Friday, December 15, 2017

Jim Simons: Renaissance Man, Crypto Billionaire

Crypto in the older, accurate sense.
From the New Yorker, December 18 & 25, 2017 Issue:

Jim Simons, the Numbers King
Algorithms made him a Wall Street billionaire. His new research center helps scientists mine data for the common good.
A visit to a scientific-research center usually begins at a star professor’s laboratory that is abuzz with a dozen postdocs collaborating on various experiments. But when I recently toured the Flatiron Institute, which formally opened in September, in lower Manhattan, I was taken straight to a computer room. The only sound came from a susurrating climate-control system. I was surrounded by rows of black metal cages outfitted, from floor to ceiling, with black metal shelves filled with black server nodes: boxes with small, twinkling lights and protruding multicolored wires. Tags dangled from some of the wires, notes that the tech staff had written to themselves. I realized that I’d seen a facility like this only in movies. Nick Carriero, one of the directors of what the institute calls its “scientific-computing core,” walked me around the space. He pointed to a cage with empty shelves. “We’re waiting for the quantum-physics people to start showing up,” he said.
The Flatiron Institute, which is in an eleven-story fin-de-siècle building on the corner of Twenty-first Street and Fifth Avenue, is devoted exclusively to computational science—the development and application of algorithms to analyze enormous caches of scientific data. In recent decades, university researchers have become adept at collecting digital information: trillions of base pairs from sequenced human genomes; light measurements from billions of stars. But, because few of these scientists are professional coders, they have often analyzed their hauls with jury-rigged code that has been farmed out to graduate students. The institute’s aim is to help provide top researchers across the scientific spectrum with bespoke algorithms that can detect even the faintest tune in the digital cacophony.

I first visited the Flatiron Institute in June. Although the official opening was still a few months away, the lobby was complete. It had that old-but-new look of expensively renovated interiors; every scratch in the building’s history had been polished away. Near the entrance hangs a Chagall-like painting, “Eve and the Creation of the Universe,” by Aviva Green. Green’s son happened to be spending the year at the institute, as a fellow in astrophysics. “Every day, he walks into the lobby and sees his mother’s picture,” Jim Simons, the institute’s founder, told me.

Simons, a noted mathematician, is also the founder of Renaissance Technologies, one of the world’s largest hedge funds. His income last year was $1.6 billion, the highest in the hedge-fund industry. You might assume that he had to show up every day at Renaissance in order to make that kind of money, but Simons, who is seventy-nine, retired eight years ago from the firm, which he started in the late seventies. His Brobdingnagian compensation is a result of a substantial stake in the company. He told me that, although he has little to do with Renaissance’s day-to-day activities, he occasionally offers ideas. He said, “I gave them one three months ago”—a suggestion for simplifying the historical data behind one of the firm’s trading algorithms. Beyond saying that it didn’t work, he wouldn’t discuss the details—Renaissance’s methods are proprietary and secret—but he did share with me the key to his investing success: he “never overrode the model.” Once he settled on what should happen, he held tight until it did.

The Flatiron Institute can be seen as replicating the structure that Simons established at Renaissance, where he hired researchers to analyze large amounts of data about stocks and other financial instruments, in order to detect previously unseen patterns in their fluctuations. These discoveries gave Simons a conclusive edge. At the Flatiron, a nonprofit enterprise, the goal is to apply Renaissance’s analytical strategies to projects dedicated to expanding knowledge and helping humanity. The institute has three active divisions—computational biology, computational astronomy, and computational quantum physics—and has plans to add a fourth.

Simons works out of a top-floor corner office across the street from the institute, in a building occupied by its administrative parent, the Simons Foundation. We sat down to talk there, in front of a huge painting of a lynx that has killed a hare—a metaphor, I assumed, for his approach to the markets. I was mistaken, Simons said: he liked it, and his wife, Marilyn, did not, so he had removed it from their mansion in East Setauket, on Long Island. (Marilyn, who has a Ph.D. in economics, runs the business side of the foundation, and the institute, from two floors below.) An Archimedes screw that he enjoyed fiddling with sat on a table next to a half-filled ashtray. Simons smokes constantly, even in enclosed conference rooms. He pointed out that, whatever the potential fine for doing so is, he can pay it.

Simons has an air of being both pleased with himself and ready to be pleased by others. He dresses in expensive cabana wear: delicate cotton shirts paired with chinos that are hiked high and held up by an Indian-bead belt. He grew up in the suburbs of Boston, and speaks with the same light Massachusetts accent as Michael Bloomberg, with frequent pauses and imprecisions. He sometimes uses the words “et cetera” instead of finishing a thought, perhaps because he is abstracted, or because he has learned that the intricacies of his mind are not always interesting to others, or because, when you are as rich as Simons, people always wait for you to finish what you are saying.
On a wall, Simons had hung a framed slide from a presentation on the Chern-Simons theory. He helped develop the theory when he was in his early thirties, in collaboration with the famed mathematician Shiing-Shen Chern. The theory captures the subtle properties of three-dimensional spaces—for example, the shape that is left if you cut out a complicated knot. It became a building block of string theory, quantum computing, and condensed-matter physics. “I have to point out, none of these applications ever occurred to me,” he told me. “I do the math, they do the physics.”

High-level mathematics is a young person’s game—practitioners tend to do their best work before they are forty—but Simons continued to do ambitious mathematics work well into adulthood. In his sixties, after the death of his son Nick, who drowned in Bali in 2003, he returned to it. “When you’re really thinking hard about mathematics, you’re in your own world,” he said. “And you’re cushioned from other things.” (Simons lost another son, Paul, in a bike accident, in 1996.) During these years, Simons published a widely cited paper, “Axiomatic Characterization of Ordinary Differential Cohomology,” in the Journal of Topology. He told me about his most recent project: “The question is, does there exist a complex structure on the six-dimensional sphere? It’s a great problem, it’s very old, and no one knows the answer.” Marilyn told me she can tell that her husband is thinking about math when his eyes glaze over and he starts grinding his jaw.

Our discussion turned to the Flatiron Institute. Renaissance’s computer infrastructure, he said, had been a central part of its success. At universities, Simons said, coding tends to be an erratic process. He said of the graduate students and postdocs who handled such work, “Some of them are pretty good code writers, and some of them are not so good. But then they leave, and there’s no one to maintain that code.” For the institute, he has hired two esteemed coders from academia: Carriero, who had led my tour, had been recruited from Yale, where he had developed the university’s high-performance computing capabilities for the life sciences; Ian Fisk had worked at cern, the particle-physics laboratory outside Geneva. Simons offered them greater authority and high salaries. “They’re the best of the breed,” he said. Carriero and Fisk sometimes consult with their counterparts at Renaissance about technical matters.

