The Zeitgeist – May 14, 2021

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Here’s what we’re reading and working on this week at Epsilon Theory.

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Every item in this email is a discussion thread on the Epsilon Theory Forum – a safe space to speak your mind, a safe space to find like-minded truth-seekers. Watch from a distance if you like, but when you’re ready … join us.



Ransom Paid

From a Bloomberg article on Thursday:

Colonial Pipeline Paid Hackers Nearly $5 Million in Ransom

“Colonial Pipeline Co. paid nearly $5 million to Eastern European hackers on Friday, contradicting reports earlier this week that the company had no intention of paying an extortion fee to help restore the country’s largest fuel pipeline, according to two people familiar with the transaction.”

“The company paid the hefty ransom in difficult-to-trace cryptocurrency within hours after the attack, underscoring the immense pressure faced by the Georgia-based operator to get gasoline and jet fuel flowing again to major cities along the Eastern Seaboard, those people said. A third person familiar with the situation said U.S. government officials are aware that Colonial made the payment.”

Once they received the payment, the hackers provided the operator with a decrypting tool to restore its disabled computer network. The tool was so slow that the company continued using its own backups to help restore the system, one of the people familiar with the company’s efforts said.

I love how Colonial thinks it’s important for you to know that they were very dissatisfied with Darkside customer service. The decryption tool was so slow! LOL.

I think there’s zero coincidence that you also saw this Bloomberg article on Thursday:

Binance Faces Probe by U.S. Money-Laundering and Tax Sleuths

“The officials involved include prosecutors within the Justice Department’s bank integrity unit, which probes complex cases targeting financial firms, and investigators from the U.S. Attorney’s Office in Seattle. The scrutiny by IRS agents goes back months, with their questions signaling that they’re reviewing both the conduct of Binance’s customers and its employees, another person said.”

“The U.S. Commodity Futures Trading Commission has also been investigating Binance over whether it permitted Americans to make illegal trades, Bloomberg reported in March. In that case, authorities have been examining whether Binance let investors buy derivatives that are linked to digital tokens. U.S. residents are barred from purchasing such products unless the firms offering them are registered with the CFTC.”

The tax sleuths are on the case! More from that Bloomberg article:

“In the U.S., authorities have been cracking down on exchanges for flouting laws that are meant to prevent financial crimes, with officials citing the platforms use by terrorists and hackers. Tax violations have also been a priority, with the government recently winning a court order as it seeks to unmask U.S. clients of Kraken, a San Francisco-based exchange.”

It’s so weird that a Kraken affiliate was so vocal on Twitter saying that my In Praise of Bitcoin article was nonsense. Still more:

“In October, federal prosecutors in Manhattan announced charges against the founders of Seychelles-based BitMEX, accusing them of violating the Bank Secrecy Act by permitting thousands of U.S. customers to trade while publicly claiming to restrict their access. The claims included failing to register as a futures merchant with the CFTC and not having adequate anti-money laundering controls. Three of the BitMex officials pleaded not guilty and a trial has been scheduled for March 2022. One remains at large.”

I missed this Arthur Hayes news! From April 6:

Former BitMEX CEO Arthur Hayes Surrenders to Face Charges

“A Singapore resident, Hayes on Tuesday surrendered to U.S. authorities in Hawaii, six months after federal prosecutors in New York accused him and his BitMEX co-founders of conspiring to skirt U.S. laws requiring the implementation of money-laundering controls. He appeared before a federal judge in Honolulu and, pursuant to an earlier agreement, was released on $10 million bond pending future court proceedings in New York.”

Arthur Hayes is the most fascinating criminal defendant since Martin Shkreli, and I think (hope?) his trial next March will live up to its amazeballs potential. Hayes writes a really smart blog, btw, and his latest blog post – “Fear Is The Mindkiller” – came out yesterday. It’s a great read! Honestly, I think everything Hayes writes in this post is spot-on, with gems like this:

“Most new entrants to our pond want an easy way to acquire Bitcoin vs. fiat risk. That is, they believe Bitcoin Number Go Up, but have zero interest in becoming their own financial institution. They want the 1-800 number to call when they forget their password, and a human to complain to when things don’t go as planned. Service providers are happy to sell paper Bitcoin derivatives that provide exposure to the asset, while dealing with all the pesky blockchain issues for a fee.”

Judging by the asset gathering success of Grayscale’s GBTC, Coinshare’s XBT Provider, and other paper derivatives, the average investor just wants price risk.

Bingo!

Or like this:

“Crypto has nothing to fear from Doge. It instead should be used as a foil to show the emperor has no clothes. Money is a mental abstraction. The sooner gen pop realizes that everything is make-believe, the sooner they can make the leap from physical government issued bank notes, to a purely digital decentralized currency.”

They are both equally as fake.

It’s all true, as Hemingway said. It’s all fake, as Hayes says. Both statements are correct!

Because that is the definition of good art: a fake that tells the truth.

And crypto is such good art.

Man, I can’t wait to read what Hayes writes next.

Unfortunately for him, if Sauron has anything to say about it, he’ll have a lot of free time on his hands for writing over the next few years. Just maybe not a lot of access to his blog.



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Wage Inflation Isn’t Coming. It’s Already Here.

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George Soros has a great line that we’ve used a lot in Epsilon Theory notes. When he was asked how he could possibly have predicted what would happen when he famously “broke the Bank of England” in 1992, he replied in that growly Hungarian voice: I’m not predicting. I’m observing.

It’s the perfect catchphrase for the modern, narrative-savvy investor. It’s the perfect catchphrase for the Three-Body Problem market, where there is no algorithm for predicting markets (no closed-form solution, in the lingo), but only tools for calculating markets. Where there is no Answer for successful investing. But there is a Process.

I’m not predicting. I’m observing.

The Three-Body Problem

It’s a hard concept to wrap your head around, this difference between calculating the future and predicting the future, but it will change the way you see the world.

And your place in it.  

Here’s what I am observing:

Over the past four quarters, the United States has generated more wage inflation than at any point over the past 40 years.

Seeing is believing, so here’s the chart of average weekly earnings (weekly earnings, not hourly!) for Americans in private sector jobs from 1963 through today, measured quarterly.


Bloomberg: US Average Weekly Earnings SA, Quarterly Jan 1, 1963 – Mar 31, 2021

And here’s the same data on a monthly basis over the past 14 years:


Bloomberg: US Average Weekly Earnings SA, Monthly Jan, 2007 – April, 2021

These are the facts. These are not predictions. This is what has already occurred.

  • Q1 2021 wages were 7.7% higher than Q1 2020 wages.
  • Q4 2020 wages were 7.7% higher than Q4 2019 wages.
  • Q3 2020 wages were 6.2% higher than Q3 2019 wages.
  • Q2 2020 wages were 6.5% higher than Q2 2019 wages.

These are also facts. I am not making this up.

  • Over the past 10 years prior to the past 4 quarters, the highest single quarterly year-over-year wage growth was 3.6% in Q4 2018.
  • Over the past 20 years prior to the past 4 quarters, the highest single quarterly year-over-year wage growth was 4.5% in Q4 2006.
  • Over the past 30 years prior to the past 4 quarters, the highest quarterly year-over-year wage growth was 4.8% in Q4 1997.

