We’ve written about Erwin Schrödinger’s famous thought experiment several times in Epsilon Theory, notably here, here, and here. To reprise the intro to one of those notes …
“Most people confuse Schrödinger’s Cat with the Observer Effect. It’s a lot weirder and more important than that.
Here’s what Schrödinger’s Cat is NOT. There’s a live cat inside a box, and the act of opening the box to see the cat will break one of two glass vials also inside the box. If Glass Vial A is broken, a deadly poison is emitted that kills the cat. If Glass Vial B is broken, nothing happens and the cat stays alive. This is an example of an Observer Effect – that the act of observation determines the outcome.
In the true Schrödinger’s Cat thought experiment, the poison gas vial isn’t broken by the observer opening the box, but could break open by chance over some period of time. As in the Observer Effect experiment, though, there’s no way to know if the cat is alive or dead without opening the box. After you open the box, the observer knows for sure whether the cat is dead or alive. But before you open the box?
The insight of Schrödinger’s Cat is that the cat is alive AND the cat is dead before the box is opened. It’s not merely unknown whether the cat is alive or dead. The cat is actually alive AND actually dead at the same time.
Wait a second, Ben. What do you mean the cat is actually alive AND actually dead at the same time? Obviously that’s not true. The cat is either alive OR dead. There is a state of the world where the cat is dead, and there is a state of the world where the cat is alive. Maybe we can’t know whether the cat is alive or dead, but it MUST be one or the other. That’s reality.
Schrödinger is saying no, that’s not reality. Schrödinger is saying that reality is – in reality – probabilistic. That the actual physical reality is that the cat is both alive AND dead at the same time. Maybe our human experience of reality does not allow us to have pets that are alive and dead at the same time, but that’s our fault, not reality’s fault.
I’m being a little facetious, because Schrödinger developed his famous thought experiment as a critique of quantum physics, and it’s now used to describe different theories of superpositioning in that weird world, where the smallest building blocks of nature should theoretically exist in multiple states of nature simultaneously. In the macro world of real-life humans doing real-life things, a cat is truly either alive or dead, not both.”
Or is it?
In our real-life world of investing in markets, we frequently deal with real-life cats that are both alive AND dead at the same time.
Case in point, the largest liquid market for any single security on Earth: US Treasuries.
As of this morning, there are two very distinct forward pricing structures taking shape for 10-yr USTs (this phenomenon exists across the yield curve, but I’ll just focus on 10-yrs). There’s “Blue Wave” positioning, where Biden takes the White House and the Senate flips to the Dems, as well, and there’s every other election scenario.
In the Blue Wave scenario, the cat is dead … 10-year yields blow out to something between 1.00% and 1.25%.
In the anything-else scenario, the cat is alive … 10-year yields compress to something between 0.65% and 0.75%.
Barclays put out a note this morning estimating that the standard deviation in moves in 10-yr rates on election resultsalone is something like 25-30 bps, which is … staggering when the current 10-yr yield is 0.80% (oops, as I write this, we’re out to 0.85% … this is nuts!). And I put “on election results alone” in italics because, obviously enough, there’s a lot more happening in the world that could contribute to Treasury prices moving in one direction or another by a LOT.
Next Tuesday we will open the box and see if the US Treasury cat is alive or dead. But until next Tuesday the US Treasury cat is both alive AND dead.
This has been our real-life investment reality for the entire month of October. This has been our real-life investment reality for every asset class, not just USTs. This will be our real-life investment reality when everyone takes their month-end portfolio mark at 4p today … an entire portfolio that is both alive AND dead.
I wish I had some words of wisdom on how to invest when our actual honest-to-god reality is an exercise in quantum superpositioning, but other than the topics of prior notes – sell (lower your gross) until you can sleep at night & minimize your maximum regret in periods of technical uncertainty – I really don’t. But I will leave you with this.
Next Tuesday’s event isn’t the last box we will be opening over the next few months. It’s the first of many.
The twist is that I think the greater Dem team genuinely likes Joe Biden. I think that they are genuinely prepared to “sell out” for Joe Biden (using the term in the sports lingo, as a good thing) in a way that they were never willing to sell out for Hillary Clinton. I don’t think that stated Democratic apparatchik support for Joe Biden is virtue signaling, not in the least. I think it’s completely real.
The twist is that I think there are only two nationally prominent politicians in the United States today who instinctively understand social media and its ability to drive the common knowledge game to win a turnout election, and neither of them is named Joe Biden.
In an election where Covid-19 makes traditional, real world crowd-signaling difficult or impossible, social media provides an alternative narrative path to political success.
You may think that it is yet another example of political betrayal, yet another example of unconscionable sociopathy to hold large, non-socially distanced and mostly non-masked political rallies in the very middle of some of the hardest Covid-hit areas of the country.
Certainly I do.
But if you do not also recognize that the human animal is hardwired to respond positively to crowds of other human animals responding positively … if you do not also recognize that sweeping, cinematic video of large crowds cheering for something heroically framed in the middle distance will motivate highly positive reactions in the far larger crowd that watches that video … well, you’re missing one of the most powerful drivers of social behavior.
Donald Trump gets this.
Half a million people watched a live stream of Alexandria Ocasio-Cortez playing a video game with a small group of friends the other night.
Sorry, maybe you didn’t hear me ..
HALF A MILLION PEOPLE WATCHED AOC PLAY A VIDEO GAME THE OTHER NIGHT.
AOC gets this.
Joe Biden does not get this. At all.
What is this? This is the power of the crowd watching the crowd. This is why China still bans any mention of Tiananmen Square protests, now 30 years gone. This is why executions used to be held in public and why coronations and inaugurations still are. This is why sports are played in front of a live audience.
The power of the crowd watching the crowd starts revolutions and wars. It builds cathedrals and tears them down, too. The power of the crowd watching the crowd moves markets. The power of the crowd watching the crowd wins elections.
Especially turnout elections.
Especially turnout elections in a handful of states.
It’s not the rally crowd itself that is politically effective for Trump.
It’s the larger audience of Trump-sympathetic voters watching these rally crowds that is politically effective for Trump.
It’s politically effective because this election will not be decided by changing the mind of some loosely affiliated voter on the other side. This election will not be decided by convincing some hypothetical “undecided voter” to join your fold. No, this election – just like the 2016 election – will be decided by motivating more of YOUR people to get up off their asses and get to the polls than the other guy does with HIS people. And nothing motivates your people more than seeing and hearing a good-looking crowd of people that calls them to action by example.
This is why sitcoms are funny. This is why beer commercials work. This is why CNBC exists.
This is why Trump has a narrative path to victory today that he didn’t have three weeks ago.
Q: Is this enough for Trump to win Florida, Pennsylvania and Ohio?
I don’t know. I doubt it, although maybe that’s my political preference speaking. My sense is that this narrative reawakening for Trump is happening too late in an election where tens of millions of votes have already been cast. My sense is that Biden is still more likely to win than not. But the path for Trump is this: three in-person rallies per day in the five states that matter, use social media to distribute footage of those rallies as widely as possible to drive turnout in those states. That’s his best shot. That’s his only shot. It’s not a terrible shot!
Q: Could Biden counter this narrative path with a crowd-watching-the-crowd effort of his own?
Of course he could. And I don’t mean by holding big rallies like Trump. Even if Biden were a conscienceless sociopath who would risk his voters’ lives by encouraging them to gather en masse, I don’t think he has the draw or charisma to get a crowd anywhere near the size of Trump’s. But you don’t need to hold physical in-person rallies to create a “crowd” that can inspire the larger crowd of PA, FL and OH voters. What you need is imagination, like AOC showed with her Twitch livestream. What you need is creativity, like the NBA showed with their “crowds”. Go give an “impromptu” pep talk to a dozen “brave Americans” standing in a long, properly socially-distanced line for early voting (just be sure you’re not violating any electioneering laws!). Hell, do a series of those scripted town hall events in Florida. Just do that.
Instead we get this.
With one week to go in his campaign for President of the United States of America, the Democratic candidate is speaking in Warm Springs, Georgia to an impassioned crowd of at least … three? … non-reporters. I am not making this up.
The problem is that Joe Biden believes that polls are themselves an effective crowd signaling device. I mean, look at the Democratic primary. Biden’s entire early primary campaign was based on his “electability” as shown by … wait for it … POLLS. Then the actual voting started and Biden’s entire approach had to be scrapped for a just-stop-Bernie collective effort by all the other candidates on Super Tuesday.
Joe Biden loves to use polls as a signal to the crowd that the crowd is supporting Joe Biden.
To receive a free full-text email of The Zeitgeist whenever we publish to the website, please sign up here. You’ll get two or three of these emails every week, and your email will not be shared with anyone. Ever.
I don’t think it would be especially insightful for us to point out that everybody knows everybody knowsstimulus talks are what market participants are paying attention to. At this point, I think the idea that this is common knowledge is, well, common knowledge.
We did, however, think that it would be interesting to see how different patterns of language were more or less common among stimulus-related news reports. In other words, we thought it would be fascinating to see which Fiat News expression of “what the stimulus is really about” was the most connected and which was the least.
The network graphs below – produced using software from our friends at Quid – show articles referencing “stimulus” language since October 1. A dot is an article. A “cluster” designated by color and proximity indicates highly similar language, as does a line between two dots. North/south and east/west have no meaning other than proximity. The most connected dots and clusters are those which demonstrate the most similar and connected language. For each graph below, the bold-faced lines and dots reflect those which also reference the language of a range of secondary topics (e.g. families, the unemployed, markets, etc.) as highlighted in the graph’s title.
“Fiscal Stimulus is About American Families“
“Fiscal Stimulus is About Small Businesses“
“Fiscal Stimulus is About Financial Markets“
“Fiscal Stimulus is About the Unemployed“
If I were to ask you, “In which of those graphs does it feel like the bold-faced dots and lines are the most connected to the overall structure of the graph”, I suspect I could account for most of the answers with one of the following:
1) They all seem pretty close; or
2) Maybe the “About the unemployed” map by a little bit.
And that’s correct. Qualitatively, which is to say intuitively, and quantitatively, based on our measures of narrative attention. In short, there are a LOT of missionaries out there trying to tell you what prospective stimulus is about and what other parties are trying to make it about. It hasn’t coalesced into a single narrative structure, so far as we can tell.
But what is fiscal stimulus absolutely, positively not about? If you answered any of “the deficit”, “the debt”, “small government” or “the budget”, you are today’s big winner.
“Fiscal Stimulus is About the Deficit“
When (or, y’know, if) the election concludes peacefully and the turn of the calendar arrives, you will read a lot of predictions about what from this insane dumpster fire of a year will become an essentially permanent feature of our world. Want a sure bet?
To receive a free full-text email of The Zeitgeist whenever we publish to the website, please sign up here. You’ll get two or three of these emails every week, and your email will not be shared with anyone. Ever.
If there’s a single common thread in everything we write about politics here at Epsilon Theory, it’s that we believe our domestic political games have been transformed from coordination games, where positive-sum gameplay is at least possible, into pure competition games, where only zero-sum gameplay exists. We believe that has happened without any change in the “rules” of our domestic political games, but through the actions of political entrepreneurs who “defected” from the way the game was traditionally played. Once one political entrepreneur enjoyed success from this defection, ALL politicians were forced to follow suit, permanently changing the nature of the game.
The exact same thing happened in the NBA.
The NBA has had a 3-point line since 1979. This is not a new rule. It’s been tinkered with over the past 40 years, almost always to make the 3-point shot more difficult, but there is absolutely nothing new about this RULE. And yet, over the past 13 seasons (the elimination of midrange 2-point shots begins in earnest in the 2007-08 NBA season), the way that the NBA game is played has been totally transformed.