Simons’s emphasis on what most of us think of as back-office functions is of a piece with the distinctive computational focus of the institute. The Flatiron doesn’t conduct any new experiments. Most of its fellows collaborate with universities that generate fresh data from “wet” labs—the ones with autoclaves and genetically engineered mice—and other facilities. The institute’s algorithms and computer models are meant to help its fellows uncover information hidden in data sets that have already been collected: inferring the location of new planets from the distorted space-time that surrounds them; identifying links to mutations among apparently functionless parts of chromosomes. 

As a result, the interior of the institute looks less like a lab than like an ordinary Flatiron-district office: casually dressed people sitting all day at desks, staring at screens, under high ceilings.
Simons has amassed the same processing capacity as would normally be present in the computer hub of a mid-sized research university: the equivalent of six thousand high-end laptops. This is powerful, but not ostentatiously so. And, as Carriero conceded, it “cannot be compared to the corporate-wide resources of an Amazon or a Google.” But, because there are far fewer people at the Flatiron Institute, each researcher has immediate access to tremendous computing power. Carriero said that, by supplying scientists with state-of-the-art “algorithms guidance” and “software guidance,” he can help them maintain a laserlike focus on advancing science.

Simons has placed a big bet on his hunch that basic science will yield to the same approach that made him rich. He has hired ninety-one fellows in the past two years, and expects to employ more than two hundred, making the Flatiron almost as big as the Institute for Advanced Study, in Princeton, New Jersey. He is not worried about the cost. “I originally thought seventy-five million a year, but now I’m thinking it’s probably going to be about eighty,” he said. Given that Forbes estimates Simons’s net worth to be $18.5 billion, supporting the Flatiron Institute is, in financial terms, a lark. “Renaissance was a lot of fun,” he told me. “And this is fun, too.”....MUCH, MUCH MORE
If interested see also January's "Rare Interview With Renaissance Technologies' Jim Simons"
One quick heads-up. Simons considers RenTech's secrets to be secret.
And he's good at keeping secrets....
(see also: Crypto

And a post from 10 years ago:
Attention Investors: Here's Your Competition-Bookmark this Link

YCombinator's Sam Altman: The Heresy That Dare Not Speak Its Name

The heresy is thinking/speaking ideas unapproved by the thought police.
Our headline is a play on a phrase that came up at Oscar Wilde's indecency trial,
Mr. Altman's headline is from the quote attributed to Galileo following his recantation at his heresy trial for belief in heliocentricity.

From Mr. Altman's blog:

E Pur Si Muove
Earlier this year, I noticed something in China that really surprised me.  I realized I felt more comfortable discussing controversial ideas in Beijing than in San Francisco.  I didn’t feel completely comfortable—this was China, after all—just more comfortable than at home.

That showed me just how bad things have become, and how much things have changed since I first got started here in 2005.

It seems easier to accidentally speak heresies in San Francisco every year.  Debating a controversial idea, even if you 95% agree with the consensus side, seems ill-advised.

This will be very bad for startups in the Bay Area.

Restricting speech leads to restricting ideas and therefore restricted innovation—the most successful societies have generally been the most open ones.  Usually mainstream ideas are right and heterodox ideas are wrong, but the true and unpopular ideas are what drive the world forward.  Also, smart people tend to have an allergic reaction to the restriction of ideas, and I’m now seeing many of the smartest people I know move elsewhere.

It is bad for all of us when people can’t say that the world is a sphere, that evolution is real, or that the sun is at the center of the solar system.

More recently, I’ve seen credible people working on ideas like pharmaceuticals for intelligence augmentation, genetic engineering, and radical life extension leave San Francisco because they found the reaction to their work to be so toxic.  “If people live a lot longer it will be disastrous for the environment, so people working on this must be really unethical” was a memorable quote I heard this year.

To get the really good ideas, we need to tolerate really bad and wacky ideas too.  In addition to the work Newton is best known for, he also studied alchemy (the British authorities banned work on this because they feared the devaluation of gold) and considered himself to be someone specially chosen by the almighty for the task of decoding Biblical scripture.

You can’t tell which seemingly wacky ideas are going to turn out to be right, and nearly all ideas that turn out to be great breakthroughs start out sounding like terrible ideas.....MORE
Back to the headlines, it is doubtful Galileo actually expressed the "And yet it moves" sentiment, even if he believed it, as doing so would make him a relapsed heretic, the punishment for which was being burned to death.
See for example: Joan of Arc.
From what I understand the old boy was pretty intelligent and could do a risk - reward analysis.

In the case of Wilde it is possible that the "Love that dare not speak its name" line, written by Wilde's lover Lord Alfred Douglas, 16 years his junior (but of-age at the time of their meeting), was not male homosexuality but pederasty which would open up a whole 'nother thing, although not in the Queensberry case where Oscar was vindictively charged with gross indecency not child rape.
Had there been anything to the pedo rumors (rumors in England at any rate, abroad: see André Gide and Oscar), you can bet the Marquess' detectives and attorneys would have found a way to get it out at the trials.

So, where were we?
Ah, careful what you say in Silicon Valley.

Blockchain Consortium, Hyperledger, Loses Members, Funding

No word on Blythe Masters and Digital Asset Holdings which just hooked up with the Australian Stock Exchange.
From Fortune:
More than 15 members of blockchain consortium Hyperledger have either cut their financial support for the project or quit the group over the past few months, according to documents seen by Reuters.
Exchange operators CME Group and Deutsche Boerse have decided to downgrade their membership for the consortium starting at the end of January 2018, according to slides titled “member attrition” from a board meeting presentation held on Friday.

Led by the Linux Foundation, Hyperledger was launched in 2015 to develop blockchain technology for businesses. Blockchain, which first emerged as the system powering cryptocurrency bitcoin, is a shared record of data that is maintained by a network of computers on the internet.
CME Group and Deutsche Boerse were premier members of the group and will downgrade to a general membership.

Premier members are given board seats in the consortium and pay a fee of $250,000 a year. General memberships range from $5,000 to $50,000 based on the size of the companies, according to Hyperledger’s website.

Blockchain consortium R3 has also decided to downgrade its premier membership next year, according to the documents....MUCH MORE
Here's the current Hyperledger membership page.


Well worth a read.

From the Macro Tourist:

I have had some bad trades in my day. But lately, one call has been especially atrocious. For the past couple of years, I have taken stabs on the long side of the grain market. At different times, I have held various positions for different lengths of time, but make no mistake - grains have done nothing but cost me money. Sure, I might have a decent sounding argument, The Last Remaining Cheap Asset, but the market is indisputably telling me that I am dead wrong.