You have to go back 40 years – to Q3 1981 – to find a higher quarterly year-over-year wage growth number (+8.5%).

This is not an anomaly. This is not a single quarter aberration. This is not transitory.

This is four straight quarters of the highest wage growth numbers in 40 years.

For those keeping score at home, the US inflation rate in 1981 was 10.3%.

Now I know what you’re thinking. You don’t believe me. Surely, you say, if this were true we would have heard some mention of this not-in-forty-years wage growth phenomenon. Surely, you say, someone involved in the creating or proselytizing or questioning the Fed’s dominant “transitory inflation” narrative would have mentioned this little nugget. I mean, it seems … relevant.

I think I know why you’ve heard nothing about this. Also, don’t call me Shirley.

The reason no one recognizes that remarkable wage inflation has already occurred is largely because of the intentional cartoonification of unemployment and wage data.

I’m using the word ‘cartoon’ in its technical sense here, as an abstraction of an abstraction. I’ve written about these cartoons of macroeconomic data in service to political ends quite a bit (in fact, earlier this week we published a nice short note on the cartoon that is CPI), but here’s the note that discusses the cartoon of average hourly wage earnings in great detail:

The Icarus Moment

We live in a Cartoon Age, an era not of alienation per Karl Marx, but of alienation per Groucho Marx.

What’s the cause, what’s the future, and what do we do about all this? 

And here’s an extended money quote from that note:

In the beginning, there was a desire to model the employment patterns of the U.S. economy to help policymakers figure out what was actually going on. So in 1884 (!) Congress established the Bureau of Labor Statistics (BLS) to do some counting and abstracting, and since 1915 (!) the BLS has been surveying employers to estimate how many Americans are working and how much they’re being paid. On the first Friday of every month, the BLS releases its report on the real-world employment patterns in the U.S. for the prior month. This data is an abstraction, to be sure, full of seasonal adjustments and model estimations, but it is a first level abstraction. This is not the cartoon.

One of the standard calculations that the BLS reports is the percentage change on a year-over-year basis in how much workers are being paid. Usually this wage growth report takes a backseat to the more famous “jobs report” of how many jobs were added or subtracted from the U.S. economy in the prior month and the even more famous “unemployment report” (which is actually based on an entirely different survey) of the percentage of Americans who were actively looking for work but were unable to find jobs. But when everyone and his cousin is either worried about wage inflation or hoping for wage increases, then the wage growth “number” takes on enormous importance. It’s the depiction and the narrative around the BLS wage growth calculation that is the cartoon. And that cartoon is everything for markets today.

The most basic way to look at wages for a monthly report would be to count up how much all workers got paid in that prior month. But that doesn’t work for a month-to-month comparison because different months have meaningfully different numbers of days. Unless you’re getting paid on a monthly or twice-monthly basis, then you’re going to be making less in February than you are in January. So the BLS uses the work week as their basic apples-to-apples comparison basis.

As far back as I can trace the theater of BLS reports — and that’s how one should think about these market data reports, as theatrical productions consciously designed to impact behavior — the “number” that’s reported isn’t the apples-to-apples comparison of weekly wages. Instead, it’s hourly wages. Why? Because back in 1915 this is how most people got paid. The abstracted idea of hourly wages connects with people more than the abstracted idea of weekly wages. It’s a more effective tool for eliciting a behavioral response, so that’s why our theatrical effort focuses on it every month.

But here’s the problem with the hourly wage abstraction. It requires introducing a new data estimation into the mix, one that has nothing (or at least very little) to do with the real-world concept we’re trying to represent, which is whether you’re taking home more money today than you did last year. That additional layer of abstraction is the average length of the work week.

Now this data estimation changes very little from month to month. Unlike the difference in work days from month to month, which can be meaningful and is incredibly easy to measure, the difference in work hours from week to week is an immaterial and almost certainly statistically spurious estimation. Here are the average number of hours in the work week since 2012.

Since 2012, the average length of the work week has been as low as 34.3 hours and as high as 34.6 hours. For more than SIX YEARS, the maximum deviation from the mean has been less than NINE MINUTES, less than ONE-HALF OF ONE PERCENT of the total work week. This is the flattest line you will ever see in any time series, and any month-to-month deviation from the mean is almost certainly a spurious statistical estimation. Meaning that the month-to-month differences in the average work week are so far inside your margin of error for this sampling and estimation process that you can have ZERO confidence that you are abstracting anything real. This is as bogus of an abstraction as you will ever see.

And yet it makes all the difference in the world for hourly wage calculations!

Why was the February wage growth number reported on March 9th as 2.6% rather than 2.9%?

Because the average work week in February 2018 was randomly estimated as being six minutes longer than it was a year ago.

Everything you read about what the March 9th wage growth number meant for your portfolio — the entire Goldilocks narrative of a “contained” wage inflation number combined with strong job growth — is based on a statistically spurious result. Everything. It’s all made up. None of it is real.

And yet, on the basis of the Goldilocks narrative, which was the all-day headline of the Wall Street Journal and the talking point of every Missionary on CNBC that Friday, the S&P 500 was up more than 1.7% on the day. That’s $415 BILLION of market wealth created in the S&P 500 alone, in one day, from a cartoon representation of annualized wage growth in the U.S. economy.

I wrote that in 2018. Here’s a chart of what’s happened since then to that average hourly work week that changes weekly earnings to hourly earnings:


Bloomberg: US Average Weekly Hours All Employees Private Sector SA, Monthly Jan, 2006 – April, 2021

You see what’s happening? We are now at an all-time high of estimated average weekly hours worked, which artificially depresses the average hourly earnings cartoon. If you just make your percentage comparisons off hourly earnings data, wage inflation doesn’t look nearly as bad. It’s still quite noticeable, but seems more of an anomaly, more of something that is “transitory”.


Bloomberg: US Average Hourly Earnings All Employees Private Sector SA, Y-o-Y %, Monthly Jan, 2007 – April, 2021

Again, there is absolutely no fundamental reason to report an hourly earnings number instead of a weekly earnings number. The BLS itself calculates the weekly number as their primary dataset to see what is truly happening with wages, and only converts to hourly wages because THAT WAS POLITICALLY ADVANTAGEOUS BACK IN FREAKIN’ 1915.

The investment question you hear constantly today is whether or not supply-driven inflation will eventually make its way into wage and price inflation. This is the wrong question. Or rather, it was the right question to ask a year ago, but now it’s been answered.

Wage and price inflation aren’t coming. They’re already here.

The right question to ask today is how bad this wage and price inflation cycle will be. I think it’s gonna be pretty bad, in large part because it’s not yet common knowledge. It’s not yet what everyone knows that everyone knows. It’s not yet contemplated as a potential outcome by our omnipotent market missionary, the Federal Reserve, who remains trapped – not by policy but by narrative – in its insistence that this cannot possibly be the start of a wage-price inflation cycle.