There are no more 2-point shots that aren’t lay-ups or put-backs from near the basket. For all practical purposes, they do not exist. There are lay-ups and there are free throws and there are 3-point shots. That’s it.
How did this happen? Were NBA coaches in the 28 year period from 1979 to 2007 just not as smart as NBA coaches today? Could they just not figure out that this was the way to win games? Were NBA players in the 28 year period from 1979 to 2007 just not as talented as NBA players today? Could they just not hit a 3-point shot?
Nope, Daryl Morey happened. I mean, there were others with similar ideas, but I’m going to give credit to (my friend) Daryl Morey, the just recently departed GM of the Houston Rockets. Daryl Morey is an entrepreneur in the truest sense of the word – he had a new idea and the guts to stake his career on that idea. When he was promoted to the GM role by Rockets owner Les Alexander in 2007, he didn’t change the rules of professional basketball, he changed the idea of how basketball should be played within those rules. He constructed a team and found coaches who would play basketball to maximize the impact of the 3-point shot, and the Rockets enjoyed quite a bit of success over the next 13 years. In fact, the Houston Rockets had the second best won-lost record in the entire NBA over Morey’s tenure as GM.
But the Rockets never won a championship. Why not? Because equilibrium. Because any idea that gives any sort of marginal advantage in the individual competitions that make up the overall game of professional basketball will be immediately copied by other GMs and coaches. Because as brilliant as Daryl Morey is and as talented as James Harden is, there are enough equally brilliant and equally talented people in the NBA to wash out the fleeting advantage of a good idea.
Once Daryl Morey’s new idea became the common knowledgeof the NBA – once everyone knew that everyone knew that the way to win NBA games is to maximize 3-point shots and lay-ups – then it became a permanent feature of the way professional basketball is played. It became an equilibrium.
And you can’t undo an equilibrium.
So today, any player who attempts a midrange 2-point shot will be benched, any coach who institutes a strategy for anything other than 3-point offense and defense will lose, and any GM who constructs a team that doesn’t emphasize 3-point shooting will be fired.
“Yay, 3-point shooting and lay-ups! Yay, free throws!”
Some people think this new NBA game is a good game. Certainly it’s working out just fine for basketball entrepreneurs. I think it’s a much worse game that we will never recover from without fundamental changes in the rules of the game. I think it’s not working out well at all for us NBA fans.
It’s exactly the same with politics.
Some people think this new political game is a good game. Certainly it’s working out just fine for political entrepreneurs. I think it’s a much worse game that we will never recover from without fundamental changes in the rules of the game. I think it’s not working out well at all for us citizens.
Every once in a very rare while, we see what we call a Missionary statement (an action or a speech by a famous person or organization that is widely distributed on a ubiquitous media platform) that has the potential to change the Common Knowledge (what everyone believes that everyone believes) about an important aspect of our investment lives.
For example, this February we highlighted the Missionary statement by the Tokyo Marathon organizers that they were canceling the 2020 race out of Covid concerns, leading to our view that the common knowledge around the business of sport was about to crack apart. This was in mid-February, well before anyone was considering (publicly, at least) canceling the Olympics or eliminating attendance in professional sports. For most people, the Tokyo announcement didn’t seem like a big deal, but when you spend your professional life immersed in narrative-world, as Rusty and I do, that announcement was like a car alarm going off, precisely because it was so different from the common knowledge of the time and it was presented so publicly.
Here’s another shockingly different and very public Missionary statement that’s striking us like a car alarm, published on October 12 in the FT:
QEP’s Wil VanLoh says country’s oil production capacity is lower than believed
Wil VanLoh, chief executive of Quantum Energy Partners, a private equity firm that through its portfolio companies is the biggest US driller after ExxonMobil, said too much fracking had “sterilised a lot of the reservoir in North America”. “That’s the dirty secret about shale,” Mr VanLoh told the Financial Times, noting wells had often been drilled too closely to one another. “What we’ve done for the last five years is we’ve drilled the heart out of the watermelon.”
“Even if we wanted to, I don’t think we could get much above 13m” barrels a day, Mr VanLoh said. “I don’t think it’s physically possible, because we’ve messed up so much reservoir. I would argue that what the US was touting three or four years ago, in theoretical deliverability, is nowhere close to what we think it is now.”
To be clear, I have no idea if VanLoh is right about this.
What I DO know, however, is that this Missionary statement has the potential to wreck one of the common knowledge pillars of the energy sector narrative specifically and the US economic growth and US foreign policy narratives more broadly, that the United States has achieved energy independence through the fracking “revolution”.
We took a quick look with the Narrative Machine at financial media using the phrase “energy independence” in Q4 2015, Q3 2018 and Q3 2020.
In all three of these narrative maps (each containing about 1,200 independent financial media articles), I’ve put a green oval around the Clean Energy narratives and a gold oval around the Politics/White House narratives. These are the constant narrative super-clusters within any time frame where we look at this question. What you’ll notice, though, is a new super-cluster in 2020 that I’ve marked with a blue oval, containing sub narratives that directly use the phrase “US Oil Independence” and its variants. This new super-cluster has the single largest article cluster with 9.3% of all articles published during the period, and more importantly occupies the center of the overall narrative map. Up/down/left/right doesn’t matter in these maps. Centrality does. Not only is “US energy independence” now explicitly a narrative super-cluster within a universe of articles that contain the words “energy” and “independence” – showing that it has cohered into a standalone narrative in its own right – but it is also the most central and influential narrative super-cluster within that universe.
But wait, there’s more. The overall narrative map in 2020 is much less cohesive than the overall narrative map in 2018. Numerically it’s about 40% less coherent, and visually you can get a feel for this by the greater number of individual clusters in 2020 and their greater dispersion from the map center off to the periphery. Our view is that a less cohesive map is a more complacent map, meaning that any narrative shock – like a Missionary statement that the US is NOT energy independent – is likely to wreak more havoc on the existing narrative system.
To be clear, it’s by no means certain that this Missionary statement will get picked up and amplified by other market missionaries in the near term. Also, until energy demand picks up and there’s an economic need to produce more than the current 11 million bbl/day, we won’t be able to test the proposition that 13 million bbl/day (our high water mark pre-Covid) is now the US production limit.
But Missionary statements like this don’t just go away.
Whenever we come out of this Covid recession and energy demand starts to pick up again, this Missionary statement will get some play. And depending on how other market missionaries pick up on it (or not), it could have a really broad impact, I think, on the entire US growth and economic strength narrative (and the politics around THAT), whether or not it is factually true.
Yes, whether or not it is factually true.
We live in Fiat World, where opinions expressed as news or fact pack just as much punch – if not more! – as actual news and actual facts. Whatever your view may be on the Truth with a capital T in regards to US oil reserves, I think we’ve all learned over the past 12 years that those views and that Truth can be wrong for a loooong time if the narrative is blasting loudly in a different direction. We’ll keep our eye on this for you and alert you to any signs of this Missionary statement getting more traction in narrative-world.
Not yet the wise of heart would cease To hold his hope thro’ shame and guilt, But with his hand against the hilt, Would pace the troubled land, like Peace;
Not less, tho’ dogs of Faction bay, Would serve his kind in deed and word, Certain, if knowledge bring the sword, That knowledge takes the sword away—
‘Love thou thy land, with love far-brought’, by Alfred, Lord Tennyson
From time to time, these pages refer back to the piece that Ben wrote for Epsilon Theory before the election in 2016. In it, we argued that Clinton’s candidacy was in trouble. That piece included a phrase that to this day confounds and frustrates a lot of readers. Ben wrote that Trump would break us.
Trump, on the other hand … I think he breaks us. Maybe he already has. He breaks us because he transforms every game we play as a country — from our domestic social games to our international security games — from a Coordination Game to a Competition Game.
Of course, Ben was right about everything in this piece. He was right about Clinton being in trouble. Right about us being broken. Still, a lot of people still struggle over the particulars of the language. They don’t like what sounds like the scale of our very public social breakdown being laid at the feet of an individual.
Get over it. It doesn’t matter.
Maybe you do think it was Trump himself – the person – who broke us. Maybe you think it was our predictable interactions under the gaze of a figure as polarizing as Trump – the hero worshippers and TDS sufferers alike – that broke us. Maybe you think we were already broken before and Trump simply pulled the bandage off the deep wounds in our coordination game. I’ll say it again: It. doesn’t. matter.
Sure, maybe it matters to how you and I will vote in a few weeks. And we should, even though we will do so under the weight of entrenched interests telling us that our vote is our sole venue to access political and social change. But our vote can’t change this. For the country we will hand off to our children, for the reality of our world for the next 20, 30 or 40 years, our brokenness isn’t on the ballot.
We can’t vote our broken politics out of office.
Earlier this week, the New York Post published a news story about Hunter and Joe Biden. You probably heard about it. Twitter blocked all mentions of the Post story, Facebook blocked a bunch of other things and then for good measure YouTube blocked QAnon conspiracy videos. Quite a week. Having quit Twitter, I suppose I don’t have to worry about being black-listed, so here it is.
It is…a lot to unpack. But may I confess to you that I am not particularly interested? It doesn’t really constitute information in any sense to me, by which I mean that it didn’t really change my mind about anything. I think that if you were at all surprised by the “revelation” that a life-long senator and former vice president of the United States was maybe involved in some measure of political corruption and light nepotism, you need to stop reading this note and commit a few days to deep personal reflection. I’m not saying that it isn’t newsworthy, and I’m not saying that it isn’t bad, if some of its implications end up being true. I AM saying that if any of that surprised you, you have been walking around with your eyes closed for the last 50 years.
As fuel for narratives with the capacity to change common knowledge, of course, the New York Post article and the responses it got from other outlets ARE absolutely fascinating. But even its importance in narrative-world isn’t what I found most informative. What was most informative was what the venues for this information told us about themselves.
So let’s start with this: if real, the email from Pozharskyi to Hunter is absolutely newsworthy.
Its provenance is worthy of serious questioning. Its contentions are worthy of discussion. The motivations behind its disclosure at this juncture are also newsworthy. Maybe more so. But the email itself is absolutely an item of public interest of some scale. Personally, I happen to think that scale is circumstantial and relatively small. You may disagree. Doesn’t matter.
What happened next does matter.
First, the only “news” departments to deem it newsworthy were those in media outlets whose “opinion” pages would favor the outcome of an explosive public response to its revelations. Here are the top publishers of articles referencing “Burisma” from October 14th or October 15th, 2020.
Meanwhile, let’s take a look at the output of some other key newsrooms.
CNN: We cannot locate a single article published by CNN during this period satisfying this query.
MSNBC: We located two articles. One is a roundup / digest-style piece that refers to the claims as nonsense and links to a Jonathan Chait piece. The other is an opinion piece which is a discussion of Giuliani’s seemingly obsessive attachment to the Ukraine issue (which is ALSO a newsworthy topic, if a distinct one). Nothing else we can find.
New York Times: The Times published three articles. Rather than summarize, I’ll let you decide for yourself. We cannot locate an active link to the third article mentioned below, but at risk of letting a headline do too much of the work, its bent seems more or less self-explanatory. The other two are classic Fiat News examples of reframing: “This is how you should think about these emails,” packaged into news.
Biden Did Not Meet With Ukrainian Energy Executive, Campaign Says [New York Times]
Washington Post: The Washington Post took a more active role in contesting the core allegations, publishing a fact check-style piece alongside a range of other takes. In all, one opinion piece and five news pieces, all positioning themselves in opposition to the Post’s original news piece. Fiat News all around.
I have zero interest in engaging on any discussion about whether Fox News and Breitbart’s 50-article barrage was an exaggeration of a nothing-burger, or whether the New York Times and CNN pretending it was only worthy of explaining away was “worse” or more indicative of bias. We have seen and written about widespread differences in the perception of actual news events before.