And it’s hard to sit and watch the grains go down. Day after day. Week after week. Month after month. Like the slow drip of a leaky faucet that no one can fix, it can drive you insane.
Have a look at the 5-year chart for front month Wheat.
Tough to make money writing any blue tickets with that sort of action. All rallies have been opportunities to sell, not the start of any sustainable uptrend.
This recent grain bear market has pushed the big three contracts (wheat, corn and soybeans) down to near all time lows when measured in real terms.

I don’t want to bother with another forecast about why this time will be different, and how the low will be made in the coming weeks. After a certain number posts, I begin to more closely resemble a degenerate gambler than a cool calculating macro trader (I think that number might be three, which means it’s too late for me, and I do in fact resemble Richard Dreyfuss a whole lot more than George Soros).
And although I poke fun at myself, it’s no laughing matter. The amount of economic pain in farming is downright scary. According to an article in The Guardian, Why are America’s farmers killing themselves in record numbers?, the stress from low grain prices is causing an epidemic amongst the agricultural community.
Once upon a time, I was a vegetable farmer in Arizona. And I, too, called Rosmann. I was depressed, unhappily married, a new mom, overwhelmed by the kind of large debt typical for a farm operation.
We were growing food, but couldn’t afford to buy it. We worked 80 hours a week, but we couldn’t afford to see a dentist, let alone a therapist. I remember panic when a late freeze threatened our crop, the constant fights about money, the way light swept across the walls on the days I could not force myself to get out of bed.

“Farming has always been a stressful occupation because many of the factors that affect agricultural production are largely beyond the control of the producers,” wrote Rosmann in the journal Behavioral Healthcare. “The emotional wellbeing of family farmers and ranchers is intimately intertwined with these changes.”

Last year, a study by the Centers for Disease Control and Prevention (CDC) found that people working in agriculture - including farmers, farm laborers, ranchers, fishers, and lumber harvesters - take their lives at a rate higher than any other occupation. The data suggested that the suicide rate for agricultural workers in 17 states was nearly five times higher compared with that in the general population......
...... The lightbulb
For the longest time, I had no idea why grain prices were so low. It perplexed me. Central Banks around the globe were printing money at an unprecedented pace. All else being equal, you would expect a real asset, like grains, to have rallied in these circumstances. Yeah sure the advances in farming technology might keep the price of grains pressured, but at the same time, demand has also never been higher, so you would expect the debasement of money to eventually win out and send grains prices skyward.

But more importantly, these situations are usually self correcting. Nothing solves the problem of oversupply like low prices. Except this time. Even with the state of farming littered with heartbreaking stories of ruined families, not enough farmers are giving up planting crops to allow the price to rise....MUCH MORE
Our intro to and outro from "The Last Remaining Cheap Asset":
Ah hell.
I hate seeing stuff like this in print.
It's true but I hate seeing it.
And it gets worse. He profanes an image of Julie Andrews along the way.
This Julie Andrews:
"It would surprise no one, perhaps, to learn that Julie Andrews travels with her own teakettle."
No. No it would not....
The only positives of seeing this talked about are 1) You are dealing with a complex-chaotic system (a financial market) overlaid on a complex-chaotic system (weather) that will crush (soybean pun) you should you start to exhibit any symptoms of hubris, thus weeding out quite a few of the wannabes and 2) although the intraday move can be violent the longer term stuff can lull you into serious misreadings of reality.
The best examples are in the presentation of the charts above; it's actually been a wonderful trading environment.
If you've been short.
Which is what two-sided markets are all about.

The Andrews quote is from "Nun with a Switchblade: Julie Andrews and The Fiftieth Anniversary Of The Sound Of Music".

If interested we embedded one of the better homages to Dame Julia Elizabeth in "Watch Out Mary Poppins: The World's First Tea Brewing System Utilizing Machine-Learning Algorithms Has Received Pre-Launch Seed Funding (plus a Princess Rap Battle)"  although purists will probably have the same reaction to it that I had to news Disney was planning a Mary Poppins sequel:


Artificial Intelligence in Risk Management: Looking for Risk in All the Wrong Places

Opportunity is where you find it, turn your risk manager into a profit center.

From naked capitalism, November 15:

Artificial Intelligence and the Stability of Markets
Of course, for those who can take advantage of it, instability in markets may not be such a bad thing. Nor is systemic risk, especially if the public good is not your first concern.
Artificial intelligence (AI) is useful for optimally controlling an existing system, one with clearly understood risks. It excels at pattern matching and control mechanisms. Given enough observations and a strong signal, it can identify deep dynamic structures much more robustly than any human can and is far superior in areas that require the statistical evaluation of large quantities of data. It can do so without human intervention.

We can leave an AI machine in the day-to-day charge of such a system, automatically self-correcting and learning from mistakes and meeting the objectives of its human masters.

This means that risk management and micro-prudential supervision are well suited for AI. The underlying technical issues are clearly defined, as are both the high- and low-level objectives.
However, the very same qualities that make AI so useful for the micro-prudential authorities are also why it could destabilise the financial system and increase systemic risk, as discussed in Danielsson et al. (2017).

Risk Management and Micro-Prudential Supervision
In successful large-scale applications, an AI engine exercises control over small parts of an overall problem, where the global solution is simply aggregated sub-solutions. Controlling all of the small parts of a system separately is equivalent to controlling the system in its entirety. Risk management and micro-prudential regulations are examples of such a problem.

The first step in risk management is the modelling of risk and that is straightforward for AI. This involves the processing of market prices with relatively simple statistical techniques, work that is already well under way. The next step is to combine detailed knowledge of all the positions held by a bank with information on the individuals who decide on those positions, creating a risk management AI engine with knowledge of risk, positions, and human capital.

While we still have some way to go toward that end, most of the necessary information is already inside banks’ IT infrastructure and there are no insurmountable technological hurdles along the way.
All that is left is to inform the engine of a bank’s high-level objectives. The machine can then automatically run standard risk management and asset allocation functions, set position limits, recommend who gets fired and who gets bonuses, and advise on which asset classes to invest in.
The same applies to most micro-prudential supervision. Indeed, AI has already spawned a new field called regulation technology, or ‘regtech’.

It is not all that hard to translate the rulebook of a supervisory agency, now for most parts in plain English, into a formal computerised logic engine. This allows the authority to validate its rules for consistency and gives banks an application programming interface to validate practices against regulations.

Meanwhile, the supervisory AI and the banks’ risk management AI can automatically query each other to ensure compliance. This also means that all the data generated by banks becomes optimally structured and labelled and automatically processable by the authority for compliance and risk identification.

There is still some way to go before the supervisory/risk management AI becomes a practical reality, but what is outlined above is eminently conceivable given the trajectory of technological advancement. The main hindrance is likely to be legal, political, and social rather than technological.
Risk management and micro-prudential supervision are the ideal use cases for AI – they enforce compliance with clearly defined rules, and processes generating vast amounts of structured data. They have closely monitored human behaviour, precise high-level objectives, and directly observed outcomes.