Inflation is transitory” is the new “subprime is contained”.

Do I think we will continue to see wage inflation running at 7% year-over-year? Not really. I dunno. I really don’t. I’m not here to predict. I mean, these things are always overdetermined, and if you want to tell me that last spring’s wage increases were a constructed illusion based on lots of low-wage workers getting booted and higher-wage workers staying on the job, I can’t say that you’re wrong. But I can tell you that month-over-month wage increases THIS spring are running at more than 10% annualized.

More importantly, I can tell you that it doesn’t matter.

Are all of these government wage and price reports constructed artifacts of a whole host of nudging and nudgeable factors? YES. That is my point! They are all cartoons. Intentionally so. Why? Because cartoons work. We are biologically hardwired and socially trained to respond to these cartoons, both as employers AND as workers. The cartoons work in both directions, to encourage deflationary expectations AND inflationary expectations.

When I observe the narrative coming out of last Friday’s jobs reports, I see employers coming to grips with the fact that they need to lift wages even more to satisfy their labor needs in a reopening economy. I see a new ballgame when it comes to wages and prices. A new ballgame that we haven’t played in forty years. A new ballgame where we are only in the first inning.

I’m not predicting. I’m observing.


29+

Wage Inflation Is Already Here

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I’ll be writing this up as a more general note over the next day or so, but wanted to go ahead and share with you some data that is totally out of the financial media narrative, but I think is really important. Please feel free to give me a shout or drop me a note if you want more details before publication of the long-form note, which I hope will get some attention. Here’s the skinny:

Over the past four quarters, the United States has generated more wage inflation than at any point over the past 40 years.

This is not an anomaly. This is not a single quarter aberration. This is four straight quarters of the highest wage growth numbers in 40 years.

Here’s the chart of average weekly earnings (weekly earnings, not hourly!) for Americans in private sector jobs from 1964 through 2021, measured quarterly.



And here’s the same data on a monthly basis over the past 14 years:



Q1 2021 wages were 7.7% higher than Q1 2020 wages.

Q4 2020 wages were 7.7% higher than Q4 2019 wages.

Q3 2020 wages were 6.2% higher than Q3 2019 wages.

Q2 2020 wages were 6.5% higher than Q2 2019 wages.

Over the past 10 years prior to the past 4 quarters, the highest single quarterly year-over-year wage growth was 3.6% in Q4 2018.

Over the past 20 years prior to the past 4 quarters, the highest single quarterly year-over-year wage growth was 4.5% in Q4 2006.

Over the past 30 years prior to the past 4 quarters, the highest quarterly year-over-year wage growth was 4.8% in Q4 1997.

You have to go back 40 years – to Q3 1981 – to find a higher quarterly year-over-year wage growth number (+8.5%).

The US inflation rate in 1981 was 10.3%.

The reason no one is talking about this or even recognizing that this wage inflation exists is largely because of the cartoonification of unemployment and wage data. I’m using the word ‘cartoon’ in its technical sense here, as an abstraction of an abstraction. I’ve written about these cartoons of macroeconomic data in service to political ends quite a bit (in fact, yesterday we published a nice short note on the cartoon that is CPI), but here’s the note that discusses the cartoon of average hourly wage earnings in great detail:

The Icarus Moment

We live in a Cartoon Age, an era not of alienation per Karl Marx, but of alienation per Groucho Marx.

What’s the cause, what’s the future, and what do we do about all this? 

In the beginning, there was a desire to model the employment patterns of the U.S. economy to help policymakers figure out what was actually going on. So in 1884 (!) Congress established the Bureau of Labor Statistics (BLS) to do some counting and abstracting, and since 1915 (!) the BLS has been surveying employers to estimate how many Americans are working and how much they’re being paid. On the first Friday of every month, the BLS releases its report on the real-world employment patterns in the U.S. for the prior month. This data is an abstraction, to be sure, full of seasonal adjustments and model estimations, but it is a first level abstraction. This is not the cartoon.

One of the standard calculations that the BLS reports is the percentage change on a year-over-year basis in how much workers are being paid. Usually this wage growth report takes a backseat to the more famous “jobs report” of how many jobs were added or subtracted from the U.S. economy in the prior month and the even more famous “unemployment report” (which is actually based on an entirely different survey) of the percentage of Americans who were actively looking for work but were unable to find jobs. But when everyone and his cousin is either worried about wage inflation or hoping for wage increases, then the wage growth “number” takes on enormous importance. It’s the depiction and the narrative around the BLS wage growth calculation that is the cartoon. And that cartoon is everything for markets today.

The most basic way to look at wages for a monthly report would be to count up how much all workers got paid in that prior month. But that doesn’t work for a month-to-month comparison because different months have meaningfully different numbers of days. Unless you’re getting paid on a monthly or twice-monthly basis, then you’re going to be making less in February than you are in January. So the BLS uses the work week as their basic apples-to-apples comparison basis.

As far back as I can trace the theater of BLS reports — and that’s how one should think about these market data reports, as theatrical productions consciously designed to impact behavior — the “number” that’s reported isn’t the apples-to-apples comparison of weekly wages. Instead, it’s hourly wages. Why? Because back in 1915 this is how most people got paid. The abstracted idea of hourly wages connects with people more than the abstracted idea of weekly wages. It’s a more effective tool for eliciting a behavioral response, so that’s why our theatrical effort focuses on it every month.

But here’s the problem with the hourly wage abstraction. It requires introducing a new data estimation into the mix, one that has nothing (or at least very little) to do with the real-world concept we’re trying to represent, which is whether you’re taking home more money today than you did last year. That additional layer of abstraction is the average length of the work week.

Now this data estimation changes very little from month to month. Unlike the difference in work days from month to month, which can be meaningful and is incredibly easy to measure, the difference in work hours from week to week is an immaterial and almost certainly statistically spurious estimation. Here are the average number of hours in the work week since 2012.

Since 2012, the average length of the work week has been as low as 34.3 hours and as high as 34.6 hours. For more than SIX YEARS, the maximum deviation from the mean has been less than NINE MINUTES, less than ONE-HALF OF ONE PERCENT of the total work week. This is the flattest line you will ever see in any time series, and any month-to-month deviation from the mean is almost certainly a spurious statistical estimation. Meaning that the month-to-month differences in the average work week are so far inside your margin of error for this sampling and estimation process that you can have ZERO confidence that you are abstracting anything real. This is as bogus of an abstraction as you will ever see.

And yet it makes all the difference in the world for hourly wage calculations!

Why was the February wage growth number reported on March 9th as 2.6% rather than 2.9%?

Because the average work week in February 2018 was randomly estimated as being six minutes longer than it was a year ago.

Everything you read about what the March 9th wage growth number meant for your portfolio — the entire Goldilocks narrative of a “contained” wage inflation number combined with strong job growth — is based on a statistically spurious result. Everything. It’s all made up. None of it is real.

And yet, on the basis of the Goldilocks narrative, which was the all-day headline of the Wall Street Journal and the talking point of every Missionary on CNBC that Friday, the S&P 500 was up more than 1.7% on the day. That’s $415 BILLION of market wealth created in the S&P 500 alone, in one day, from a cartoon representation of annualized wage growth in the U.S. economy.