Still, the magnitude of the difference with which organizations purporting to tell us the facts of the world perceived the newsworthiness of a fact of the world in this case exceeds just about anything we have seen in the last four years. ALL of our media outlets have uniformly empowered their news rooms to reflect the editorial and political predispositions of their publishers. It is a gross betrayal.
I’m sure you have perspectives and preferences about all of the above. If so, I have a question for you.
Do you think this goes away between November 3rd and November 4th?
If there is a story that presented a close second place in terms of the divergent evaluations of its newsworthiness, however, it was certainly the publishing of Donald Trump’s tax returns by the other paper in New York on September 27th. It was followed by a firestorm of follow-up news coverage and opinion pieces from across the spectrum.
It is…also a lot to unpack. May I confess to you once again that I am not particularly interested? It doesn’t really constitute information in any sense to me, by which I mean that it didn’t really change my mind about anything. I think that if you were at all surprised by the “revelation” that a brand-pushing real estate investor with a penchant for bankruptcies has mastered the art of finding dubious losses to reduce taxable income, you need to stop reading this note and commit a few days to deep personal reflection. I’m not saying that it isn’t newsworthy, and I’m not saying that it isn’t bad. I AM saying that if any of that surprised you, you have been walking around with your eyes closed for the last 50 years.
As narratives with the capacity to change common knowledge, of course, the New York Times article and the responses it got from other outlets ARE absolutely fascinating and potentially far-reaching. This is, after all, a man who built his narrative on wins, not losses. But even its importance in narrative-world isn’t what I found most informative. What was most informative was what the venues for this information told us about themselves.
So let’s start with this: Donald Trump’s tax returns and the details within them are absolutely newsworthy.
Their provenance is worthy of questioning. Their implications are worthy of discussion. The motivations behind their disclosure at this juncture are also newsworthy. But the returns themselves are absolutely an item of public interest of some scale. I happen to think that scale is pretty meaningful, not so much because I care about anyone minimizing their taxes (on the contrary, I consider it every American’s solemn duty), but because the reality seems to conflict with prior statements and appears to include some dubiously aggressive interpretations of tax law, potentially concerning debt, and potential improprieties in consulting payments, etc. You may disagree. Doesn’t matter.
What happened next does matter.
First, most of the “news” departments to deem it newsworthy were those in media outlets whose “opinion” pages would favor the outcome of an explosive public response to its revelations. To keep the periods in question consistent, here are the top publishers of articles referencing “Trump” and “Tax Returns” from September 27th or September 28th.
It isn’t quite the monoculture of those who deemed the Biden email an earth-shattering scoop, but peeking underneath the hood, it’s close. How about the “other side” of the aisle from an editorial perspective?
While it did get some discussion on the air, a query of news published on the Fox News website turned up zero news articles relating to the New York Times findings on Donald Trump’s tax returns over those two days. They did muster, however, an outraged opinion piece.
The Daily Wire (Ben Shapiro’s outfit) filed two articles as “news” reports. Both would fall squarely under our definition of Fiat News. The first simply aims to adjust readers’ interpretations to a “not illegal” framing. The second frames the issue as being more about Joe Biden’s mockery-worthy response to the report.
The Daily Caller (until this summer Tucker Carlson’s home away from Fox) posted two articles as well. Both can be easily characterized as Fiat News. The first is designed to build a foundation for a framing that “Trump has always been honest about avoiding taxes.” The second frames the issue as really being about CNN’s bias.
Breitbart manages to be the fourth most prolific publisher of articles. That looks like a broken pattern…until you begin to review the articles themselves. The vast majority select and summarize video clips to provide a megaphone to obvious defenses of the President against the implications of the Times’s work, mostly through some (and this is putting it kindly) creative framing.
Maybe you think a 20-story barrage from the WaPos of the world is the “exaggerated” version of this story, or maybe you think that the non-coverage is the more indicative of a news room infected by an organization’s editorial and opinion posture. Either way, we may still observe that the gap in how simple facts are presented and reported, not on opinion pages but in black and white news, is vast.
I’m sure you have perspectives and preferences about all of this. If so, I have a question for you.
Do you think this goes away between November 3rd and November 4th?
That’s not all, of course.
On October 14th, after the New York Post published its piece, Twitter chose to implement a “long-standing” policy restricting the spread of materials which may have been acquired without the permission of the individuals referenced, hacked or stolen. In other words, Twitter blocked access to the New York Post article and suspended accounts of some of those who linked to it, despite lacking any evidence that it was ill-gotten. And they did so despite having happily permitted the New York Times article from two weeks prior to spread like wildfire, despite the Times having acquired the tax returns in undisclosed ways, and despite Trump himself claiming that they were acquired illegally.
More than that, to any Trump-supporting conservative it was a confirmation in narrative world of the reason most have used to justify their sometimes-grudging support: that only a Trump could counter the unlevel playing field created by news outlets and social media platforms in which progressive politics seep from opinion pages to news pages. It is the most powerful justification we humans have for signing on to corruption – that it serves a greater truth. And whether you believe in it or not, the “greater truth” of a news media and social media industry hopelessly derisive toward political conservatives is absolutely one of the reasons the election of Trump was able to break us.
I expect that some readers will comfort themselves with the idea that one of the stories above really was a nothing-burger, that the other one really was a big-effing-deal that people aren’t talking enough about, that the differences in coverage just reflect that reality has a left/right-leaning bias, and that this is really just evidence that our side is populated by all the unbiased clear thinkers.
Let’s say those readers are right. I mean, they aren’t, but let’s say that they are.
In a political world in which those responsible for telling us the truth provide us with two distinct sets of facts, even if we are 100% convinced that our facts are always the correct ones and our truth-tellers the honest ones, dismantling the competition game that results in politically polarized truth-tellers should STILL be a huge objective.
‘Knowledge brings the sword’‘
If we believe we are right, we should seek truth and fight for what we believe it is.
‘Knowledge takes the sword away‘
Even when we are absolutely convinced we are right, we will still benefit from actively seeking to create opportunities for cooperative game play. Or, you know, clear eyes and full hearts. Anything which structurally supports the infection of news pages with the sentiments of a publication’s opinion pages is always and in all ways anathema to that objective.
How do we do that in our media consumption? Some intangible thoughts and some tangible ones follow:
Act Boldly, Hold Loosely: It’s OK to believe we’re right, and we should act boldly on those beliefs. We must! But seeking out cooperative gameplay in the widening gyre – a world of two sets of facts – means not immediately dismissing people who hold to a set of facts that will seem absolutely ludicrous to us. Sometimes that instinct will be right. This doesn’t mean letting those content to wallow in obstinate ignorance waste our time. More often, I think it means being intentional about providing a few instances of uncomfortable patience, grace and humility before we dust off our shoes and move on.
Transition to Regional Newspaper Consumption: There is a crowd-watching-the-crowd effect that manifests in news outlets designed for national consumption and social media consumption. Once an outlet decides that it is part of the “national dialogue”, it will be inexorably pulled into the widening gyre. There are a wide range of city papers in the US in which the editorial page is very appropriately partisan without excessively poisoning its news pages. Anecdotally from our Fiat News work, we have found the Chicago Tribune, Houston Chronicle, Miami Herald and San Francisco Chronicle to be among them.
BITFD: There is a projection racket which defends polarized national media from criticism of their commercially oriented, rage-opinion-funded-and-infected news pages. It’s time to work together to restore the fourth estate and empower the fifth estate, and dismantling those projection rackets is an important part of doing so. More on this to come.
If you have publishable academic research that you think expands our collective understanding of financial or political markets, and you’d like to give it access to our network of 100,000+ investment professionals, asset owners, academics and market enthusiasts, please send it to us at [email protected]
Will making academic journals irrelevant save the world? No.
Mike Aguilar is a Teaching Associate Professor at the Department of Economics, University of North Carolina at Chapel Hill and the Chief Investment Officer at Cardinal Retirement Planning, Inc. in Durham, NC. Email: [email protected]
Ruyang Chengan is an Analyst at Dimensional Fund Advisors in Charlotte, NC. Email: [email protected]
Anessa Custovic is a Quantitative Research Analyst at Cardinal Retirement Planning, Inc in Durham, NC. Email: [email protected]
The tracking error is a ubiquitous tool among active and passive portfolio managers, used widely for fund selection, risk management, and manager compensation. In this paper we show that traditional measures of tracking error are incapable of detecting variations in higher order moments (e.g. skewness and kurtosis). As a solution, we introduce a new class of Quantile Tracking Errors (QuTE), which measures differences in the quantiles of return distributions between a tracking portfolio and its benchmark. Through an extensive simulation study we show that QuTE can detect variations in higher order moments. We also offer guidance on the granularity of the quantile grid and weighting schemes for the relative importance of various quantiles. A case study illustrates the benefits of QuTE during the Dot Com Bubble and the Great Recession
Traditional measures of tracking error are inadequate. Although there are several variants, most commonly tracking errors are cast as squared deviations between a tracking portfolio and benchmark over some period of time. However, this type of quadratic structure is inconsistent with the linear performance fees through which most managers are compensated (see Kritzman ). Instead, managers are incentivized to avoid extreme return deviations (Rudolf, Wolter, and Zimmermann ), which implies that higher order moments, such as kurtosis, are relevant. Moreover, Beasley, Meade and Chang  suggest that managers are incentivized to avoid consistently underperforming their benchmark, suggesting that skewness is also relevant.
Dorockov  and Blume and Edelen  point out that the goal of a tracking error is to measure how closely a portfolio can exactly replicate its associated benchmark. There is a preponderance of evidence that asset returns are non-Gaussian. Mills  documents excess skewness and kurtosis in daily asset returns, while Chung, Johnson and Schill  document it for monthly  asset returns as well. Therefore, tracking only the first two moments, as do conventional measures, is insufficient.
Other shortcomings of traditional tracking error measures have been cited. For instance, Pope, and Yadav  illustrate the bias in tracking error due to serial correlation in returns. Moreover, Ammann and Tobler  recognize that tracking error variance is subject to sampling error.
This paper makes two contributions to the literature on portfolio tracking. First, we detail a previously undocumented shortcoming of traditional tracking errors. Through a simulation study we show that traditional tracking errors (such as average tracking error and tracking error volatility) fail to detect situations in which the skewness (and/or kurtosis) of the tracking portfolio differs from that of the associated benchmark.
The second contribution of this paper is to introduce a class of quantile based tracking errors (QuTE). As we will discuss in Section 2.2, there are many variants of tracking error. Some have symmetric loss functions, structured via absolute or squared deviations. Meanwhile other variants incorporate asymmetries visa vis semi standard deviations, which are aligned with downside risk. Each have an analogue within our quantile based measures. We show that even the most basic of these QuTE measures is able to detect deviations in higher order moments of returns.
We begin with a detailed accounting of the traditional measures of tracking error alongside the newly proposed quantile based measures. We then conduct an extensive simulation study to explore the relative merits of QuTE. Finally, we document historical episodes where QuTE was able to detect important differences between a tracking portfolio and it’s benchmark, while the traditional measures were unresponsive.
In this section we detail the lineage of tracking errors and provide a compendium of its variants. We complement with an introduction of the new QuTE class of tracking errors.
Equation (1) was seen first in the academic literature in Franks , which defined it simply “excess of benchmark returns”. Among practitioners, the object in Equation (1) is sometimes referred to as Tracking Difference. Roll  refers to this object as “Tracking Error”, which we find to be commonly applied within the proceeding academic literature, and as such reserve that terminology throughout the balance of this paper. Note that the object in Equation (2) is simply an average of the Tracking Error over a period of time.