Financial stability is different. There the focus is on systemic risk (Danielsson and Zigrand 2015), and unlike risk management and micro-prudential supervision, it is necessary to consider the risk of the entire financial system together. This is much harder because the financial system is for all practical purposes infinitely complex and any entity – human or AI – can only hope to capture a small part of that complexity.

The widespread use of AI in risk management and financial supervision may increase systemic risk. There are four reasons for this.

1. Looking for Risk in All the Wrong Places
Risk management and regulatory AI can focus on the wrong risk – the risk that can be measured rather than the risk that matters.

The economist Frank Knight established the distinction between risk and uncertainty in 1921. Risk is measurable and quantifiable and results in statistical distributions that we can then use to exercise control. Uncertainty is none of these things. We know it is relevant but we can’t quantify it, so it is harder to make decisions.

AI cannot cope well with uncertainty because it is not possible to train an AI engine against unknown data. The machine is really good at processing information about things it has seen. It can handle counterfactuals when these arise in systems with clearly stated rules, like with Google’s AlphaGo Zero (Silver et al. 2017). It cannot reason about the future when it involves outcomes it has not seen....MORE

"When Google was training its self-driving car on the streets of Mountain View, California, the car rounded a corner and  encountered a woman in a wheelchair, waving a broom, chasing a duck. The car hadn’t encountered this before so it stopped and waited." 

Things Are Still Touch-and-Go In Farm Country—Kansas City Fed Farm Credit Conditions

First up, a major piece from the Omaha World-Herald, December 12:

Continued low prices have some corn and soybean growers trying to figure out how to hang on
Farmers clean up their equipment after harvest each year. This year, some are also polishing their résumés.
The situation facing corn and soybean growers has southeast Nebraska farmer Steve Sugden looking for an off-farm job to help support his family. That includes his wife, a schoolteacher; his daughter, a University of Nebraska junior; and twin sons, freshmen in high school.
The low prices farmers are fetching for their crops don’t cover business costs at many operations. Average monthly prices for corn have been below $3.50 a bushel for over a year now; farmers received over $7 and in some cases over $8 in 2012 and 2013.
Meanwhile, Sugden said, family living expenses haven’t come down; just consider the cost of car insurance for those three young drivers, he said.
“The numbers don’t lie,” Sugden said.
Sugden said he has farming in his blood, and he’s not planning a farm sale, choosing to keep the land his family owns free and clear instead of using it as collateral on a loan to pay for next year’s farm operations.
Sugden said he’s at a crossroads.
He has company. Eastern Nebraska farm auctioneers said they aren’t seeing an uptick in farm sales driven by financial stress, but farm finance experts said Corn Belt farmers and their bankers do face another winter of tough decisions if they want to renew operating loans and keep farming next year.
Farm income plunged for three straight years from 2014, and only a slight uptick is expected this year, the U.S. Department of Agriculture said in November. Crop revenue continues to fall, however. Livestock sales are growing.
The picture can be dramatically different from one farm to the next, depending on the size of the farm, the type of crops or livestock raised, the climate and soil, marketing decisions and financial fundamentals.
As many as half of farmers and ranchers are profitable this year, Nebraska Farm Bureau economist Jay Rempe said. About a third are breaking even, he said, and the rest are “really struggling.”
Across the nation, the median farm income will drop, to a loss of nearly $1,100 in 2017. In other words, most farms will lose money.
And next year, belt-tightening is likely to continue, as economists’ forecasts call for no meaningful increases in crop prices.
One of the reasons for stagnant low prices: There is a glut of grain on the market. And U.S. farmers this coming spring are expected to plant still more acres than they did this year of corn and soybeans. Competition also is growing globally. So there’s no reason to expect prices to rise, economists say....

And from the Federal Reserve Bank of Kansas City:

Greetings of the Season From Société Générale's Albert Edwards

Via ZeroHedge:

Albert Edwards: "Clients No Longer Care About Overvaluation; They Are Concerned About Triggers And Timing"
"How do you know when an asset price rise has turned into a bubble?" That's the rhetorical question Albert Edwards leads his today's Global Strategy Weekly note, and responds that in the non- virtual world, valuation is often a good starting point. "A bubble can also be identified by the steepness and persistence of any price ascent as doubters and naysayers are swept away in a tidal wave of bullish froth." We showed as much two days ago when we brought our readers the latest chart from Convoy Investments, showing that Bitcoin has now surpassed "Tulip Mania" in terms of sheer exponential price action.
Well, according to Edwards, it's not just bitcoin: the vertical price ascent "is something the main S&P Composite Index certainly shares with Bitcoin." And whereas one justification for the surge in stocks is a profits recovery, Edwards counters that "the underlying profits recovery looks increasingly fragile and indeed on some key measures a rapid deceleration is underway. With equities over-valued, overbullish and now also over-bought, the boring old profits cycle still needs watching."

All of which brings us to a fundamental question: why do people avoid bubbles in the first place if - by definition - so many other investors are actively buying them up?

According to Edwards, the answer is that "investors generally agree, usually through hard experience, that to avoid or short an asset class on perceived overvaluation alone is to risk buying a one-way ticket to ruin. That is not to say that investors do not accept the basic investment principle that the primary determinant of long-term investment returns is their entry valuation. But as we have seen with equity markets in recent years, the market can stay expensive and irrational far longer than most investors can stay solvent, or indeed longer than most investment managers can retain their jobs in the face of underperformance."

Which brings us once again to a point that Citi touched on in June, namely that every asset class iw now a bubble as "the principal central bank transmission channel to the real economy has been... lifting asset prices." That has required continuous CB balance sheet growth. Meanwhile, as financial markets scramble to maximize every last ounce of what central bank impulse remains, we get such bubbles as London real estate, bitcoin and vintage cars, or as Citi puts it: "the wealth effect is stretching farther and farther afield." Yes, bitcoin is merely one of the side-effects of the biggest bubble in history that central banks have blown over the past decade.
And here Stanley Druckenmiller was spot on when he told CNBC on Tuesday that the bitcoin bubble will burst - as all bubbles eventually do - but only after the far bigger central bank bubble has also finally exploded and central planning is no more:

Druckenmiller: Bitcoin, art, wine, equities, credit, you name it. everything is one way up and there are huge distortions taking place, and it’s all in the name of this 2% inflation target. And when you get a misallocation of resources, it really hinders growth over the longer term.

Going back to Edwards, he naturally agrees with Druckenmiller and Citi, and explains that clients are forced, as Chuck Prince of Citigroup famously said, to keep dancing while the liquidity mood music is playing. In fact, the SocGen strategist claims that "clients are no longer concerned with overvaluation; they are more concerned about timing and triggers."

