmingly majority chose Program D.

One can see that the expected outcomes here are identical: 200 people are saved for sure, while 400 people die for sure vs. 1/3 probability of saving all and a 2/3 probability of losing all.

The authors explain this flip as “framing” the issue either as a sure win or loss. When you frame the sure thing as a loss, people choose the gamble. But when you frame it as a gain, people pick the sure thing. The choice is determined by how the problem is framed. Furthermore, what led subjects to distinguish between a gain and a loss was a psychological state, which differs from individual to individual. We’ll explore how this may apply to the coronavirus pandemic, but first we must examine another key finding of Kahneman and Tversky’s research.

In another series of studies, they found that subjects were confused by remote probabilities. They feared a one-in-a-billion chance of loss more than they should and attached more hope to a one-in-a-billion chance of gain than they should. They treat all remote probabilities as if they are possibilities. People’s emotional response to extremely long odds leads them to reverse their usual taste for risk, and to become risk-seeking when pursuing a long-shot gain and risk-averse when faced with the extremely remote possibility of loss.

In gambles that offer a certain outcome, people willingly pay a premium beyond the expected outcome for that certainty. To predict how people actually choose when faced with radical uncertainty, one had to “weight” the probabilities with emotion. Then one can explain why people overpay when they buy insurance or lottery tickets. We reconcile this behavior as loss aversion to distinguish it from pure risk aversion.

The desire for certainty can extract a high price, but the problem we all face in life is that the only certainty is uncertainty. So, people unknowingly pay a high price for themere illusion of certainty.

I hope we are beginning to see how these human instinctual behaviors will play out when faced with a radically uncertain virus pandemic. So far, we have two premises supported by empirical studies:

  1. People react differently to risk depending on how the narrative of uncertainty is framed;
  2. People overestimate remote probabilities and react emotionally to loss aversion.

In the context of a real virus pandemic we have several more confounding factors. First, unlike the Asian Disease experiment, we have no idea of the probabilities of imagined scenarios, so we are heavily influenced by psychological factors. Second is the effect of the media and the bias of its messaging. It is common knowledge in the news industry that emotional sensationalism sells the product far better than sterile information: “If it bleeds, it leads.”  We see this in the emphasis of news reporting on daily death counts and unconfirmed fatality, hospitalization, and infection rates. Then these frequency counts and rates of change are projected in a straight line or even exponential function to invoke wild predictions of an apocalyptic future.

The effect on mass psychology, especially with the prevalence of unfiltered social media, transmutes rational prudence and caution into unrestrained fear and panic. This is very bad science, but profitable, if not good, journalism.

Finally, we must turn to the challenges facing public leaders in managing an uncontrollable pandemic crisis. What do they do? To fathom that we need to examine the incentives that these public officials themselves face, given that they’re subject to the same psychological effects as everyone else.

Public officials mostly face downside risk: they get blamed for obvious failure, yet the upside consists mostly of avoiding that blame. If they project 500,000 people might die if nothing is done, but then only 200,000 die, they can claim success. But if they claim only 150,000 may die and 200,000 actually die, then they are blamed for losing 50,000 lives. We saw these incentives in play early in the pandemic with wild predictions of 65% infection rates combined with 2% mortality rates, implying that millions if not tens of millions of people would die unless drastic measures were taken.

State and local officials could also avoid taking responsibility for the costs of these measures under the guise of Federal revenue sharing and disaster relief. In other words, they can pass the buck. The result of this risk-reward incentive structure means these public officials and politicians, with few notable exceptions, are inclined to be extremely risk and loss averse, no matter what the cost. This means rational economic trade-offs are ignored in favor of extreme measures pursuing the delusion of zero-risk tolerance.

Medical experts, trained under the Hippocratic Oath to do no harm, face these same incentives, no matter how competent they are. They are trained to heal and avoid deciding life-and-death trade-offs of a deadly pandemic.

We can summarize how this story inevitably plays out and if that helps us understand how the pandemic mismanagement has unfolded in reality. When the threat was a distant one in a distant land, almost all politicians understandably underplayed the threat. The outbreaks in Italy then set off the media messaging, hyping the fear of coronavirus infection and death from Covid-19. The fact that the crisis could be politicized in an election year added fuel to the fire.

Let us recall “Flatten the curve” back in March, 2020, when exponential functions of death counts were all the rage. At this time certain governors promoted incomprehensible exponential predictions of mortality rates amid demands for complete shutdowns of society. All of this unfolded in a deep shroud of uncertainty with media accounts of catastrophes in China, Italy, and Spain.

So, the risk was framed as the ultimate loss of life with probabilities that had little basis in empirical data and were presented as likely possibilities rather than truly remote probabilities. With public criticism and accountability falling on political leaders, “flatten the curve” suddenly was transformed into perpetual lockdowns with moving goal posts.

The uncertainty surrounding the virus pathology led medical experts to issue contradictory and inconsistent directives and advice. First masks were unnecessary, then they became mandatory. A distance of six feet was determined as the range of infectious spread, but then sitting on a beach in solitude was deemed unacceptable.

Soon the data began to reveal the narrowness of the at-risk population based on age and co-morbidities, but the media focus targeted singular anomalies associated with anecdotal cases, heightening fears that Covid-19 is a deadly threat to all. The demands for zero-risk tolerance grew among those more loss averse and less affected by economic lockdowns. Life could not return to normal until a vaccine was developed, ignoring the fact that no virus vaccines are near 100% effective.

Rational health practices to bolster immune systems and protect at-risk population cohorts have been forced into one-size-fits-all safety protocols. At the same time, the deadly health effects of an endless quarantine are being ignored for this overreaction to remote possibilities. More data has started to make this clear, yet we seem rooted in the emotion-driven mistakes of the status-quo.  

The risks of Covid-19 vary widely across the population based on personal characteristics, mostly age and immune health, so a general risk factor cannot be established, except that it is extremely small compared to most other health risks. For example, I am a 66-year-old, Caucasian male in good health with no immune issues. I plugged my data into two online calculators of my risk for contracting the virus and my contingent risk of dying from Covid-19. My risk of contracting the virus and getting sick is 2.2% or 2 in 100. Assuming that I actually do test positive and get ill, my chance of dying in considered high at 1.08% or 1 in 100. But multiplying these two risks (I can’t die of Covid-19 if I don’t contract the virus) gives me a risk factor of dying from Covid-19 of 0.024%. That’s not 2 of 100 or even 2 out of 1000, but 2 out of 10,000. 

The question, of course, is how much am I really willing to pay to avoid a risk of 2 out of 10,000? And I am in a high-risk age category. Selling Covid-19 life insurance has got to be one of the most lucrative business ideas imaginable.

Given the behaviors driven by human instinct and emotion and the incentive structures of free democratic societies, we should concede the inevitability that such crises will be mismanaged with public policy. (Authoritarian regimes, on the other hand, don’t face the same hurdles.)

This coronavirus pandemic is a global tragedy, one that is still ongoing. Unfortunately, the crisis gets magnified by our human failings. Probably the best free democracies can do is to take simple, not drastic, precautions and wait for the virus to resolve itself. We must live with that uncertainty.

Chalk it up to a costly and painful learning experience.