The object in Equation (3) is the next most commonly used variant of the term Tracking Error. Franks  refers to this object as Tracking Error, whereas Roll  refers to this as Tracking Error Volatility (TEV). Many proceeding academic studies (see Jorion ) use the TEV terminology. Moreover, Equation (3) is commonly referred to as Tracking Error among practitioners. Often this is reported as an annualized value. Equation (4) is subtly distinct, but is less often used in the literature than is Equation (3). Used by Ammann and Tobler , it captures the square root of the sum of the squared tracking error. Root Mean Squared Tracking Error (RMSTE) in Equation (5) was used by Chincarini and Kim  as a way to capture both the variability and the level of the tracking errors.
As noted by Kritzman , portfolio managers are rewarded by linear performance fees based upon the differences between their portfolio and the corresponding benchmark. Rudolf, Wolter and Zimmermann  argue, that due to this fact, linear deviations between the portfolio and benchmark give a more accurate description of the investors’ risk attitudes than do squared deviations. As such, tracking measures based off of absolute, rather than squared differences, such as those in Equation (6) and Equation (10) are sometimes advocated.
Both the quadratic and absolute measures heretofore are inconsistent with investor loss aversion. Rudolf, Wolter and Zimmermann  advocate the use of semi-variances for downside risk measurement. Equations (7) – (10) reflect this downside risk.
Finally, Beasley, Meade and Chang  introduce a generalized tracking error written as
Intuitively, QuTE compares two assets via differences in the quantiles of their respective return distributions. This is especially useful in finance given the preponderance of returns with excess skew and kurtosis, and quantile-based methods’ ability to capture these distributions (see Rostek ). Moreover, a quantile based approach is consistent with the utility maximization via quantile maximization of Rostek , as well as with Giovannetti , who builds an asset pricing model consistent with CRRA preferences via quantile maximization.
Since the Value-at-Risk (VaR) is merely a quantile of a return distribution, we can see QuTE as matching on the space of VaR’s are various levels. Yamai and Yoshiba  show us that portfolio ranking via VaR is consistent with expected utility maximization and is free of tail risk. We adapt the findings of Rostek , who characterizes the behavior of an agent evaluating different (investment) alternatives by the -th quantile of the implied (return) distributions and selects the one with the highest quantile payoff. We can represent an investor’s preferences via the quantiles of the associated return distribution. In the context of benchmark tracking, we can then cast the investor’s preferences for deviations from their benchmark via the differences in the quantiles of the portfolio and benchmark. Portfolio construction with VaR based objective functions is increasingly common (see Gaivoronski and Pug  for recent examples). Moreover, a quantile based approach is especially attractive given the prevalence of VaR for portfolio risk management. For instance, Follmer and Leukert  uses VaR in the context of dynamic hedging.
Note that a natural analogue to QuTE is moment based matching, rather than quantile based. One could use a method of moments type estimator to match a select set of empirical moments between the benchmark and optimal portfolio. Although potentially attractive, a moment based approach lacks the flexibility of a nonparameteric quantile based method.
Notice the similarities with the tracking error measures defined in Section 2.1. Importantly, the averaging in the QuTE class is not done over time , but rather across quantile levels . The QuTE measures never force the portfolio managers to compare his/her portfolio to the benchmark on a daily basis. This might mitigate the problem of “short termism” as indicated by Ma, Tang and Gomez . Specifically, short evaluation periods for performance based compensation may damage fund performance by incentivizing managers to engage in such activities as risk shifting and window dressing to boost short-term performance.
Since there is a one-to-one mapping between the quantiles (returns) and the quantile levels (probabilities), portfolio tracking via QuTE can be cast within the wide literature of distribution matching. Cast this way, QuTER falls within the Fidelity Family of similarity measures. These types of measures are used in a wide variety of fields.
Beasley, Meade and Chang  expand their tracking error to accommodate for the case where someone might want to weigh the importance of the return deviations differently over time. Analogously, we introduce a quantile weighted version of QuTE. We illustrate below for the case of QuTER, but this approach can easily be extended to any of the measures within the QuTE family.
In this section we explore the differences between QuTE and traditional TE tracking measures. Of particular importance, in subsection 3.1, is the sensitivity of each measure to differences in the empirical distributions of the benchmark and tracking portfolio. Subsections 3.2 and 3.3 focus on robustness of QuTE to various calibrations.
Sensitivity to Differences in Return Distributions
In this subsection we conduct a simulation study to evaluate the traditional tracking error measures of Section 2.1 as well as the QuTE based measures of Section 2.2. We craft a toy exercise that, while simple in nature, permits us to highlight the sensitivity of the tracking errors to differences in the underlying return distributions. Given the preponderance of evidence citing skewness and kurtosis (see Chung, Johnson and Schill , Mills , among others) in asset returns, coupled with the calls for linear performance measures a la Rudolf, Wolter and Zimmermann  and Kritzman , we consider deviations in these “higher order” moments.
We begin by creating a benchmark portfolio. For simplicity, we assume the returns of the benchmark follow a standard Normal distribution. We calibrate the length and empirical moments of the benchmark to match that of the monthly returns on Dow Jones Industrial Average over the period 1985 through 2019. This same index is used in a Case Study detailed in Section 4. Our simulations contain 10,000 paths, each of length 414 months.
Next, we generate a tracking portfolio that follows one of five distinct distributions, which are depicted in Table 1. In Case 0, the tracking portfolio has the same distribution as the benchmark portfolio. In Case 1, they differ only in the mean. Similarly, Case 2 varies in terms of variance, Case 3 in terms of skewness, and Case 4 in terms of kurtosis.
We explore the ability of the various traditional tracking measures to detect differences in the mean (standard deviation, skewness, kurtosis) of the tracking portfolio and benchmark. As noted in Section 2.1, the TEV depicted in Equation (3) is the most commonly used tracking measure among academics and practitioners. We compare the TEV to ATE, TER, and RMSTE.
First, we vary the mean return of the tracking portfolio in excess of the benchmark (i.e. excess mean) in the range. Next, we compute the ATE, TER, RMSTE and TEV for each of these values of excess mean, simulated and averaged over 10,000 paths. Finally, we scale the values for each of the cases for ease of visual comparison. Panel A of Figure 1 depicts the ATE, TER, RMSTE and TEV values over the range of excess mean values. Panels B, C, and D similarly reflect excess standard deviation, skewness, and kurtosis.
A desirable measure of tracking error should achieve a minimum at an excess mean (standard deviation, skewness, kurtosis) of 0, i.e. when there is no difference between the tracking portfolio and benchmark, the tracking error measure should be at its low point. We find that ATE is unable to detect changes in any of the four moments. Meanwhile, TEV performs similarly to TER and RMSTE across Cases 2 through 4. In this sense, TEV is roughly equivalent to TER and RMSTE.
Next, we compare the traditional and quantile based tracking measures in terms of their abilities to detect differences in the underlying statistical distributions of the benchmark and tracking portfolios. Our comparison is centered around the TER of Equation (4) and the QuTER of Equation (12). We note our prior findings that TER is roughly equivalent to the popular TEV, which makes this comparison relevant. Moreover, we note that QuTER is a direct analogue of QuTER, providing a fair comparison.
In Table 2 we explore these relative sensitivities by computing the percent change in the (Qu)TER statistic relative to Case 0. The greater is the percent change in the (Qu)TER in Case 1 relative to Case 0, the more sensitive is that measure to variations in the means of the two series.
The p-value of 0 for Case 1 in Table 2 implies that the percent change in the QuTER statistic for Case 1 relative to Case 0 is not equal to the percent change in the TER statistic for Case 1 relative to Case 0. In fact, we find that QuTER and TER have unequal sensitivities to differences in each of the first four statistical moments. Moreover, one-tailed t-tests suggest that the QuTER is in fact more sensitive than TER in all Cases.
We explore these findings further by conducting a sensitivity analysis as we did above. Again, we vary the degree of mean returns in the tracking portfolio in excess of the benchmark (i.e. excess mean) in the range. Next, we compute the TER and QuTER for each of these values of excess mean, simulated and averaged over 10,000 paths. Finally, we scale the values for each of the cases for visual comparison. Panel A of Figure 2 depicts the TER and QuTER values over the range of excess mean values. Panels B, C, and D similarly reflect excess standard deviation, skewness, and kurtosis. Again, a desirable measure of tracking error should achieve a minimum at an excess mean (standard deviation, skewness, kurtosis) of 0, i.e. when there is no difference between the tracking portfolio and benchmark, the tracking error measure should be at its low point.
Panel A of Figure 2 suggests that TER and QuTER are both sensitive to variations in the mean return of the tracking portfolio and benchmark. They each reach minimum values near 0 excess mean, and rise at values above and below that amount. Similarly, Panel B illustrates that both TER and QuTER appear sensitive to deviations in excess standard deviation. However, Panels C and D illustrate that TER is not sensitive to deviations in skewness nor kurtosis. Meanwhile QuTER continues to respond to these excess variations. We note that these findings are consistent for ATE/AQuTE, AATE/AAQuTE, and ATR/AQuTER.
We can see from Table 3 that the estimated is positive and statistically significant for Cases 1, 3, and 4. This finding aligns with Figure 2, where QuTER appears to detect changes in the third and fourth moment, while TER is unable to do so. In terms of kurtosis, it appears that QuTER grows at least twice as fast per unit of change in excess kurtosis as TER does. Overall, we find that the sensitivities of the quantile based tracking errors are different, and in most cases larger, than the sensitivities of the traditional tracking errors.
Robustness to Granularity of Quantile Grid
In this subsection we explore whether the granularity of the quantile grid for the QuTE statistics impacts their ability to detect differences in the distributions of the tracking portfolio and the benchmark.
We repeat the exercise of Section 3.1 by simulating the benchmark returns as simple Gaussian noise and then varying the tracking portfolio in four ways; Case 1 alters the mean, Case 2 alters the variance, Case 3 alters the skewness, and Case 4 alters the kurtosis. Figure 3 depicts the percentage change in the QuTER statistic in a given Case relative to Case 0. The x-axis varies the size of the quantile grid (). The reported values are the median across 10,000 simulated paths.
We find that the percentage change in the QuTER statistic falls as the number of quantiles in the grid rises. The relationship appears to plateau near 10 quantiles. This stability is important, indicating that the QuTER measure is robust to choice of quantile grid.
Impact of Varying Quantile Weights
In this subsection we explore whether variations in the quantile weighting scheme impact QuTE’s ability to detect deviations between the distributions of the tracking portfolio and benchmark.
Blitz and Hottinga  illustrate how to compare various investment strategies via a Tracking Error framework. They consider weighting strategies by several methods of importance, such as tracking error, information ratio, and the like. In a similar vein, we can weight various quantiles by whatever criterion is most important to the investor. In the following, we consider four weighting schemes: equal weight, tail risk weight, down side risk weight, and total return attribution.
Finally, we consider a total return attribution weighting scheme, wherein each quantile is weighted according to its contribution to the portfolio’s total return. Specifically, using the equally spaced 100 quantile grid (i.e. percentiles), we compute the midpoint between each grid point to signify the average return in that return bin. We then compute the relative frequency of return observations that fall within that bin. Notice that the average return in each bin times the relative frequency of observations occurring within that bin is approximately equal to the total return. To compute the attribution of any given bin, we take the average bin return times relative frequency and divide by the total portfolio return.By design these attributions sum to 1, and thus are viable choices for quantile weights .