I wrote that in 2018. Here’s a chart of what’s happened since then to that average hourly work week that changes weekly earnings to hourly earnings:



You see what’s happening? We are now at an all-time high of estimated average weekly hours worked, which artificially depresses the average hourly earnings cartoon. If you just make your percentage comparisons off hourly earnings data, wage inflation doesn’t look nearly as bad. It’s still quite noticeable, but seems more of an anomaly, more of something that is “transitory”.



Again, there is absolutely no fundamental reason to report an hourly earnings number instead of a weekly earnings number. The BLS itself calculates the weekly number as their primary dataset to see what is truly happening with wages, and only converts to hourly wages because THAT WAS POLITICALLY ADVANTAGEOUS MORE THAN A CENTURY AGO IN FREAKIN’ 1915.

The investment question you hear constantly today is whether or not supply-driven inflation will eventually make its way into wage and price inflation. This is the wrong question. Or rather, it was the right question to ask a year ago, but now it’s been answered … wage and price inflation aren’t coming in the future, they’re already here.

The right question today is how bad this wage and price inflation cycle will be. I think it’s gonna be pretty bad, in large because the financial media narrative hasn’t even recognized it. Yet. Do I think we will continue to see wage inflation running at 7% year-over-year? Not really. That’s not my baseline expectation. But when I look at last Friday’s jobs reports and employers coming to grips with the fact that they need to lift wages even more to satisfy their labor needs in a reopening economy … I don’t think we’re going back to sub-3% wage growth, either. It’s a new ballgame when it comes to wages and prices, and we’re just in the first inning.


0

I’m Trying To Understand Hedonic Adjustments

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Brent Donnelly is a senior risk-taker and FX market maker at HSBC New York and has been trading foreign exchange since 1995. He is the author of The Art of Currency Trading (Wiley, 2019) and his latest book, Alpha Trader, hits the shelves in Q2 2021.

You can contact Brent at [email protected] and on Twitter at @donnelly_brent.

As with all of our guest contributors, Brent’s post may not represent the views of Epsilon Theory or Second Foundation Partners, and should not be construed as advice to purchase or sell any security.



With the intense sturm und drang around inflation right now, expect the next six months to yield intense hand-wringing and chin scratching over CPI, PCE and … hedonic quality adjustment. Quite often when people go to Twitter to rail about CPI, they say something like “Man, my cost of living doesn’t look like that! My tuition and health care costs are skyrocketing!” Savvy statisticians then respond with a flurry of charts that look like the one at right and say “the plural of anecdote is not data” or something similar.

So all those rising prices are being offset by all those falling prices and people are just noticing the prices that go up, right? Nope. The source of the data on that chart is the BLS, the U.S. organization charged with preparing the CPI data. The data has been sliced and diced like crazy.

The CPI index is not a cost of living measure! It is a hedonically-adjusted basket of assumptions that attempts to track some sort of cost vs. quality / total-utility metric over time. So what exactly is hedonic quality adjustment? Let’s ask the BLS:

Hedonic quality adjustment is one of the techniques the CPI uses to account for changing product quality within some CPI item samples. Hedonic quality adjustment refers to a method of adjusting prices whenever the characteristics of the products included in the CPI change due to innovation or the introduction of completely new products.

The use of the word “hedonic” to describe this technique stems from the word’s Greek origin meaning “of or related to pleasure.” Economists approximate pleasure to the idea of utility – a measure of relative satisfaction from consumption of goods. In price index methodology, hedonic quality adjustment has come to mean the practice of decomposing an item into its constituent characteristics, obtaining estimates of the value of the utility derived from each characteristic, and using those value estimates to adjust prices when the quality of a good changes.

The CPI obtains the value estimates used to adjust prices through the statistical technique known as regression analysis. Hedonic regression models are estimated to determine the value of the utility derived from each of the characteristics that jointly constitute an item.

There is a significant degree of modelling and massaging going on. The BLS dances around the CPI as cost of living measure question here (see question #9) and the media often inaccurately describe CPI data as changes in cost of living. This leads to a situation where people see prices ripping higher and CPI at 2.0% and it makes them want to yell at CPI.

Since I always think of TVs and cars as goods that have seen flat or falling prices over the years, I decided to have a quick peek at the real life vs. BLS-imagined price movements in the world of new vehicles. I made the arbitrary and grossly simplifying decision to use the Honda Accord and the Ford Mustang as my cars because they have been popular for more than 30 years, there is plenty of historical pricing data, and their target demographics and brand image haven’t changed much over time.

Here is the Honda Accord, then and now:


The 1990 vs. 2020 versions are clearly not the same car, especially when you compare airbags (or lack thereof), ABS, computer-assist, and all that stuff. The tricky thing for non-time-traveling humans, though, is that roughly the same socioeconomic cohort that bought Ford Mustangs or Honda Accords in 1990 is in line to buy those same cars in 2021. If you hedonically-adjust away the improvements, you are not talking about cost of living anymore, you are talking about quality-adjusted or utility-adjusted cost of living. And the result is a super-squishy approach that requires a litany of assumptions and leads to model output that is more a vague approximation of some utility-adjusted price, not an index that reflects actual real life changes in price.

The next chart is one I created that shows the evolution of the BLS new vehicle price data (red) along with the average price of a Honda Accord and a Ford Mustang each year. Funny enough, the price of a car and the average hourly wage move up at about the same speed (probably not a coincidence!). That’s how Honda Accords and Ford Mustangs remain workhorse middle class cars for over 30 years. If the price was truly remaining flat as the BLS series implies, we’d all be driving BMWs (and eventually McLarens) as wages go higher in a fairly straight line and car prices remain unch.

Did the price of cars go up, or stay flat since 1990? It’s a surprisingly difficult question to answer!

Nominal prices of cars went up about 150%. Hedonically-adjusted car prices barely moved[1]. Real car prices (adjusted for wage growth) were flat. My brain is becoming a pretzel.



And for good order this analysis is not sensitive to what kind of car you pick. All the lines look about the same, the only difference is the upward gradient of the slope, never the direction.

And when it comes to new vehicles, here are some quality changes that might trigger a hedonic adjustment:

If you’re curious, here is the BLS explainer for hedonic adjustment, which includes this fun stuff:

If the item being modeled is men’s shirts, the independent variables might be sleeve length and fiber composition; a simplified version of a hedonic model for men’s shirts might be:

Here all shirts are either short sleeve or long sleeve and either cotton/poly or 100% cotton. After doing the statistical processing BLS might estimate that ß1 = 0.15 and ß2 = 0.25. This indicates that a long sleeve shirt is 15 percent more valuable than a short sleeve one and that a 100% cotton shirt is 25 percent more valuable than a cotton/poly blend shirt.

If the BLS data collector is forced to replace a short-sleeve cotton/poly shirt in the CPI sample with a long sleeve 100% cotton shirt, the CPI would adjust the price of the old item by the features in the new item, leading to a price adjustment of about 49 percent (e0.15+0.25).