But instead of focusing on Bitcoin, Edwards then once again takes aim at his favorite nemesis - equities - and notes that "two key additional indicators to undermine the equity market have now fallen into place."

As well as overvaluation, we showed recently that the extreme bullishness currently prevailing among professional advisors has not been seen in markets since (just before) the 1987 crash. In addition, amid all the focus on the parabolic rise of Bitcoin it has gone almost unnoticed that, following its rapid ascent, the main S&P Composite index is now most overbought since 1995 (see chart below). Yet even this overvalued, overbullish and overbought market might not be enough to unleash the dormant bear (see for example, John Hussman?s excellent writings).


Thursday, December 14, 2017

Here Come the Bitcoin Margin Loans

Not formal margin loans but not subject to Reg. T either!
This is a magnificent scam in the making, nothing new, just magnificent.

From Bloomberg:
These Guys Want to Lend You Money Against Your Bitcoin
  • Lenders offer loans with digital coins as collateral
  • ‘I would be very interested in doing this,’ Bitcoin Jesus says
The woes of an early bitcoin investor. Until recently, people who paid virtually nothing for the virtual currency and watched it soar had only one way to enjoy their new wealth -- sell. And many weren’t ready.

Lenders on the fringe of the financial industry are now pitching a solution: loans using a digital hoard as collateral.

While banks hang back, startups with names like Salt Lending, Nebeus, CoinLoan and EthLend are diving into the breach. Some lend -- or plan to lend -- directly, while others help borrowers get financing from third parties. Terms can be onerous compared with traditional loans. But the market is potentially huge.

Bitcoin’s price hovered around $17,000 much of this week, giving the cryptocurrency a total market value of almost $300 billion. Roughly 40 percent of that is held by something like 1,000 users. That’s a lot of digital millionaires needing houses, yachts and $590 shearling eye masks.“I would be very interested in doing this with my own holdings, but I haven’t found a service to enable this yet,” said Roger Ver, widely known as “Bitcoin Jesus” for his proselytizing on behalf of the cryptocurrency, in which he in one of the largest holders.

People controlling about 10 percent of the digital currency would probably like to use it as collateral, estimates Aaron Brown, a former managing director at AQR Capital Management who invests in bitcoin and writes for Bloomberg Prophets. “So I can see a lending industry in the tens of billions of dollars,” he said.

One problem is that bitcoin’s price swings violently, which can make it dangerous for lenders to hold. That means the terms can be steep.

Someone looking to tap $100,000 in cash would probably need to put up $200,000 of bitcoin as collateral, and pay 12 percent to 20 percent in interest a year, according to David Lechner, the chief financial officer at Salt, which has arranged dozens of loans.

That’s in line with interest rates for unsecured personal loans. The difference is that putting up bitcoin lets people borrow more.
The new loans should be of particular interest to miners, whose computers solve complex math problems to obtain new coins and help confirm transactions, Brown said. They have to pay for electricity and equipment. But, like many bitcoin believers, they don’t like to sell their crypto. Bitcoin startups also need cash to pay employees.,,,MORE

With Janet Yellen's Impending Departure, A Look At Asset Prices

From StockCats:

Note the date.

We May Have Found An Actual " for carrying on an undertaking of great advantage, but nobody to know what it is”"

Stock Cats is focused on Bitcoin this month. Should you visit be forewarned, it is difficult to stop scrolling.

That Time The National Security Agency Invented Bitcoin

One of the commenters in the post immediately below, "The FT's Izabella Kaminska Explores the Numéraire and Why It Matters" mentions the fairly widely known fact that the NSA is the home of the SHA-2 (Secure Hash Algorithm 2) and includes the link.

There is another link that I think is even more interesting, this one hosted at:
The link goes to:

Anonymous: Fried, Frank got NSA's permission to make this report available. They have offered to make copies available by contacting them at <21stcen""> or (202) 639-7200. See:

Received October 31, 1996

With the Compliments of Thomas P. Vartanian
Fried, Frank, Harris, Schriver & Jacobson
1001 Pennsylvania Avenue, N.W.
Washington, D.C. 20004-2505
Telephone: (202) 639-7200


Laurie Law, Susan Sabett, Jerry Solinas
National Security Agency Office of Information Security Research and Technology
Cryptology Division
18 June 1996

1.1 Electronic Payment
1.2 Security of Electronic Payments
1.3 Electronic Cash
1.4 Multiple Spending
2.1 Public-Key Cryptographic Tools
2.2 A Simplified Electronic Cash Protocol
2.3 Untraceable Electronic Payments
2.4 A Basic Electronic Cash Protocol
3.1 Including Identifying Information
3.2 Authentication and Signature Techniques
3.3 Summary of Proposed Implementations
4. 1 Transferability
4.2 Divisibility
5.1 Multiple Spending Prevention
5.2 Wallet Observers
5.3 Security Failures
5.4 Restoring Traceability


With the onset of the Information Age, our nation is becoming increasingly dependent upon network communications. Computer-based technology is significantly impacting our ability to access, store, and distribute information. Among the most important uses of this technology is electronic commerce: performing financial transactions via electronic information exchanged over telecommunications lines. A key requirement for electronic commerce is the development of secure and efficient electronic payment systems. The need for security is highlighted by the rise of the Internet, which promises to be a leading medium for future electronic commerce.

Electronic payment systems come in many forms including digital checks, debit cards, credit cards, and stored value cards. The usual security features for such systems are privacy (protection from eavesdropping), authenticity (provides user identification and message integrity), and nonrepudiation (prevention of later denying having performed a transaction) .

The type of electronic payment system focused on in this paper is electronic cash. As the name implies, electronic cash is an attempt to construct an electronic payment system modelled after our paper cash system. Paper cash has such features as being: portable (easily carried), recognizable (as legal tender) hence readily acceptable, transferable (without involvement of the financial network), untraceable (no record of where money is spent), anonymous (no record of who spent the money) and has the ability to make "change." The designers of electronic cash focused on preserving the features of untraceability and anonymity. Thus, electronic cash is defined to be an electronic payment system that provides, in addition to the above security features, the properties of user anonymity and payment untraceability..

In general, electronic cash schemes achieve these security goals via digital signatures. They can be considered the digital analog to a handwritten signature. Digital signatures are based on public key cryptography. In such a cryptosystem, each user has a secret key and a public key. The secret key is used to create a digital signature and the public key is needed to verify the digital signature. To tell who has signed the information (also called the message), one must be certain one knows who owns a given public key. This is the problem of key management, and its solution requires some kind of authentication infrastructure. In addition, the system must have adequate network and physical security to safeguard the secrecy of the secret keys.