In Figure 4 we illustrate how the QuTER objective function varies with the four aforementioned weighting schemes. Specifically, we repeat the exercise from Section 3.1 by simulating the tracking portfolio and benchmark. Each case varies one of the first four moments of the return distribution for the tracking portfolio. The height of each bar is the associated QuTER averaged over 10,000 paths. The number above each bar is the gross change of that average QuTER statistic relative to Case 0. For instance, the 1.1 above the first bar in Case 1 implies that the QuTER value for the equal weight scheme in Case 1 is 1.1 times as large as the equal weighting scheme QuTER statistic for Case 0. The legend can be read as follows: EW = Equal Weight, TR = Total Return Attribution, Tail = Tail Risk, and Down = Downside Risk.
Within Case 1, we find that all of the weighting schemes are roughly equally (in)sensitive to excess mean returns. Gross changes are 1.1 for equal weighting, tail risk weighting, total return attribution, and for downside risk weighting. Within Case 2, total return and tail risk are again equally sensitive to variations in excess standard deviation, while downside is slightly more sensitive and equal weight is slightly less sensitive. For excess skewness, we find that tail risk is the most sensitive, downside is the least sensitive, while equal weight and total return have similar sensitivities. For excess kurtosis, equal weight and total return attribution are again similarly sensitive, with tail risk and downside risk being less so. In summary, a quantile weighting scheme of equal weight or total return attribution is robust to a wide array of differences in the underlying return distributions of the benchmark and tracking portfolio.
In this section we conduct two small case studies in order to illustrate the behavior of QuTE alongside a traditional measure of tracking error. The first case regards tracking the DJIA, while the second focuses on tracking the MSCI Emerging Markets index. We apply the QuTER and TER measures in both an unconditional and conditional setting.
Tracking the DJIA
In our first case study we use the Dow Jones Industrial Average (DJIA) as a benchmark and the DIA SPDR ETF as a tracking portfolio. The DJIA is a leading index of equity market returns in the U.S., being launched in May 26, 1896 and with approximately 1,876.70 dollars indexed to it’s performance. The DIA is among the largest of the DJIA ETF tracking portfolios, with an average of 7,102,449 USD in daily volume since the inception date. It is also one of the oldest ETFs to track the DJIA portfolio, with an inception date of January 13, 1998.
Our dataset contains monthly simple returns for both the DJIA (benchmark) and the DIA (tracking portfolio) over the period January 1998 to June 2019. Figure 5 depicts the time variation of the two return series overlayed upon one another. Simple visual inspection suggests they are quite similar. In fact, the correlation between the two return series is 0.99. Table 4 contains basic descriptive statistics such as mean, standard deviation, skewness, and kurtosis, as well as select quantiles of the two series. The last row contains the p-value for tests of equality between these various measures. A standard t-test is used for equal means. A standard F-test is used for equal variances. A two-way Kolmogorov-Smirnoff test is used to compare all of the four moments jointly. Finally, to compare the quantiles we use employ the Wilcox et al  test with a quantile estimator proposed by Harrell and Davis .
Figure 4 complements the comparisons in Table 4 by overlaying histograms of the tracking portfolio and benchmark in Panel A, and presenting a two-way QQ plot in Panel B. In addition, Table 5 presents various measures of (quantile) tracking errors. Note that the TE and QuTE values are not directly comparable given the different scaling of each measure.
Taken together, the above results reveal that the DIA has distributional properties that are remarkably similar to the DJIA, thereby supporting our visual inspection. Each of the moments and quantiles examined are statistically identical across the two portfolios.
Nonetheless, the two series can differ over time that are important to portfolio managers and investors. Figure 7 charts the difference in returns (TE) for each month. Deviations between the two series are particularly visible during the aftermath of the dot-com bubble in 2001 as well as during the Great Recession of 2008-2010. Of particular note is the variability in the TE over time. Figure 5 depicts the time variation in the difference in the first four moments of the tracking portfolio and benchmark. For the benchmark, we compute the mean return over a trailing three year window. We repeat for the tracking portfolio. Then we subtract those two values. That is a single point in Panel A of Figure 8. We then roll each sample forward by one month, recompute the means, and subtract. We continue that process for the rest of the times series, and repeat that exercise for the standard deviation (Panel B), skewness (Panel C), and kurtosis (Panel D).
In a similar fashion we compute the TER and QuTER statistics between the benchmark and tracking portfolio. Panel A of Figure 5 depicts the rolling tracking measures computed over rolling three year windows, while Panel B depicts the month to month percent change in each tracking measure.
The statistical properties of the tracking portfolio differ from that of the benchmark over time. Our findings from Section 3 suggest that the QuTER statistic might be able to detect these differences when the TER cannot. For instance, as you can see from Figure 7, there is a large spike in the TE during 2001, followed by volatility of the TE until 2004. Figure 5 Panel A shows us that the differences in mean returns between DJIA and DIA was small and steady during this episode, while Panel D shows high differences in kurtosis. The TER is steady near 1.05 during this period, while the QuTER rises from 1 to 1.175, then falls back down to 1 by February 2004. These movements in the QuTER reflect its sensitivity to differences in return distributions that were not detected by TER.
Another episode of interest is the Great Recession. The TE swings wildly from 0.70 to 0.86 over the period 2008 to 2009. The mean return differences, as depicted in Panel A of Figure 5, vary between 0.17 and 0.20, and with it TER rises from 0.70 to 0.86. Notice that skewness changed from -0.01 to 0.11 and kurtosis from -0.05 to -0.10 over that period. QuTER captured these movements, by increasing by almost 50 percent over that period, rising from 0.79 to almost 1.20, outpacing the roughly 22% change in TER.
Tracking the MSCI Emerging Markets Index
In our second case study we use the MSCI Emerging Markets Index (MSCI-EM) as a benchmark and the EEM iShares ETF as a tracking portfolio. We focus in on a recent episode that exemplifies the differences between TER and QuTER. Our dataset consists of monthly simple returns over the period January 2013 through November 2019.
The correlation between the two return series is 0.97 during this sample period. As depicted in Figure 10 the empirical distributions are similar. Nonetheless, as depicted in Figure 11, there are differences between the two series. Analogous to Figure 5 in Section 4.1, Figure 5 illustrates the time variation in the differences of the first four empirical moments of the benchmark and tracking portfolio. Panels B, C, and D show stark time variation in the differences of standard deviation, skewness, and kurtosis.
TER is little changed during this period, as seen in Figure 5, ranging from approximately 1 to 1.3. Meanwhile, QuTER is able to detect these variations in the series, ranging between .8 and 1.7. The relative sensitivity of QuTER is even more stark in Panel B of Figure 5.
In this paper we document a shortcoming of traditional tracking error measures. Cast as a quadratic norm of return differences between a tracking portfolio and benchmark, traditional tracking error measures like TEV and TER are focused on only the first two moments of the underlying return distributions. As such, they are inconsistent with the manner with which most portfolio managers are compensated. If the portfolio and benchmark differ in ways other than the mean or variance, traditional measures are insufficient.
As a remedy, we introduce a new class of tracking errors that are based on the differences in the quantiles of the tracking portfolio and benchmark, namely QuTE. Just as there are myriad variants of tracking error, so too are there variants of QuTE (see Section 2 for a complete listing).
We show via simulation that a simple quadratic summary statistic (QuTER) is more sensitive to differences in higher order moments than is its TER counterpart. We also document in two cases studies situations wherein the QuTER statistic is able to detect important differences in tracking portfolios from their benchmarks, which the TER missed.
Our findings are directly relevant for ex-post performance measurement as well as risk evaluation. Differences in higher order moments matter, and quantile based measures of portfolio tracking provide a useful complement to traditional measures.
 Ammann, M. and Tobler, J. (2000). Measurement and decomposition of tracking error variance. Working paper, University of St. Fallen.
 Barro, D. and Canestrelli, E. (2009). Tracking error: a multistage portfolio model. Annals of Operations Research, 165(1):47-66.
 Beasley, J. E., Meade, N., and Chang, T. J. (2003). An evolutionary heuristic for the index tracking problem. European Journal of Operational Research, 148(3):621-643.
 Blitz, D. and Hottinga, J. (2001). Tracking error allocation. The Journal of Portfolio Management, 27.
 Blume, M. and Edelen, R. (2004). S&p 500 indexers, tracking error, and liquidity. The Journal of Portfolio Management, 30:37-46.
 Chincarini, L. and Kim, D. (2006). Quantitative Equity Portfolio Management An Active Approach to Portfolio Construction and Management: An Active Approach to Portfolio Construction and Management. McGraw- Hill.
 Chung, Y. P., Johnson, H., and Schill, M. J. (2006). Asset pricing when returns are non-normal: Famafrench factors versus higher order systematic comoments. The Journal of Business, 79(2):923-940.
 Dorockov, M. (2017). Comparison of etf’s performance related to the tracking error. Journal of International Studies, 10:154-165.
 Follmer, H. and Leukert, P. (1999). Quantile hedging. Finance and Stochastics, 3(3):251-273.
 Franks, E. C. (1992). Targeting excess-of-benchmark returns. The Journal of Portfolio Management, 18(4):6-12.
 Gaivoronski, A. and Pug, G. (2005). Value-at-Risk in Portfolio Optimization: Properties and Computational Approach. Journal of Risk, 7(2):1-31.
 Giovannetti, B. C. (2013). Asset pricing under quantile utility maximization. Review of Financial Economics, 22(4):169 – 179.
 Harrell, F. E. and Davis, C. E. (1982). A new distribution-free quantile estimator. Biometrika, 69(3):635-640.
 Jorion, P. (2004). Portfolio optimization with tracking-error constraints. Financial Analysts Journal, 59.
 Kahneman, D. and Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2):263-291.
 Kritzman, M. P. (1987). Incentive fees: Some problems and some solutions. FinancialAnalysts Journal, 43(1):21-26.
 Ma, L., Tang, Y., and Gomez, J. (2019). Portfolio manager compensation in the U.S. mutual fund industry. The Journal of Finance, 74(2):587-638.
 Mills, T. C. (1995). Modelling skewness and kurtosis in the london stock exchange ftse index return distributions. Journal of the Royal Statistical Society. Series D (The Statistician), 44(3):323-332.
 Pope, P. F. and Yadav, P. K. (1994). Discovering errors in tracking error. The Journal of Portfolio Management, 20(2):27-32.
 Roll, R. (1992). A mean/variance analysis of tracking error. The Journal of Portfolio Management, 18(4):13-22.
 Rostek, M. (2010). Quantile Maximization in Decision Theory*. The Review of Economic Studies, 77(1):339-371.
 Rudolf, M., Wolter, H.-J., and Zimmermann, H. (1999). A linear model for tracking error minimization. Journal of Banking & Finance, 23(1):85 – 103.
 Wilcox, R., Erceg-Hurn, D., Clark, F., and Carlson, M. (2014). Comparing two independent groups via the lower and upper quantiles. Journal of Statistical Computation and Simulation, N/A:9pp.
 Yamai, Y. and Yoshiba, T. (2002). On the validity of value-at-risk: Comparative analyses with expected shortfall. Monetary and Economic Studies, 20(1):57-85.
  documents excess skewness and kurtosis for cross-sectional daily, weekly, monthly, quarterly and semi-annual asset returns.
 See for example, the ESMA https://www.esma.europa.eu/sites/default/files/library/2015/11/2012-832en_guidelines_on_etfs_and_other_ucits_issues.pdf, Morningstar https://media.morningstar.com/uk/MEDIA/Research_Paper/Morningstar_Report_Measuring_Tracking_Efficiency_in_ETFs_February_2013.pdf, and Vanguard https://www.vanguard.com.hk/documents/understanding-td-and-te-en.pdf
 CFA Institute https://www.cfainstitute.org/-/media/documents/support/programs/investment-foundations/19-performance-evaluation.ashx?la=en hash=F7FF3085AAFADE241B73403142AAE0BB1250B311, International Organization of Securities Commissions and European Securities and Markets Authority https://www.iosco.org/library/pubdocs/pdf/IOSCOPD414.pdf
 Each series was simulated within Matlab using the pearsrnd function for a Pearson system of random numbers with moments calibrated to match the mean, standard deviation, skewness, and kurtosis of the monthly return of the Dow Jones Industrial Average over the period 1985 through 2019.