If the price of the original shirt had been $20 and that of the replacement shirt $30, rather than using a $10 increase in price for that sample observation, the CPI would adjust the original shirt for sleeves and cotton content resulting in a price estimate of $29.84 (20*e0.15+0.25). This adjustment attributes most of the price difference between the shirts to the change in characteristics and an increase of only $0.16 is shown.

With 46% of the products in CPI hedonically adjusted, it’s hard to know what CPI is actually telling us. Here is a good description of the problem from the Praxis Advisory blog.

While theoretically attractive, hedonic adjustment misses a key point. In all likelihood the good purchased was for the same or slightly higher price, regardless of quality (have Lexus cars dropped in price over the last 5 years?) Consider the following example. Say the only product that Americans purchase are M&M candies—100 M&Ms in a bag that costs $1.00. Each person is limited to one bag.

Through the miracle of productivity, a way is found to fill each bag with 110 M&Ms that is now priced at $1.10. Hedonic adjustment would say that the bag really only costs $1.00 and that the CPI has not increased, since each M&M still costs a penny each. But the cost of the bag of M&Ms has gone up. And since each person must buy a bag, instead of an individual M&M, their cost of living has gone up by 10%. They must fork over an extra dime even though they’re getting more for their money. We can’t buy individual parts of a new car; we have to buy the whole car, complete with quality improvements. And the whole package costs more, improved or not.

While hedonic adjustment may accurately reflect productivity increases, they don’t accurately reflect America’s cost to live. These adjustments accrue to businesses (which in turn don’t provide adequate raises) and the federal government (which in turn under-compensates Social Security recipients).

Which is all fine and good. But we’re still left with this:


A Honda Accord cost $12,000 in 1990 and it costs $25,000 now.

A Mustang was $9,000 and now it’s $27,000.

The BLS has new car prices close to unchanged over the past 30 years.


Here’s the best my pretzel-brain can do to reconcile this. Here are my takeaways:

  • People think CPI is a cost of living index, but it’s not. Stop comparing CPI to how your cost of living has changed.
  • That said, the BLS does not push back very hard on the idea of CPI as a cost of living index!
  • Even though CPI is not a cost of living index, CPI is still used for cost-of-living adjustments to Social Security and other benefits. CPI is also used to price TIPS.
  • There is a point of abstraction at which the original object is no longer recognizable. It is possible that CPI is such an abstraction and this explains why it does not compute with the everyperson’s day-to-day experience. We are all staring at Salvador Dalí’s “Persistence of Memory” screaming “that’s not a clock!” It IS a clock. But it’s also not a clock.
  • Prices for an item can go up or down and that item’s contribution to CPI can be in the same direction, the opposite direction or zero.
  • Interestingly, hedonic adjustments only act as deflators. Say the airline crams another seat in your row, eliminates carry-on bags and otherwise makes your flight less happy and hedonic. Does the hedonically-adjusted price of your airfare increase? Nope.
  • CPI is a model output dependent on a huge string of assumptions, not necessarily a true reflection of price changes for goods and services purchased by individual consumers.
  • Just because you hear 14 scary inflation anecdotes tomorrow, don’t assume all or any of those price movements will impact CPI in the way that you expect. They might! Or they might not.

This has been my best attempt to better understand the inner workings of CPI and why it looks and smells so different from reality. There is no easy answer to this conundrum as evidenced by this long discussion on the topic on the Social Security dot gov website, but I think this article from Bloomberg on the history of CPI sums it up pretty well:


The first architects of price indexes appreciated the degree to which these numbers are nothing more than vague approximations that, precisely because they rest on such shaky foundations, can be put toward political ends.


Sounds about right. Have a plaid and/or ludicrous day.


[1] I am fairly sure hedonic-adjustment of new vehicle prices started in 1998 / 1999 but I couldn’t find the precise answer.


Brent Donnelly is a senior risk-taker and FX market maker at HSBC New York and has been trading foreign exchange since 1995. He is the author of The Art of Currency Trading (Wiley, 2019) and his latest book, Alpha Trader, hits the shelves in Q2 2021.

You can contact Brent at [email protected] and on Twitter at @donnelly_brent.


Disclaimer

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The Zeitgeist – May 7, 2021

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Here’s what we’re reading and working on this week at Epsilon Theory.

To receive a weekly full-text email of The Zeitgeist, please sign up here. It’s free, and your email will not be shared with anyone. Ever.

Every item in this email is a discussion thread on the Epsilon Theory Forum – a safe space to speak your mind, a safe space to find like-minded truth-seekers. Watch from a distance if you like, but when you’re ready … join us.



Now Hiring

From a Wall Street Journal article on Thursday:

Millions Are Unemployed. Why Can’t Companies Find Workers?

In a red-hot economy coming out of a pandemic and lockdowns, with unemployment still far higher than it was pre-Covid, the country is in a striking predicament. Businesses can’t find enough people to hire.

Rising vaccination rates, easing lockdowns and enormous amounts of federal stimulus aid are boosting consumer spending on goods and services. Yet employers in sectors like manufacturing, restaurants and construction are struggling to find workers. There are more job openings in the U.S. this spring than before the pandemic hit in March 2020, and fewer people in the labor force, according to the Labor Department and private recruiting sites.

If only there were some mechanism by which companies could entice people to work for them. I dunno, it’s a real head scratcher.


What Sort of Business is Investment Banking?

If you haven’t read Marc Rubinstein’s note we published this week, it’s well worth checking out!

So fortunate to have the chance to republish some of Marc’s notes, and his meme-craft is beyond compare. Honestly, this is the best meme I’ve seen on Fintwit in a loooong time.


Origins of the Panopticon

Also well worth checking out is the podcast we put out this week — In Praise of Bitcoin. I think this is our best pod yet, not just for what it has to say about Bitcoin, but as a good entry point to what Epsilon Theory is all about.

Part of what Epsilon Theory is all about is pointing out modern implementations of the Panopticon … a proposal from the late 1700s for the perfect prison, where the inmates (that’s us!) willingly enforce the jailer’s discipline on themselves.

An ET Pack member sent me this DM regarding the origins of the Panopticon concept, which I found fascinating! Yes, Jeremy Bentham proposed the design as a perfect prison, but it was his brother Samuel who originally conceived the idea as a way of forcing shipyard laborers to accept fiat and snitch on each other regarding their favored form of compensation: leftover lumber.

Ben, forgive me if you already know this bit of panopticon history, but I didn’t see it mentioned in the original ET Panopticon note, nor hear it in the recent Bitcoin podcast. I thought you’d enjoy it if you aren’t already familiar:

I believe it was in fact Jeremy’s brother, Samuel Bentham, who developed the concept for the Panopticon.

The inspiration for its development came from Samuel’s time working in British naval yards and trying to develop a way to enforce laborers to ACCEPT MONETARY WAGES as a form of remuneration. They didn’t want the currency, they wanted “chips” — bits of unused material from the ship building process for which they could negotiate value.