This report has surveyed the academic literature for cryptographic techniques for implementing secure electronic cash systems. Several innovative payment schemes providing user anonymity and payment untraceability have been found. Although no particular payment system has been thoroughly analyzed, the cryptography itself appears to be sound and to deliver the promised anonymity.
These schemes are far less satisfactory, however, from a law enforcement point of view. In particular, the dangers of money laundering and counterfeiting are potentially far more serious than with paper cash. These problems exist in any electronic payment system, but they are made much worse by the presence of anonymity. Indeed, the widespread use of electronic cash would increase the vulnerability of the national financial system to Information Warfare attacks. We discuss measures to manage these risks; these steps, however, would have the effect of limiting the users' anonymity.

This report is organized in the following manner. Chapter 1 defines the basic concepts surrounding electronic payment systems and electronic cash. Chapter 2 provides the reader with a high level cryptographic description of electronic cash protocols in terms of basic authentication mechanisms. Chapter 3 technically describes specific implementations that have been proposed in the academic literature. In Chapter 4, the optional features of transferability and divisibility for off-line electronic cash are presented. Finally, in Chapter 5 the security issues associated with electronic cash are discussed.

The authors of this paper wish to acknowledge the following people for their contribution to this research effort through numerous discussions and review of this paper: Kevin Igoe, John Petro, Steve Neal, and Mel Currie.


We begin by carefully defining "electronic cash." This term is often applied to any electronic payment scheme that superficially resembles cash to the user. In fact, however, electronic cash is a specific kind of electronic payment scheme, defined by certain cryptographic properties. We now focus on these properties....


The FT's Izabella Kaminska Explores the Numéraire and Why It Matters

Numéraire is one of those wonderful words adopted by the English language that, if you google it, will return half the results in French:

Définition académique pour le terme « numéraire » (parue en 1986).
Signification du terme « numéraire », parution de 1932, dictionnaire académique Français.
Ancienne signification développée en 1835 par l’académie Française (ACAD - 1835).
Ancienne signification éditée en 1798 pour le terme « numéraire » par l’Académie Française.

We and Ms Kaminska have been posting on the importance of the concept from time to time for a while now, here's her latest:
Oops, first the definition in English:
An item or commodity acting as a measure of value or as a standard for currency exchange.
Now, here's FT Alphaville: 

Bitcoin’s fractioning problem
Here’s a thought experiment.

If I purchase 0.000,000,001 of a bitcoin from Kadhim for $100, should the value of one bitcoin should now be considered to be $100bn per bitcoin?

If not why not?

Ah, you say. Because the vast majority of buyers would not be prepared to buy for that absurd valuation.

But here’s the thing.

Are the vast majority of “buyers” really prepared to buy for the current valuation? Or are they simply thinking in dollars worth rather than in bitcoin’s worth?

The difference is important.

A valuation should represent the value at which the majority of Hodlers could sell their bitcoin if they decided to sell their holdings all in one go. If this is impossible, the price per bitcoin isn’t really real. It’s just an illusion. The challenges that Hodlers face in cashing out their mega bucks speak volumes as a result. It’s not really real money or realisable wealth if you have to wait weeks or months for liquidation to occur — especially given the volatility of bitcoin.

In that vein, here’s another thought experiment.

If the asset you’re buying is so removed from physical reality, so abstract, does new money flowing in really care what fraction of it it is buying? If not, what’s the constraint on valuation?
In any other commodity market a physically bounded objective usually dominates:...MUCH MORE 
I think Kadhim is in some sort of trouble. Why is Izabella being so nice to him? Why is she valuing his bitcoin at $100 billion? Something's up.

Here's one of our numéraire posts, from April 2013:

"Bitcoin Is No Longer a Currency" 
It never was a currency. It was always quoted as "dollars per Bitcoin" not Bitcoins per dollar".
Swiping a line from the Wikipedia entry for "Numéraire":
...If a store sells 1 can of soup for $1.20, the numéraire is dollars. If the store would buy $1 for 5/6 of a can of soup, the numéraire is cans of soup. Trading a can of soup is simpler than trading fractional cans of soup, so most stores use a numéraire of money, which has fractional units....
The numéraire was the whole point of my comment on the FT Alphaville article "Debunking goldbugs":
Are you quoting rocks per dollar or dollars per rock?
As long as gold is quoted as dollars-per-ounce it is the dollars that are money, not the gold.
Or the Bitcoins....

Cat Bonds/Reinsurance: California Fires Could Force Payouts

From Artemis, Dec 14:

2017 is different for aggregate cat bonds, wildfires a threat: Twelve Capital
Insurance and reinsurance linked investment fund manager Twelve Capital highlights that 2017 is a very different year for aggregate catastrophe bonds, as wildfire risks which typically only contribute a small amount of any cat bonds expected loss could be the peril that tips a number of transaction into paying out.

ILS manager Twelve Capital said that California wildfire damages are, in a typical year, not expected to result in cat bond losses, but 2017 has proven to be different, placing a number of bonds at particular risk.

Before 2017, the largest U.S. wildfire industry loss was recorded at around $4.5 billion, a figure which is likely to be more than doubled by the October wildfires in northern California which are now set for a $10 billion or more loss to insurance and reinsurance interests.

Of the $4.5 billion previous wildfire industry loss record, Twelve Capital said, “Compared to losses caused by major hurricanes, which often exceed USD 10 billion, this is a relatively small amount and hence explains why the expected loss contribution from wildfires in cat bonds is typically minimal.”
As a result, the 2017 wildfires could change the way catastrophe bonds are modelled, in terms of the contribution to expected losses that the wildfire peril contributes, especially for aggregate industry loss trigger deals.

Twelve Capital says that “the situation is different this year” for aggregate catastrophe bonds, as these multi-peril transactions have already seen a substantial amount of the aggregate attachment eroded by events including hurricanes Harvey, Irma and Maria, leaving less of a buffer to protect investors in the notes.

Twelve Capital says that it believes that “the insurance industry’s losses from October’s wildfires are likely to exceed USD 10 billion.”

This is an increasingly widely held view, as evidence emerges in the form of loss estimates for some insurers that appear to suggest the insurance and reinsurance industry loss total could rise further into double-figures.

Add in the losses that the industry will face from the December outbreaks of California wildfires and it’s possible that these aggregate deductibles will be further eroded, placing more of these multi-peril catastrophe bonds that cover wildfires at risk of loss or lowering the chances of a further event during their current risk period causing a loss to the notes....MORE
Also at Artemis, Dec. 13:
California wildfires have now destroyed 1,200+ structures in December

The Waymo Patents at the Heart of the Uber Lawsuit May Be Problematic (GOOG)

From Wired, Dec. 6:

The Spectator Who Threw a Wrench in the Waymo/Uber Lawsuit
Eric Swildens knows how damaging intellectual property trials can be. In 2002, Speedera Networks, the content delivery network he cofounded, was sued for patent infringement and trade secrets violation by Akamai. “It was trial by fire,” says the 50-year-old engineer. “I learned a bunch of stuff I didn’t necessarily want to learn.”