 The measures of absolute and semi tracking error are beyond the scope of this paper
 We also consider excess standard deviation in the range 0.10 to 5, excess skewness in the range -1.4 to 1.4, and excess kurtosis in the range 1 to 7.
 We scale as follows: Tracking Measure Value – min(Tracking Measure Value)/(max(Tracking Measure Value)-min(Tracking Error Value))
 We also consider excess standard deviation in the range 0.10 to 5, excess skewness in the range -1.4 to 1.4, and excess kurtosis in the range 1 to 7.
 We scale as follows: Tracking Measure Value – min(Tracking Measure Value)/(max(Tracking Measure Value)-min(Tracking Error Value))
 More precisely, we divide by the sum of the average bin returns times relative frequencies. Due to the averaging across the bins, this value may not be equal to the actual portfolio return in any given dataset, but will approach that value as the distance between the grid points approach 0.
 Our findings are similar for AQuTE and AAQuTE
 During this time period, the difference in kurtosis reached a high of -0.50.
“Hours after he had boasted on CNBC that the virus was contained in the United States and “it’s pretty close to airtight,” Mr. Kudlow delivered a more ambiguous private message.
Mr. Callanan reported that numerous Trump administration officials — Mr. Kudlow, Secretary of State Mike Pompeo and economists at the Council of Economic Advisers, who had given the presentation at the White House on Feb. 24 — expressed a greater degree of alarm about the coronavirus than the administration was saying publicly.
To many of the investors who received or heard about the memo, it was the first significant sign of skepticism among Trump administration officials about their ability to contain the virus. It also provided a hint of the fallout that was to come, said one major investor who was briefed on it: the upending of daily life for the entire country.
“Short everything,” was the reaction of the investor, using the Wall Street term for betting on the idea that the stock prices of companies would soon fall.“
We write a lot about the metagame at Epsilon Theory, which is ten-dollar word for seeing the forest rather than the trees. The metagame is the game of games. The metagame is the non-myopic repeated play of many individual games. The metagame is the long-term game of life or investing or business success or whatever you are playing a long game for.
This is an epic metagame fail, btw.
Every single bit of Facebook and Twitter’s response to the NY Post hatchet job on Hunter Biden has been a metagame fail of gigantic proportions. Whatever aspirations Rudy Giuliani and his insane clown posse might have had in the planting of this story … whatever dreams of political impact they might have had … well, they’ve been exceeded by a factor of ten through this bonkers effort by the crack Facebook and Twitter comms team to “fact check” the NY Post and “temporarily reduce distribution” as part of their “standard process”. LOL.
But the point of this note isn’t about the metagame fail we’re seeing play out right now in social media companies, but about a different sort of meta and the grifts it inspires: meta information.
When I tweeted about this NY Times article that lays out how White House insiders like Larry Kudlow were saying one thing about Coronavirus fears in public and quite another in private, and how – quelle surprise! – these private conversations were immediately funneled to hedge fund managers like David Tepper, I got a whole series of Twitter replies like this:
The grift here (in the lingo, tipping material non-public information) by Kudlow and his pals is not the statements that Kudlow et al made directly in these private conversations. It’s not the information within those private statements itself.
The material non-public information that Kudlow tipped was the knowledge that what the White House said in public about Covid’s impact on the American economy was not what the White House truly believed about Covid’s impact on the American economy.
The grift was the difference in the private statements and the public statements.
The grift was the information about the information.
THAT is meta information.
What is meta information?
Meta information is the wink.
Of course, meta information is also edge.
Meta information is also a legal source of alpha in public markets, maybe the only legal source of alpha left. Which leads me to the following rather important question:
Do you have to sell your soul … do you have to hobnob with the Larry Kudlows and Peter Navarros of the world and participate in their endless sea of grift and influence peddlingin order to have ANY edge in investing today?
I think there’s another way. God, I hope there’s another way.
The Epsilon Theory narrative research program – where we try to identify the structure of market-moving narratives – is all about discovering the tells of Wall Street without being part of their wink-wink old boy’s club. It’s all about trying to identify novel information ABOUT information by being smarter instead of slimier. Is it as direct and certain as getting a briefing from the White House on what they really think about the world? Nope. But it sure is easier to sleep at night.
Last week I wrote that markets would move from pillar to post up until the election, and this week I thought it might be useful to revisit the origin of that phrase. The expression was originally ‘from post to pillar’, and it referred to the practice of whipping some miscreant on a post and then moving them over to a pillory for display to a jeering crowd. Here’s someone in a pillory. Not sure if he’s been soundly whipped or not.
I suppose I can’t say that this is what “the market” feels like these days, what with the S&P 500 having its best week in months last week, and the QQQ having its best week in forever. But I do think this is what it feels like to have a view on the market or the election these days. Maybe you’ve already been whipped soundly for that view and maybe you haven’t. But anyone with a view has got to be feeling locked in a pillory. Anyone with a view has got to be worried that a whipping is just around the corner.
Three weeks ago, the common knowledge – what everyone knows that everyone knows – was that a Constitutional crisis was inevitable and that more stimulus was impossible. Last week, the common knowledge was that a “blue wave” was inevitable and that not only was more stimulus on the way, but it could easily be MOAR stimulus. This week … I dunno … it feels like we’re recognizing that the entire world is going to hell in a new Covid-wave handbasket. Next week … well, next week I’m expecting the aliens to land. Or for the large hadron collider at CERN to make contact with a parallel universe. Actually, that last bit is not a joke.
What we’re dealing with here in October 2020 is not risk. It’s uncertainty.
Decision-making under risk is something we’re all very practiced at. All of expected-utility theory, all of portfolio theory – ALL of it – is based on decision-making under risk, where probabilities and outcomes are knowable.
Decision-making under uncertainty, on the other hand, is something we have very little practice at (thank goodness!) and even fewer tools and theories. But there is a strategy that works. From the Epsilon Theory note Once in a Lifetime …
The decision-making strategy designed specifically for uncertainty is Minimax Regret.
Minimax Regret was invented (or at least formalized) in 1951 by Leonard “Jimmie” Savage, one of the founding fathers of what we now call behavioral economics. Savage played a critical role, albeit behind the scenes, in the work of three immortals of modern social science. He was John von Neumann’s right-hand man during World War II, a close colleague of Milton Friedman’s (the second half of the Friedman-Savage utility function), and the person who introduced Paul Samuelson to the concept of random walks and stochastic processes in finance (via Louis Bachelier) … not too shabby! Savage died in 1971 at the age of 53, so he’s not nearly as well-known as he should be, but his Foundations of Statistics remains a seminal work for anyone interested in decision-making in general and Bayesian inference in particular.
As the name suggests, the Minimax Regret strategy wants to minimize your maximum regret in any decision process. This is not at all the same thing as minimizing your maximum loss. The concept of regret is a much more powerful and flexible concept than mere loss, because it’s entirely subjective. But that’s exactly what makes the strategy human. That’s exactly what makes the strategy real when the ultimate human chips of living and dying are on the table.
Minimax Regret downplays or eliminates the role that probability distributions play in the decision-making process.
Minimax Regret doesn’t calculate the odds and the expected utilities over multiple rolls of the dice. Minimax Regret says forget the odds … how would you FEEL if you rolled the dice that one time and got snake-eyes?
More technically, Minimax Regret asks how would you feel if you took Action A and Result 1 occurs? What about Result 2? Result 3? What about Action B and Result 4, 5, or 6? Now out of those six potential combinations of action + result, what is the worst possible result “branch” associated with each action “tree”? Whichever action tree holds the worst possible result branch … well, don’t do THAT. Doing anything but THAT (technically, doing the action that gives you the best worst-result branch) is the rational decision choice from a Minimax Regret perspective.
The motto of Minimax Regret is not Know the World … it’s Know Thyself.
Because when faced with an uncertain event, where you only have one roll of the dice on a probabilistic event, that’s all we can know.
We are only given the world once. Usually that’s not a big deal from an investing standpoint, because the possible parallel universes aren’t that far apart in their market consequences. Over the next three weeks (and maybe longer than that!), the fact that we are only given the world once is a very big deal indeed.
My advice over this span … pay less attention to what the world is telling you about the future, and more attention to what your gut is telling you about yourself. Knowing yourself and your maximum regret does NOT necessarily mean playing it safe. I know lots of investors for whom playing it safe IS their maximum regret. What it means is just that – know thyself – and if you’re managing other people’s money – know them, too – and avoid action trees that hold the worst possible result branch given that knowledge.
Just do that, and this will all be over soon enough, even if it feels like being in a pillory right now. Minimize that maximum regret and you’ll live to fight (and invest) another day. No matter what parallel universe we end up in!
ET Live! is our interactive feature that is all about the narratives that infect financial markets, culture and politics. Don’t forget to refresh your browser if your video doesn’t start promptly after 2:00 PM ET.
To receive a free full-text email of The Zeitgeist whenever we publish to the website, please sign up here. You’ll get two or three of these emails every week, and your email will not be shared with anyone. Ever.
Three years earlier , part of a €528m Vatican portfolio “derived from donations” bought structured notes containing CDS as part of a bet that Hertz would not default on its debts by April 2020, the documents show. The company filed for bankruptcy the following month, giving the Holy See a narrow escape on the investment, which paid out in full.
Other investments made by managers for the Secretariat appointed by Cardinal Becciu include financing the 2019 film Rocketman — a biopic of the musician Elton John — according to fund documents seen by the FT.
The Secretariat also bought multiple luxury residential properties in London’s Knightsbridge, and securitisations partly comprising invoices owed by the Italian state to Vatican-controlled hospitals.
The Secretariat’s investment in the London building known as 60 Sloane Avenue was made through a fund in Luxembourg in 2014 in a deal personally authorised by Cardinal Becciu. In June the Vatican’s state news service reported that Holy See prosecutors believe the investment caused “huge losses”.
It looks easy, doesn’t it? Managing a portfolio. Managing a football team.
We all think we could do it, which is why “frustrated money manager” is the core psycho-demographic that supports pretty much all financial media business models. Just like there’s a frustrated GM in all of us, which is why ESPN and sports talk radio exist.
The frustrated money manager is a very different animal than Davey Day Trader Portnoy, who – as best as I can tell – is a showman and impresario (compliments in my book) who uses trading and portfolio “management” to support his brand/media company and tout his direct investments (Penn National Gaming). Same with Jim Cramer.
No, the frustrated money manager is rarely public with his compulsion (or her compulsion, but honestly I think this is almost entirely a y-chromosome thing), unless he’s enjoyed a hot streak and starts bragging to his email buds. Which happens not infrequently in a bull market.
The frustrated money manager is almost always a smart, accomplished professional in his own field who believes VERY much in the existence of The Smart Money ™.
The frustrated money manager is almost always a liiiittttle bit on the make.
Like a Vatican cardinal.
It took me a long time to recognize the frustrated money manager within me, including when I WAS a money manager, and a non-frustrated one at that. And to be clear, I said “recognize”, not “eliminate” or something silly like that. No, we are ALL frustrated money managers. The only question is whether we let that dimension of our psychological makeup ruin our lives, like it did Cardinal Becciu and so many others.
Here’s the knowledge that helps me keep it under control in myself. You ready?
There is no Smart Money.