Excellent reading on the topic here. And credit for my knowledge of this to the late, great David Graeber, who mentioned it in Debt, IIRC:

https://newxcommoners.files.wordpress.com/2013/01/linebaugh-ships-and-chips1.pdf


But the Windmills!

From a Wall Street Journal article on Friday:

As Texas Went Dark, the State Paid Natural-Gas Companies to Go Offline

The Electric Reliability Council of Texas activated a program that pays large industrial power users to reduce their consumption during emergencies. But the grid operator, known as Ercot, didn’t know who was being paid to participate in this program and what type of facilities were getting shut off, it has since acknowledged.

The Journal’s analysis of grid records shows that participants included dozens of critical pieces of natural-gas infrastructure. Ercot ordered them to stay off for more than four days, as gas prices surged to extraordinary levels and some power plants stopped producing electricity because they couldn’t get enough fuel to function.

The estimated value of the program for the five days of the blackout was about $2 billion—and participants including oil-and-gas companies earned a portion of that for turning themselves off at Ercot’s behest.

“Ercot officials told the Journal that it was unaware it was cutting off some gas supplies when it ordered the big industrial users to stop using power.”


The Best Way to Rob a Bank – Epilogue

Here’s a follow-up from our note on the Greensill fraud and all its associated raccoons: Greensill made its bankruptcy filing!

We’ve saved this document for posterity on our servers, and you can download it here: Greensill BK Filing.

I haven’t had the chance yet to review this masterpiece in detail, but I’m certain that there must be some choice nuggets in here for enterprising Epsilon Theory readers to unearth.



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ET Podcast #10 – In Praise of Bitcoin

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The Epsilon Theory podcast is free for everyone to access. You can grab the mp3 file below, or you can subscribe at:


Bitcoin has been subverted by the neutering machine of Wall Street and the regulatory panopticon of the US Treasury Dept.

What remains is a constructed narrative that exists in service to Wall Street and Washington rather than in resistance.

We don’t have to tell a story of price. We don’t have to tell a story of apocalypse. We don’t have to scold or “educate”.

We can tell an Old Story of autonomy of mind and generosity of spirit within a new context of Bitcoin and crypto.

Bitcoin! TM definitely belongs to Caesar. It’s part of his game. But Bitcoin doesn’t have to be. It can be part of our game. Still. Again. And that will change everything.



In Praise of Bitcoin

What made Bitcoin special is nearly lost, and what remains is a false and constructed narrative that exists in service to Wall Street and Washington rather than in resistance.

The Bitcoin narrative must be renewed. And that will change everything.


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The Martingale Market

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Before I jump into a discussion of this month’s Narrative Monitors, I want to give you a brief update on our research efforts, which I’ll present around a specific example – our publication of In Praise of Bitcoin last week.

In Praise of Bitcoin

What made Bitcoin special is nearly lost, and what remains is a false and constructed narrative that exists in service to Wall Street and Washington rather than in resistance.

The Bitcoin narrative must be renewed. And that will change everything.

The note itself is really good, I think! And the response has been … crazy. For example, the tweet that I used to publicize the note has generated 1.6 million impressions so far, which is far more than any note-specific tweet to date. So yes, the note is good and popular, and yes, you should read it, but that’s not where I want to call your attention.

The note mentions our intention to examine the archetypes and structural characteristics of Bitcoin narratives. To quote:

In exactly the same way that there are only, say, a dozen archetypal scripts for every TV sitcom episode ever filmed, or in exactly the same way that there are three acts to every modern movie screenplay, so is there an underlying structure and a finite number of underlying archetypes to the media coverage of every market entity.

We believe that we can measure these narrative structures and archetypes as they apply to Bitcoin! TM, and map those structural dynamics to market behaviors.

What we’re describing here is our belief that we can now go beyond the identification of entity-specific narrative archetypes – the five Fed narrative archetypes in the CB Monitor, for example, or the three “language of Wall Street” archetypes in the SAM Narrative Monitor – and also identify underlying entity-generic narrative structures like “Bear Case” or “Bull Case” or “This is Expensive” or “This is Cheap”. We think that combining entity-generic narrative structures with entity-specific narrative archetypes creates a MUCH more powerful lens for understanding market behavior than either analysis on its own, and over the next 6-8 weeks we will find ways to roll out this new analytical lens within ET Pro.

Honestly, we’re building this airplane and testing new wing designs while we are flying it, so I don’t want to commit to any results-oriented outcomes. But I did want to give you a heads-up on our new process for understanding narratives and their impact on markets, and we are looking forward to the launch of a third Narrative Monitor over the next few months focused on … Bitcoin.

So now on to this month’s Narrative Monitors.

On the surface, very little has changed from the past several months. The dominant Central Bank narrative regime is still Hawkish, which is a moderately market-negative signal, but it’s still just barely the tallest blade of grass in a closely-mowed patch of grass. The dominant Security Analysis Methods narrative regime is still Multiples, which is a moderately market-positive signal, although it is still tightly wound-up with the hawkish/reflation narrative regime we see over in the Central Bank analysis.

As we’ve been noting for several months now, the narrative takeaway here is NOT that everyone knows that everyone knows that we have a hawkish Fed today and we’re in a value-outperformance environment today. We do NOT see common knowledge that the Fed today presents a headwind for markets in general, and growthy tech stocks, infinite PE stocks, and long-duration securities in particular. What we do see, though, is growing narrative stability around the idea that it is a matter of when, not if, that we get a hawkish Fed and a value-outperformance market environment. No Wall Street Missionary is asking for this – at least not the hawkish Fed part – but the main narrative stream is increasingly resigned to it!

This growing narrative resignation to a future hawkish Fed and a future tough road for high-flying, high-multiple stocks has two major implications, I think. First, it prevents exuberant market rallies. Good macro news becomes bad market news, because it hastens that evil day when the Fed is forced to start tapering. Sure, value stocks will outperform a bit, and that will make a certain group of long-suffering discretionary managers happy, but given the market-cap dominance of the high-flyers in every core index and every core portfolio, that’s going to be small comfort. On the flip side, though, this growing narrative resignation makes it much harder for the Fed and its Missionaries to shock the market into a really bad stretch. Narrative resignation like this acts like a repeated dose of Moderna vaccine … you get brief spells of malaise and aches and pains, and you feel pretty terrible for a while, but you’re not going to end up in the hospital. That’s a tortured metaphor to make the point, but I think you get the idea.

Here’s a better metaphor. The growing narrative resignation to a future hawkish Fed and a future tough road for high-flying, high-multiple stocks is a martingale. It’s a couple of leather straps that keep a horse from lifting its head too high and getting too spirited, and the overall effect is to keep the horse under control. I think that’s what we’re looking at with capital markets until further notice … a narrative martingale that makes market exuberance impossible, but also keeps the market from getting totally spooked.


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What Sort of Business is Investment Banking?

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Rusty and I are thrilled to announce that Marc Rubinstein will be joining us as a guest contributor to Epsilon Theory.