After a three-year battle in which he spent up to $1000 an hour on lawyers, Swildens ended up selling Speedera at a discount to Akamai for $130 million.

The experience left Swildens with a working knowledge of intellectual property battles in the tech world, and a lingering soft spot for others facing hefty patent claims. So when he heard in February that the world’s second-most valuable company, Alphabet, was launching a legal broadside at Uber’s self-driving car technology, he put himself in then-CEO Travis Kalanick’s shoes: “I saw a larger competitor attacking a smaller competitor…and became curious about the patents involved.”

In its most dramatic allegations, Waymo is accusing engineer Anthony Levandowski of taking over 14,000 technical confidential files to Uber. But the company also claimed that Uber’s laser-ranging lidar devices infringed four of Waymo’s patents.

“Waymo developed its patented inventions…at great expense, and through years of painstaking research, experimentation, and trial and error,” the complaint read. “If [Uber is] not enjoined from their infringement and misappropriation, they will cause severe and irreparable harm to Waymo.”
But Swildens had a suspicion. He dug into the history of Waymo’s lidars, and came to the conclusion that Waymo’s key patent should never have been granted at all. He asked the US Patent and Trademark Office (USPTO) to look into its validity, and in early September, the USPTO granted that request. Days later, Waymo abruptly dismissed its patent claim without explanation. The USPTO examiners may still invalidate that patent, and if that happens, Waymo could find itself embroiled in another multi-billion-dollar self-driving car lawsuit—this time as a defendant.

Prosecuting a patent in a lawsuit is a risky business. Patents undergo intense scrutiny during a trial, where many are shown to be poorly written, inapplicable, or even to have been granted in error. But Waymo thought it had a slam dunk for a big patent win. Public records seemed to show Uber using its technology, and an email from a supplier contained an Uber circuit board almost identical to its own lidars....MUCH MORE

Gathering Data: "Who Knows Me Best: Google or Facebook?"

From New York Magazine's Select/all:

Have Silicon Valley’s biggest companies become too powerful? This series examines monopoly and power in the tech industry — and what, if anything, can be done.
Antitrust MeFacebook knows I wear glasses. I mean, it knows exactly what I look like, and can easily identify me in photos. It’s pretty sure I went to summer camp. (I did.) It knows I live in an apartment and if I won the lottery I’d like to fill that apartment with overpriced mid-century modern furniture. And it’s not my only close, intimate friend on the internet. Google can tell you everywhere I’ve been in the last month. It knows what movies I’ve been thinking of seeing. It also probably has a pretty good sense of the state of my immune system.

These companies know me so well because I’ve more or less willingly handed over all this data to them: submitting it to Facebook as profile updates and photos, and to Google as searches on maps or the web. But I’ve always wondered: Which one of my two big, friendly internet giants knows me better?

A big part of what Facebook does with your information revolves around ads, so that was where I started my great data-harvest adventure. The easiest way to find out what the company already knows about you — or thinks it knows about you — is to check out your ad preferences. Facebook says it uses a list of a whopping 98 data points, from what kind of credit card (American Express) you use to whether you commute to work (yes, when the trains actually run) to determining how far you live from where you grew up (about a four-hour drive) to target ads to you. A lot of this is information you probably don’t even realize you’ve given them. Who among us can remember every single page we’ve liked or group we’ve joined?

But the thing I always forget is just how good Facebook is at seeing. Facebook breaks down your preferences into categories, like “hobbies and activities,” “family and relationships,” and “lifestyle and culture.” Some of the topics within mine make sense to me — emoji, women’s rights, hiking. Some of them, uh, less so — natural selection, fuel, Bernie Madoff. Unclear what Facebook would want to sell me with that last one. Still, the things it gets right about me far outnumber the bits of information about me it gets wrong. I’m not a brunette. I have no interest in luxury cars. And I’m genuinely befuddled as to why “masculinity” appears under my education preferences. But I am a left-leaning apartment dweller, an iPhone user, and a journalist, with a birthday in March, who spends too much time on Twitter. (I feel so seen!)

On the other hand, Facebook’s algorithm has a tendency to take things very literally. A shot of a plane’s wing indicates my interest in “wing tips,” but when I clicked to see sample ads for that preference, Facebook had nothing to show me — probably because I was interested in an ad for a pair of menswear-inspired shoes, not aviation. I’m not sure how to take that: Facebook knows I like wing tips, but it doesn’t know what wing tips are. Does that mean it knows me better, or worse?

Google makes it a bit easier to see just how much it’s been surveilling you — the company has a handy “My Activity” dashboard where you can see everything you’ve searched, mapped, listened to or watched....

"The Transaction Costs of Tokenizing Everything"

From Elaine's Idle Mind, October 14:
I wonder if Al Gore ever looks down at us peons, crawling around the internet like eight-legged leeches:
I invented that. I took the initiative in creating the Internet. Now all these freeloaders are using MY internet protocol to drive billions of dollars worth of value. For FREE.
Damn, I should have done an ICO.
Even though Al Gore neglected to tokenize his internet protocol*, someone else came along with the next-best thing. 
In 1999, a clever company called Enron invented something called a bandwidth contract.
The internet is just a bunch of routers and cables, sending and receiving data all day long. Most internet providers have peering agreements, where they carry each other’s traffic for free. Sharing is mutually beneficial, and their customers pay a fixed monthly rate regardless of use.
That’s all well and good when capacity is plentiful, but what happens if half the country wants to stream Sunday Night Football while I’m trying to sync my Bitcoin node? Whose data gets to go first?
Enron’s bandwidth contracts were designed to solve this potential queueing problem. By forcing internet users to bid for bandwidth by the minute, the free market would decide the optimal allocation of resources [1].

Sadly, Enron imploded before it could fully realize its bandwidth trading dream. Still, the idea of turning every network into a market was pretty hot in the dot-com days [2]. To see how things might have turned out, we can look at a company called Mojo Nation.
A MASSIVE AMOUNT OF STORAGE SITS UNUSED IN DATA CENTERS AND HARD DRIVES AROUND THE WORLD. Let your hard drive shit out money by fulfilling storage requests on the open market!

Such is the marketing pitch of services like Filecoin, Sia, Storj, MaidSafe, and all those other decentralized file storage tokens. Seventeen years ago, their founders were still in diapers when Mojo Nation launched to address the problem of Pareto-inefficient data storage.
Mojo Nation created a digital payment system to buy and sell computational resources. Participants could earn Mojo tokens by contributing things like disk space, bandwidth, CPU cycles. Those who wanted resources offered bids in outgoing requests. Mojo tokens relied on a centralized mint because blockchains weren’t around yet, but centralization was the least of its problems: Tokens were a huge distraction from what users really wanted to do, which was share files [3].