That’s the big secret. That’s the most important thing I have to say to my fellow DGs and frustrated money managers. Especially if you ARE a money manager. You can’t eliminate the DG and frustrated money manager in you, but you can control it. Internalize this little nugget and you won’t get taken for a ride to the point where you ruin your life. Please.
As you may recall (and it’s in the monitor report if you don’t), the S&P 500 returns are typically positive in the month following an Inflation-focused regime reading (+1.6% on average), and typically negative in the month following a Fundamentals-focused regime reading (-0.9% on average). This may seem like a wash, but in our model portfolio construction we actually give substantially more weight to the negative signal of the Fundamentals-focused regime than the positive signal of the Inflation-focused regime.
Why? Because markets (and narrative impact on markets) happen at the margins.
Narrative signals are most impactful when they indicate a change in regimes. Narrative signals are most impactful when they exist at the edges of the narrative regime spectrum. Narrative signals are not a smooth variable, something to be z-scored, in the econometric lingo. They are a state of the world, which is why we use the term “regime”, and they change in quantum steps.
For Central Bank narratives, then, we’re most confident that we’re reading an actionable market signal when there’s a regime change (like we saw going into May when the narrative state of the world moved from the highly market-positive Fed Put regime to the slightly market-positive Inflation regime in April) or when the narrative state of the world is at the edges of the regime spectrum (Fed Put on the positive end, and Hawkish on the negative end).
Ditto for Securities Analysis Method narratives (the way that market participants talk to each other about how to think about stock prices). We’re most confident that we’re reading an actionable market signal when there’s a regime change (like we saw going into August when the narrative state of the world moved from the highly market-positive Technicals regime to the market-negative Fundamentals regime in July) or when the narrative state of the world is at the edges of the regime spectrum (Technicals on the positive end, and Fundamentals on the negative end).
So if you ask what our macro narrative signals are telling us as we go into October, they’re moderately bearish. We don’t have negative signals from both narrative monitors, but we do from the SAM monitor.
And then we had the first week of October. LOL. When I say LOL, I don’t mean that I think our macro narrative signals are wrong. I mean that I don’t think they matter very much.
From last Thursday night until 2:49 pm today, the only thing that mattered to markets was the news flow of a White House in free fall (so odds of a decisive Biden victory went way up with maybe a Senate switch, too) plus the news flow of an on-again multi-trillion dollar stimulus package, the combination of which led to the long end of the yield curve spiking ferociously (10-yr UST backing up from 0.66% to 0.78%) and an even more ferocious “Buy Cyclicals!” market narrative.
And then at 2:49 pm today, with a Trump tweet that the stimulus negotiations were kaput, that narrative and that trade collapsed, with the S&P 500 falling 2% in 15 minutes.
And then at 10 pm tonight, with another tweet that maybe stimulus negotiations could start back up again, futures are back to flat.
I understand the “Buy Cyclicals!” market narrative. It’s an instantiation of the Fourth Horseman / Inflation Cometh I’ve been writing about since October 2018 (“Things Fall Apart (Part 3) – Markets”). If there’s no big issue with a transition of Presidential power … if there’s a an unfettered path for the Democrats to unleash the mother of all fiscal stimulus packages with their MMT theology … well then, we’re off to the cyclical/inflation races! Until they raise taxes, of course.
I also understand the “Sell Cyclicals!” market narrative. If there’s no fiscal stimulus coming … if the Fed is pushing on the string of a string … if the outcome of the election (in whichever direction ) is contested and sparks the mother of all Constitutional crises … well then, we’re off to the defensive/deflation races! Until there’s a massive fiscal stimulus after all, of course.
So … I have no idea what’s going to happen next. These are essentially mutually exclusive paths. I mean, I’m sure there’s some muddle-through scenario here, and that’s probably what will actually transpire in November. But markets (and narrative impact on markets) happen on the margins. This market is going to continue to swing from pillar to post until that muddle-through event actually happens, and there is no hedge for that. Or rather, the only hedge is the best hedge – you take down gross exposure, not just net exposure. You reduce your market risk. You minimize your maximum regret.
Wall Street, like Hollywood, is geared to sell happy endings. Ultimately I think we’ll have, if not a happy ending for markets when this election is all said and done, then at least a happy-ish ending. But until THAT narrative takes shape, then it’s a narrative and market rollercoaster, where every marginal outcome is just a tweet or a Covid diagnosis away. No one has an edge in this environment, and anyone who says they do is kidding you or kidding themselves.
Stay safe! And when the smoke clears, let’s get to work.
So what you wanted to see good
Has made you blind
And what you wanted to be yours
Has made it mine
'Cause I fell on black days
I fell on black days
I sure don't mind a change
-- "Fell on Black Days" (1994)
I was driving the other week and switched the radio station over to Lithium, the grunge rock channel on Sirius, and the info panel showed that I should expect a Soundgarden song called “Fell on Black Days”. As I recalled (and the title implies), this song has the depressing lyrics of pretty much all Soundgarden/Chris Cornell songs, and I remember thinking that this would be a pretty good theme song for the year. Covid and the election and protests and urban violence and massive wildfires and brain-eating amoebas in south Texas drinking water … these sure feel like Black Days that we’ve fallen into. 2020, amirite?
But before playing the track, there was a brief recorded interview with Chris Cornell from some years ago (he committed suicide in 2017) explaining what he was thinking when he wrote “Fell on Black Days” back in 1994. It wasn’t what I expected.
He said that the song is about waking up one morning and realizing with a start that your life and the world in general are … off. Not just a little off, but way, way waaaay off. And not because of some huge traumatic event. Not because you got really sick or you got fired or a meteor hit the Earth, but because of a thousand little events, each regrettable and yet oddly unremarkable in itself, each building on the others, each lost in the noise of the others, each noted with a sigh and then promptly forgotten. It’s the realization that you are the frog now sitting in boiling water, that you are the one suffering a death by a thousand cuts. It’s the realization that you’re not as happy as you used to be, that you’re not as secure as you used to be, that you’re not as healthy as you used to be. And it all just … happened. It all just snuck up on you unawares, like you were asleep or something.
I know exactly what Chris Cornell is saying in this song. I bet you do, too.
“Fell on Black Days” isn’t a theme song for 2020.
It’s a theme song for all the years that got us to 2020.
Are you awake yet?
This is a picture of Chris Cornell with his daughter Toni. Looks like she’s what? Four years old? That would date this to 2008, fourteen years after he wrote “Fell on Black Days”.
Chris Cornell and I would be almost exactly the same age if he were still alive. We were both born in the summer of 1964.
I’ve got a picture just like this. I’ve got a lot of pictures just like this. If you’ve got kids, I bet you do, too.
Have you woken up to the realization that our nation and our world and your children’s place in that nation and that world are less secure and less healthy and less happy than a year ago? And that next year their place will be less secure and less healthy and less happy than this year?
I have no frame of reference for the depression that led Chris Cornell to take his own life nine years after this photograph was taken. But I know exactly the feeling of love and pride and hope in this photograph, just like I know exactly the feeling of anger and pain and realization in “Fell on Black Days”. These are the feelings that motivate me in everything I do.
The threat of the future revealed itself to me in 1996 with the death of my father and the birth of my child. One day the threat of the future will reveal itself to you, if it hasn’t already. When it does, you will be CONSUMED by thoughts of the future. You will FEEL the pressure of time more keenly than the younger you could ever imagine.
Blowing up our international trade and security games with Europe, Japan, and China for the sheer hell of it, turning them into full-blown Competition Games … that’s really stupid. But we have a nasty recession and maybe a nasty war. Maybe it would have happened anyway. We get over it. Blowing up our American political game with citizens, institutions, and identities for the sheer hell of it, turning it into a full-blown Competition Game … that’s a historic tragedy. We don’t get over that.
I don’t think people realize the underlying fragility of the Constitution — the written rules to our American political game. It’s just a piece of paper. Its only strength in theory is our communal determination to infuse it with meaning through our embrace of not only its explicit rules, but also and more crucially its unwritten rules of small-l liberal values like tolerance, liberty, and equality under the law. Its only strength in practice is that whoever runs our Executive branch, whoever is our Commander-in-Chief, whoever is in charge of “law and order”, whoever runs our massive spy bureaucracy national intelligence service, whoever controls the legitimate use of deadly force and incarceration … that he or she believes in those unwritten rules of small-l liberal values like tolerance, liberty, and equality under the law. When you hear Trump talk about “loosening the law” on torture, or “loosening the law” on libel prosecutions of anyone who criticizes HIM, or the impossibility of a federal judge being able to rule fairly because his parents were born in Mexico … well, there’s no way he believes in those small-l liberal virtues. No way.
And yeah, I know what the supporters say, that he “really doesn’t mean what he says”, or that “once he’s elected he’ll listen to the right people and his views will evolve”, or — my personal fave — “it’s only 4 years, how bad can it be?” Answer: pretty damn bad. And yeah, I understand the argument on the Supreme Court. But what I’m talking about is bigger than the Supreme Court. A lot bigger.
Now we recognize the scale and scope of what has been stolen from us over the past 40 years, a scale and scope that dwarfs the grifts and Il Duce cosplay of Donald Trump. Now we understand that our vote every four years is the merest, most insignificant part of our political participation.
We don’t play defense. We don’t content ourselves with avoiding the worst excesses of the Trumpist clownshow or the Socialist lunacies.
Now we change the entire freakin’ world. For ourselves, yes. For our children, even more.
“He’s dreaming now,” said Tweedledee, “and what do you think he’s dreaming about?”
Alice said, “Nobody can guess that.”
“Why, about you!” Tweedledee exclaimed, clapping his hands triumphantly. “And if he left off dreaming about you, where do you suppose you’d be?”
“Where I am now, of course,” said Alice.
“Not you!” Tweedledee retorted contemptuously. “You’d be nowhere. Why, you’re only a sort of thing in his dream!”
“If that there King was to wake,” added Tweedledum, “you’d go out — bang! — just like a candle!”
– Lewis Carroll, “Through the Looking Glass” (1871)
We’re all familiar with the Queen of Hearts from Alice in Wonderland, less so with the Red King. He’s sleeping all the while, and when Alice goes to wake him up she’s warned off by Tweedledee and Tweedledum, who tell her that everything in Wonderland – including Alice herself – is perhaps just the dream of the Red King. Wake him up and maybe, just maybe, everything goes … poof!
The Red King is us.
Everything changes when we wake up from our dreaming world, when we no longer allow concentrated interests of wealth and power to nudge us back to sleep with their memes and soma.
It’s time to look beyond the November 3rd election, not because it doesn’t matter or it’s not worthy of your awake-for-the-first-time political participation, but because your awake-for-the-first-time political participation in the days and weeks and months and years and decades after November 3rd matters MORE.
I think the events of 2020 have woken the Red King … us! … and we have a once in a lifetime opportunity to unmake the Black Days that were created around us while we slept, a once in a lifetime opportunity to realize our dreams of old, now long deferred.
Our dreams – and our pledge – of liberty and justice for ALL.
What happens to a dream deferred?Does it dry up like a raisin in the sun? Or fester like a sore — And then run? Does it stink like rotten meat? Or crust and sugar over — like a syrupy sweet?Maybe it just sags
like a heavy load.Or does it explode?
– Langston Hughes, “Dream Deferred” (1951)
Mark me down for explode.
We need quantum change – meaning we must have change in the rules of the system, meaning that we must have change in the state of the system – because once you fall into the stable equilibrium of our Black Days, it is impossible for incremental change or adjustment to get you out. Not just difficult. Impossible. That’s what an equilibrium means. We cannot just open a door that has been welded shut. We must blow the door open.
We must Burn. It. The. Fuck. Down.