Marc has over 25 years experience as an analyst and investor in the financials sector which he distills into a free weekly newsletter, Net Interest, which I think is a really great read! Between 2006 and 2016 he was senior analyst and portfolio manager on the Lansdowne Global Financials Fund, a fundamental long/short equity fund focused exclusively on the global financials sector. Prior to that, Marc was an Institutional Investor ranked analyst on the sell-side, most recently at Credit Suisse, where he was a managing director overseeing its European banks team. As well as writing Net Interest, Marc is an active angel investor in fintech. He can be contacted via his newsletter or on Twitter (@MarcRuby).

As with all of our guest contributors, Marc’s post may not represent the views of Epsilon Theory or Second Foundation Partners, and should not be construed as advice to purchase or sell any security.



Last weekend, Credit Suisse closed its books on the quarter and announced a big loss. It’s a peculiar sort of loss because it stems entirely from a single client – Archegos Capital Management. According to the bank, its dealings with Archegos “negate the very strong performance that had otherwise been achieved by our investment banking business.” In other words, performance would have been really good, befitting of the bull market, were it not for that one pesky client. 

Which leads to a question: what sort of a business is this? Credit Suisse has over 1.6 million individual clients, over 100,000 corporate clients and tens of thousands of institutional clients. Yet a single client can blow through the profits made from all the others. 

It’s not the first time it’s happened. Over the years, investment banks have suffered huge losses – if not at the hands of a single client then at the hands of a single employee. We highlighted some rogue trading cases here a few months ago: Jérôme Kerviel cost his bank $6.9 billion; Nick Leeson cost his bank its solvency. 

Although the frequency of rogue trading incidents has diminished in the recent past, that hasn’t stopped investment banks from piling up some stinking losses. In the living memory of most market participants, Credit Suisse alone has:

  • Written down $2.85 billion of asset-backed bonds after they had been mispriced by traders. (February 2008)
  • Topped up $633 million of write-downs with another $346 million because “even internally the scale of those positions was a surprise for a number of people” and the CEO “was not aware of the existence of those positions on that scale.” (March 2016)
  • And now, a $4.7 billion loss on Archegos.

Welcome to the business of investment banking.

What do investment banks do?

Defined broadly, investment banks do a host of stuff for corporate and institutional clients. They advise on mergers and acquisitions and underwrite securities offerings; they facilitate trading of debt and equity instruments; they structure derivatives to allow clients to hedge or to take on risk.

The biggest of the bunch is Goldman Sachs. At a presentation at Harvard a few years ago, Goldman’s former CFO explained his business: “[A] client has a risk they don’t want or wants a risk they don’t have… we make it happen for them.”

More specifically, investment banks like Goldman intermediate a diverse set of risks:

  • They take risk, like when they lead an IPO. Last week, Goldman oversaw the IPO of Deliveroo in London. The stock fell 30% on the first day of trading and Goldman was forced to go into the market to support it, buying up £75 million worth of stock. Advancing margin loans to firms like Archegos is another example of taking risk, although usually it is mitigated by concentration limits and lending less than the value of the collateral. 
  • They match risk, by transferring it between parties. As a producer of oil, the Government of Mexico may want to protect itself against a drop in oil prices. Airlines, by contrast, may want to protect themselves against a rise in prices. Investment banks provide the search functionality for either side to find each other, but also transform the specific risk that each side holds (from Maya crude in the case of the Mexican government to jet fuel in the case of the airlines). 
  • They source risk, by seeking investment opportunities for clients. Credit Suisse did this badly when it packaged Greensill loans into an investment fund for clients. (Granted, this occurred in its asset management division rather than its investment banking division, but still.) Other times it may package structured notes or baskets of stocks for clients, enabling them to express a multitude of investment views. 

In short, investment banks traffic in risk. The entry-level investment bank recruit’s handbook is Liar’s Poker. In it, Michael Lewis writes:

Risk, I had learned, was a commodity in itself. Risk could be canned and sold like tomatoes. Different investors place different prices on risk. If you are able, as it were, to buy risk from one investor cheaply and sell it to another investor dearly, you can make money without taking any risk yourself. And this is what we did.

Buying and selling risk can be a profitable business, although it’s not as profitable as it used to be. Coalition Greenwich is an analytics company that tracks global investment bank revenues. The people there reckon that last year, investment banks earned nearly $200 billion of revenue, the most in over a decade. Sales and trading made up almost $150 billion of that, the rest being advisory and underwriting fees.

Prior to 2020, operating margins in the industry had been coming down, but last year they jumped to 44%. Such high margins require a lot of capital to generate. The sales and trading business in particular is quite capital intensive. Risk has to sit somewhere and in many cases it can hang around for many years. Deutsche Bank has a specific portfolio of interest rate derivatives, for example, whose average life is eight years. Unlike a simple agency broking model, full-scale risk intermediation requires banks to carry a large balance sheet. Consequently, the returns on equity are a bit more pedestrian. Prior to last year, industry return on equity was typically sub-10%; last year it jumped to 13%.

Source: Coalition Proprietary Analytics

Beneath these aggregate numbers, though, there’s a lot of ups and downs. Last year, Goldman Sachs did $21 billion of revenue in its global markets businesses but some days were good and some were bad. According to disclosures, Goldman lost money on 24 days over the course of the year. On two of those, it dropped more than $75 million on a single day. Yet the firm also scored some massive home runs, making over $100 million per day on no fewer than 50 individual trading days.

Goldman’s skew towards a high number of really profitable days was especially pronounced in 2020. The distribution of daily trading revenue is illustrated in the chart below. Last year’s distribution (the blue line) looks closest in shape to 2011, when the number of “home runs” was last as high, but in that year there were many more loss-making days offsetting the gains. 

Source: Net Interest, company data

As well as the daily ups and downs, which can be a function of market opportunity, there is also the ebb and flow of market share. In 2020, Goldman pulled in around 13% of the total fixed income revenues generated by the top nine global investment banks. Market share tends to be quite sticky over the medium term, since there’s an optimum number of firms clients are happy to deal with – they want more than five banks but they don’t need more than fifteen. Over the past ten years, the biggest loser of market share has been Deutsche Bank, whose share of fixed income trading dropped from around 13% in 2013 to around 9% last year. Importantly, though, Deutsche had to work really hard to lose that share, cutting back its presence in the market significantly.

A Competitive Market

Competition in the industry plays out through a competition for talent – which means the wage bill in the industry can be very high. One of the banks eager to make it into the top bracket of firms before the financial crisis was Barclays. Philip Augar tells the story in his book, The Bank that Lived a Little. He quotes the global head of HR: “We are the highest payer on the street. The competitors all say we are driving up pay rates… No other bank has a scheme like our long-term plan.” Between 2002 and 2009, Barclays’ long-term incentive plan paid on average £170 million each year to 60 people on top of their salary and bonus. Nice work if you can get it!

In those days, employees would typically get a 50% cut of the revenues. This led to perverse incentives where traders would seek to maximise revenue without regard for risk in order to expand the compensation pool. The financial crisis put paid to that and compensation rates have since come down. Those big balance sheets are the gift of shareholders and since the crisis they have demanded a greater share of the economics for supporting them. Last year, Goldman paid a record low 30% in compensation to employees.