A bidding market is an awfully complicated thing. Take Bitcoin, for instance. Each block has a finite capacity, so participants submit transaction fees to incentivize miners to include their transactions....MORE

Wednesday, December 13, 2017

"Justice Department confirms criminal probe in Uber case"

First up.
A recent letter from the U.S. Attorney's office confirms the Justice Department has opened a criminal investigation connected to allegations that a former Uber executive stole self-driving car technology from a Google spin-off to help the ride-hailing service build robotic vehicles.

The letter unsealed Wednesday by a federal judge marks the Justice Department's first acknowledgement of the probe....MORE
And from's The Recorder:

Unsealed Now: Letter from Federal Prosecutors in Waymo v. Uber
Read the letter the U.S. attorney’s office sent alerting Judge William Alsup to an explosive allegations a former Uber employee had passed to an in-house Uber attorney. The letter was unsealed and posted publicly Wednesday in the Waymo v. Uber docket.

U.S. District Judge William Alsup, who is overseeing Waymo’s autonomous car trade secrets showdown with Uber, on Wednesday unsealed a letter the U.S. attorney’s office sent alerting the judge to explosive allegations a former Uber employee had passed to an in-house Uber attorney.... 
...MORE, including the letter.

Interpreting the Yield Curve: Counterintuitive Stimulative Effects of Rate Hikes

The writer, David Andolfatto is Vice President of the Federal Reserve Bank of St. Louis.
Views should in no way be attributed to the Federal Reserve Bank of St. Louis, or to the Federal Reserve System.
Neither should the blog be taken as an endorsement of the fashion sense of the Federal Reserve Economics Data clothing line:
The FRED Team

Posted in FRED Announcements
From Macromania, Nov. 27:

Interpreting the yield curve
There's been a lot of talk lately about the flattening of the yield curve, what's causing it, and what it portends. In this post, I describe a simple "neoclassical" theory of the yield curve and ask to what extent it serves as a useful guide for our thinking on the matter.
Let's start by defining terms. Let I(m) denote the yield (market interest rate) on (say) a U.S. treasury bond with maturity m. So, I(1) denotes the yield on a one-year bond and I(10) denotes the yield on a ten-year bond. The slope (S) of the yield curve is given by the difference in yields between long and short bonds. In this example, S = I(10) - I(1).

Here's what the yield curve looks like for the U.S. since 1961.

Normally, the slope of the yield curve is positive. But occasionally, it turns negative -- an event that is called yield curve inversion. Market analysts care about yield curve inversion because the event is frequently (though not always) followed by a recession (the shaded bars represent recessionary episodes).

The graph above plots the nominal yield curve. Economists frequently stress the importance of real (inflation adjusted) interest rates, which I will denote R. Because there is a ten-year Treasury-Inflation-Protected Securities (TIPS), we have a market-based measure of R(10). Let me compute     R(1) = I(1) - P(1), where P(1) denotes expected year-over-year inflation. Let me use the year-over-year change in core PCE inflation as my measure of P(1). That is, I am assuming that over the short-run, the market expectation of inflation is roughly last year's core (trend) inflation rate. Since TIPS data is only available since 2003, here is what we get:
The nominal and real yield curve share the same broad pattern. This is consistent with what we would expect if inflation expectations are stable. Note the slight bump up in the nominal yield curve following the November 2016 presidential election. Since then, both yield curves have been flattening--the real yield curve more so than the nominal curve. Does this mean we are heading for recession, or at least a growth slowdown? And if so, why? 
Postscript 11/27/2017 Some further thoughts. ***********************

Consider a world where real economic growth remained constant, i.e., y2/y1 = y3/y2 = y4/y3 = ...
In such a world, the yield curve would be perpetually flat. In a world where output fluctuated around a constant trend, the slope of the yield curve would be zero on average. (I am abstracting from inflation risk, etc.)

In reality, the yield curve is usually positively sloped. It seems unlikely that the explanation for this is that investors are perennially bullish (in the sense of expecting accelerated growth). There are other factors that may impinge on bond yields at different horizons and hence on the slope of the yield curve. One such factor is the liquidity premium attached to short-maturity debt. If the short bond in the model above is valued for its liquidity (and if liquidity is "scarce" in a well-defined sense that I don't have room to explain here), then the market yield of the short bond will be lower than what is dictated by "fundamentals." In other words, short bonds will seem very expensive. If this is the case, then the yield curve may be positively sloped even if the growth outlook is stable (instead of bullish).

To the extent that the Fed can influence the liquidity premium on bonds (and there is good reason to believe it can), then raising the policy rate in the present environment would serve to diminish the liquidity premium on bonds. In the model economies I know of where such a liquidity premium exists, eliminating it actually stimulates economic activity. This is because liquid bonds, to the extent they are used as exchange media, actually complement investment spending instead of crowding it out (as is the case in other models that abstract from the liquidity services that bonds provide).

The interpretation in this case is that raising the policy rate is reducing "financial repression," which is likely to offer modest stimulus. This policy action in itself will have no measurable impact on inflation and the associated flattening of the yield curve is what we would expect if growth prospects remain stable (the flattening yield curve does not necessarily portend recession).

Markets and Waterfowl: Machine Learning Is Only As Good As the Training It Receives

"When Google was training its self-driving car on the streets of Mountain View, California, the car rounded a corner and
encountered a woman in a wheelchair, waving a broom, chasing a duck. The car hadn’t encountered this before so it stopped and waited."
Hell, I'd stop too.
From Quartz:

AI does not have enough experience to handle the next market crash
Artificial intelligence is increasingly used to make decisions in financial markets. Fund managers empower AI to make trading decisions, frequently by identifying patterns in financial data. The more data that AI has, the more it learns. And with the financial world producing data at an ever-increasing rate, AI should be getting better. But what happens if the data the AI encounters isn’t normal or represents an anomaly?

Globally, around 10 times more data (pdf) was generated in 2017 than in 2010. This means that the best quality data is also highly concentrated in the recent past—a world that has been running on cheap money, supplied by central banks through purchases of safe securities, which is not a “normal” state for the market. This has had a number of effects, from causing a rise in “zombie” firms to creating generational lows in volatility to encouraging unusually large corporate buybacks (pdf).

With so much data residing in this era, AI might not know what a “normal” market actually looks like. Robert Kaplan, the president of the Federal Reserve Bank of Dallas, recently pointed out some of the market extremes that exist today. The essay included a caution that growing imbalances in the economy could increase the risk of a rapid adjustment....MUCH MORE