Which doors? All of ’em. All of the welded shut doors of the institutions that steal our autonomy of mind, that use us for fodder and feed. What are those institutions? Literally every single institution of human civilization.
Hey, go big or go home.
These are the ten Great Guilds of human civilization, each now fully captured by smiley-face authoritarian concentrations of wealth and power, even as the rank-and-file members of these guilds dream a pleasant dream of days gone by.
The Artists Guild— the human endeavor of entertainment, art and fashion; not only “content” (to use the modern term) but also design, marketing and sport.
The Bankers Guild— the human endeavor of money as a thing; commercial and investment banks, yes, but also all financial services.
The Doctors Guild— the human endeavor of health; not only doctors and hospitals, but also all medical services, medical devices, healthcare payers and pharmaceuticals.
The Lawyers Guild — the human endeavor of law as a thing; lawyers and law firms, yes, but also all law-making and law-execution and law-deciding.
The Masons Guild — the human endeavor of construction; the building of structures and infrastructure, including telecom/network infrastructure.
The Miners Guild— the human endeavor of natural resource extraction, for my purposes including renewable resources, agricultural resources, and constructed resources like semiconductors.
The Mercenaries Guild — the human endeavor of organized protection and the legal use of force, including soldiers, police and “security contractors”.
The Merchants Guild — the human endeavor of business as a thing; in the modern context, all of professional corporate management.
The Teachers Guild— the human endeavor of knowledge as a thing; not only education but also scientific, technical and engineering research.
The Thieves Guild — the human endeavor of organized crime and the illegal use of force; yes, this is one of the pillars of human civilization.
How did this happen? How were the Great Guilds of human civilization captured while we slept?
Through the systematic use of securitization, leverage, scale and alienation.
Securitization— the derivative connection of something in the real world with a piece of paper that can be bought and sold separately from that real world thing, with no impact on that real world thing; also known as a casino chip.
Leverage— borrowed money.
Scale — increased size generating a more than proportional increase in power.
Alienation — the process that transforms a human from making a cog to being a cog … and liking it.
These are the instruments of our Black Days. Sometimes used in unison, sometimes used separately, these are are TOOLS by by which smiley-face authoritarian concentrations of wealth and power have perverted all of our human endeavors. Application of securitization, leverage, scale and alienation is the PROCESS by which our Black Days were created.
Understanding the process is everything.
Because if I’m right about the process … then we have a blueprint for how to reverse it.
How do we fix the world?
By burning away the overwhelming levels of securitization, leverage, scale and alienation built up in every aspect of human civilization.
You may know these words by another name.
Leverage + Securitization= Financialization
Scale + Alienation= Neoliberalism
You know, in one of my Twitter spats with Angry Billionaire™ Cliff Asness, he proclaimed that the word “financialization” was not used by any serious person. By this he meant (I think) that it was a vague, mushy term tossed around for affect by people who had some inchoate beef with capitalism or wealth inequality or the like. A word full of sound and fury, signifying nothing.
Cliff was right.
Almost always, “financialization” is a word bandied about for emotional appeal. Almost always, it’s a verbal form of jazz hands, shorthand for “there’s something here that seems unfair or unjust to our woke sensibilities, so let’s all just agree that it’s bad by nodding our heads at the term”. At best, financialization is understood as Justice Potter Stewart understood pornography … we can’t define it, but we know it when we see it. Ditto with “neoliberalism”. Frankly, I think “neoliberalism” is even worse.
It’s incumbent on those of us who believe something is fundamentally and structurally WRONG with the way power and wealth are distributed in the modern world to choose our words with precision and care. I don’t mean that we have to be boring or pedantic. I mean that the burden of proof is on us to show that the current system is, in fact, structurally broken, and the best way to do that is to use our words like a scalpel, slicing away the skin of pleasant narrative and deceit to reveal the sinews of raw power beneath.
What is financialization? It’s the application of securitization and leverage.
What is neoliberalism? It’s the application of scale and alienation.
HOW are they applied to the Great Guilds of human civilization to make our Black Days?
HOW do they strip away our life, our liberty, and our pursuit of happiness?
HOW can we reverse this?
Usually I’d write a series of notes to answer these questions and post them on the Epsilon Theory website. In this case that seems … small. It seems like a bad move in the metagame! Why? Because the words I’m writing are threatening words to these concentrated interests of wealth and power and their Renfields. Because the words I’m writing can and will be used against me and anyone who chooses to act on those words with me. They will intentionally be taken out of context. They will be intentionally be misconstrued. First to scoff and dismiss out of hand. Then to attack.
To win this game, I need to write a canonical, precise, single-source resource that can be distributed in multiple modalities through multiple distribution channels to as many people as possible, insulated through its form against misinterpretation and signal jamming.
I need to write a book.
Fell on Black Days: How Financialization and Neoliberalism Broke Our World, And How We Can Fix It
You can help me if you like.
If you want to tweet at my publisher, @harrimanhouse, and tell them how excited you are about this project, that would actually be a big help.
If you want to join the Epsilon Theory Pack and support this effort directly with your subscription (yes, annual subscribers will get a free copy of this book), that would be an even bigger help.
And if you want to email me at [email protected] with an example or story of how securitization, leverage, scale and alienation has impacted your guild … well, that would be the biggest help of all! The Pack is in this together, and it’s time to howl as one.
To receive a free full-text email of The Zeitgeist whenever we publish to the website, please sign up here. You’ll get two or three of these emails every week, and your email will not be shared with anyone. Ever.
It’s been a bad few years for investors in shale companies, but a pretty good few years for shale company CEOs.
The leaders of U.S. shale companies received some of the largest executive pay increases in corporate America, even as their shareholders lost billions of dollars, a Wall Street Journal analysis has found.
There’s something about Wall Street Journal headshots that make you look guilty.
Maybe it’s the black-and-white, maybe it’s the uncanny valley stippling, but whatever it is, I have no doubt that this is why the Wall Street Journal editors used this two-by-two composite of headshots as the social media image for their broadside against shale company CEO compensation .
Similarly when you dig into the article, the words are just filler for the four individual headshots, together with their cash comp and stock price performance data for the five year period 2015 – 2019.
I mean, you don’t even need to read the article to get your blood pressure up. Tens of millions of dollars every year to each of these guys. Hey, I could do their job for a fraction of that money! Clearly these guys are guilty of … something. But guilty of what?
In the eyes of the Wall Street Journal, the mortal sin committed by these CEOs – all of whom are professional managers, not founders or entrepreneurs – is NOT that their professional managerial compensation is ridiculous and extreme. No, the mortal sin is that it’s off-narrative, that there’s no pleasant veneer of positive “total shareholder return” to justify their professional managerial compensation. We are told that these four CEOs are over-compensated because the stock price is down, not that they are over-compensated, period.
These CEOs violated the “Yay, shareholder alignment!”narrative, and THAT is why they are singled out and hung out to dry by the Wall Street Journal.
This article is not an attack on that system. It is a defense. It is telling you that the system is fine … we just need to do something about these bad apple CEOs who do not properly “align” their compensation with shareholders.
One day we will recognize the defining Zeitgeist of the post-GFC Obama/Trump years for what it is: an unparalleled transfer of wealth to the managerial class.
In the Long Now, we have argued, the single most important executive skill is not talent identification and development. It is not strategic vision. It is not logistical or subject matter expertise. It is not organizational design and process management. You know, all the things B-School management professors who have never actually, y’know, managed anything have been teaching for decades.
In the Long Now, the single most important executive skill is the ability to shape the external narrative of the company.
That is a shame for all of us. This exchange robs our collective future of the manifold promises of productivity, ingenuity and growth that come from investing in that first group of things. Instead it offers us a mess of pottage that is short-term stock price appreciation. For airline executives, on the other hand, it is a damn good thing. For if the zeitgeist is any indication, they have succeeded beyond their wildest imaginations at shaping the external narratives of their companies embraced by the national media.
Indeed, the news that we reviewed as part of our Zeitgeist feature for the last few days has been littered with the evidence of lazy reporting, with business journalists who saw the flash of the shiny lure of the narrative spun by airline company executives. When they saw executives framing the accountable party in the layoffs of tens of thousands of employees as the US Congress, they not only reported it.
CNN put forth an honest effort to ingest the rod and reel for good measure, penning a piece that might have caused Doug Parker to call his public relations vendor to ask why their own statements weren’t as powerful a promotion of the story they wanted to tell.
But he probably didn’t make that call. After all, that same P.R. agency is probably who managed to get the same publication to publish this opinion piece the day before.
Breitbart was able to integrate not only a political angle (“after Pelosi fails” is a nice touch) but a nuanced argument about the incorrect assumptions underlying the original support, quite a trick when you consider that they had half of Doug Parker’s tackle box stuffed down their gullet.
But it was the Washington Examiner which delivered the piece de resistance of on-narrative reporting, going so far as to summon an “independent aviation analyst” quote given to NBC News arguing that no-strings-attached financial support was our only choice if we didn’t want to lose the operational capacity of our airlines when things were better again.
This, of course, is the abstraction that forms the core of the narrative. It isn’t that the American people don’t have a public interest in ensuring the continuity of a very skilled subset of the US labor force. Of course we do. It isn’t that the American people don’t have a public interest in maintaining multiple competing air carriers serving our geographically sprawling country. Of course we do.
It’s that Doug Parker is telling all of us – citizens and media alike – how to think about what an airline is. Doug Parker wants you to think that “American Airlines” is the financial health of AAL, the publicly listed company with its current debt holders, current equity owners, and current programs to programmatically offer cash and non-cash compensation to senior executives. He wants all of us to think that those things are synonymous with having functional, well-maintained airplanes, protected employees and route infrastructure capable of quickly ramping back up when the depression in air travel caused by COVID-19 subsides.
And we’re buying it – hook, line and sinker.
We don’t have to. As citizens, we can carry two ideas in our heads at once. We can believe that airlines are a critical industry, that its workers are important fellow citizens worthy of public financial support and that keeping them in the industry is an indispensable part of rapidly returning to full capacity. AND we can believe that literally none of that requires us to unconditionally support the share price, current equity holders or executive compensation expectations at AAL or UAL or any other airline.
How do we do BOTH? We separate the operating entity from the ownership entity in our heads, we offer financial support for the operating entities we depend on, and we do it with these conditions, taken from a piece we published all the way back in March.
First, impose regulated caps and clawbacks on ALL senior management compensation, including stock-based compensation, for the next decade, regardless of how quickly any loan support is repaid. If these guys aren’t willing to work for $1 million or $2 million dollars per year in total comp, I’m sure we can find a perfectly good replacement CEO who will.
Second, the current board Chair for each airline should be summarily dismissed and replaced by an independent director appointed by the government. This is also a 10-year right that the government maintains, regardless of how quickly any loans are repaid.
Third, require each airline to raise new equity capital in the open market dollar-for-dollar to whatever low-interest loan facility is backstopped or made available directly by the US government. In other words, if Delta wants access to $10 billion in loans, they must raise $10 billion in new equity at whatever price the market demands to clear the equity raise. We require banks to maintain a certain level of equity capital, because we’ve judged them to be too strategically important to fail. Let’s do the same for the airlines.
Fourth, until the loan facility is repaid in full, no stock buybacks and no dividends. Duh.
This kind of bait-taking has become so prevalent in large part because financial and business media have restructured their business models to be cheerleaders for common knowledge missionaries in corporate executive positions. They drive ratings by creating stories instead of reporting them. If we are going to reclaim financial markets as a venue in which capital is channeled to its most productive ends by free participants who price such capital based on a good faith, fundamental evaluation of those ends, an independent, skeptical financial media that doesn’t buy every transparent corporate narrative hook, line and sinker is a necessary condition.