Nevertheless, pay still remains relatively high in the industry. Deutsche Bank discloses annual compensation of each of the 2,300 “material risk takers” it employs. Last year, 684 of them took home more than €1 million of total pay and one of them took home more than €10 million.

There’s another way competition plays out, which we became witness to in the Archegos saga. Back in Liar’s Poker, Michael Lewis quotes a leading bond salesman at his firm: “The trading floor is a jungle.” It’s an accurate observation of the industry. We now know one of the reasons why Credit Suisse’s loss on Archegos was so big is that for other firms involved it was so small. By unloading Archegos positions earlier, other firms were able to minimise their losses while at the same time making them worse for Credit Suisse. There’s a zero-sum aspect to the game. 

The situation reminds me of something a divisional head at Morgan Stanley once told me. Reflecting on his competitive strategy, he quoted General George S. Patton: “No bastard ever won a war by dying for his country. He won it by making the other poor dumb bastard die for his country.”

In the Archegos case, because the payoff was non-linear and situations like it come up relatively infrequently, there is little incentive for firms to cooperate. Over the very long term, of course, that strategy can go awry. In 1998, fourteen of the largest investment banks in the world agreed to post $3.65 billion to take over all the assets of failing hedge fund LTCM. Only Bear Stearns declined to participate. Ten years later, the same banks refused to bail out Bear Stearns when it ran into its own troubles. 

But striving for a Nash equilibrium in a round of Prisoner’s Dilemma is not the sole reason some firms end up doing worse than others. Credit Suisse is in the crosshairs now, but it’s part of a broader phenomenon whereby European banks just aren’t that good at investment banking compared with American banks. They can be run by Americans, staffed with Americans, they can even have grown by acquiring American firms, but they’re not that good. Understanding why gets to the heart of what it takes to be good at investment banking. 

European Investment Banks

The first thing that differentiates European investment banks from US ones is that they weren’t founded as investment banks. We’ve discussed the origins of both Goldman Sachs and Deutsche Bank here before. Goldman was founded in 1869 as Marcus Goldman, Banker and Broker, with an initial focus on the commercial paper market. Deutsche Bank was founded one year later to provide long-term loans to German industry. It wasn’t until the 1990s that Deutsche Bank threw itself into investment banking, in response to the structurally low level of profitability it faced in its domestic banking market. In 1990, it acquired London based Morgan Grenfell and, after spending heavily in an attempt to build a global presence organically, went on to buy Bankers Trust in 1999. 

Credit Suisse has a longer legacy in investment banking and may even be seen as the creator of the first truly global investment bank. Credit Suisse entered the market via joint venture, first with White, Weld and Co. (later acquired by Merrill Lynch) in 1962 and then, in 1978, with First Boston. At the time, First Boston was one of the leading firms on Wall Street; Credit Suisse took a 25% stake and they each contributed capital to a joint venture. The deal was struck just before a change in the law made it an impossible structure to replicate. The Glass-Steagall Act had prevented US commercial banks from entering investment banking since 1933, but the stipulation was only extended to foreign banks in 1978. Consequently, Credit Suisse had a twenty year headstart on European banks buying into the US investment banking market. 

Through the 1980s, the relationship worked very well. Credit Suisse looked after the Swiss market; First Boston, the American and Australian markets; and the joint venture – CSFB – was responsible for Europe and the rest of the world. Across the three groups, the franchise became a top three player in M&A and established leading positions in equity and debt underwriting, with market shares of 11-15% and 9-15% respectively. 

However, as markets increasingly globalised, the three groups started treading on each others’ toes. In 1988, they were merged into a single firm headquartered in New York, in which Credit Suisse took a 45% stake (employees held 25% and institutional investors 30%). One year later, disaster struck. CS First Boston had become a leading player in the junk bond market. In 1988 it was ranked #2, behind Drexel Burnham Lambert. When the market collapsed, CS First Boston was left holding $1.1 billion of paper it couldn’t shift. With the firm’s future in doubt, Credit Suisse was forced to bail it out, taking majority control and slashing its headcount and balance sheet. 

CS First Boston never really recovered. Although it was run autonomously, it didn’t get the resources – either capital or staff budget – that other firms did. Staff defected in droves. Having been a top 3 player in M&A and underwriting in the 1980s, the firm slipped to top 5 in the 1990s. It had its license revoked in Japan following misconduct there and suffered huge losses in Russia. To restore its position, Credit Suisse acquired parts of Barclays’ investment banking business in 1997 and then Donaldson, Lufkin & Jenrette in 2000. Yet the DLJ acquisition turned out to be one of the most expensive in investment banking history; sixteen years later its goodwill was finally written off. 

One possible explanation for the sustained poor performance of European investment banks is that they are always playing catch-up. In an effort to close the gap with the market leaders, they take short cuts, taking on excessive risk either via leverage, concentration or duration. (On duration, it is notable that Credit Suisse still has an ‘asset resolution unit’ consisting of $14 billion of assets it’s been trying to get rid of for years; Deutsche Bank has a ‘capital release unit’ of $240 billion.) 

Another explanation is that they rely too much on models, over-intellectualising the risk management process. One Twitter thread describes how Credit Suisse’s Archegos exposure may have bypassed its risk models completely, even though the underlying risk was plain. We’ve discussed risk management here before, in Wimbledon and the Art of Risk Management, and the answers aren’t always in the models. Way back in 2012, Goldman’s CFO said, “While metrics and quantitative measures are an important part of risk management the judgment and experience of our people that overlay these models is a key component.” 

The fact is, for investment banks, risk management is their business. If they take risk, match risk and source risk, they can’t outsource the management of that to a chief risk officer; it’s the job of the frontline staff. How that all hangs together – how the incentives of staff are reconciled with the health of the firm, particularly in an environment where individual compensation can be very high – comes down to the culture of the firm. And culture takes a long time to build, longer than most participants in fast-moving markets have the energy to invest. 

British readers will be familiar with Trigger’s Broom (comedy gold, if you haven’t seen it). You can change the head multiple times, you can change the stick, but it stays the same broom. Credit Suisse has gone through four investment banking heads in the past ten years and its staff has turned over, but the culture remains the same. 

After announcing his Archegos losses, the CEO of Credit Suisse said, “Serious lessons will be learned. Credit Suisse remains a formidable institution with a rich history.” Unfortunately, it’s a rich history of not learning its lessons. 

Full disclosure: I was a managing director at Credit Suisse once and still have a soft spot for the firm. I really do hope they sort this out.


Marc Rubinstein has over 25 years experience as an analyst and investor in the financials sector which he distills into a weekly newsletter, Net Interest. Between 2006 and 2016 he was senior analyst and portfolio manager on the Lansdowne Global Financials Fund, a fundamental long/short equity fund focused exclusively on the global financials sector. Prior to that, Marc was an Institutional Investor ranked analyst on the sell-side, most recently at Credit Suisse, where he was a managing director overseeing its European banks team. As well as writing Net Interest, Marc is an active angel investor in fintech. He can be contacted via his newsletter or on Twitter (@MarcRuby).

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