StreetEYE Blog

Why Blockchain Is (Mostly) Useless

Cryptocurrencies are useless. They’re only used by speculators looking for quick riches, people who don’t like government-backed currencies, and criminals who want a black-market way to exchange money.Bruce Schneier

The column from which this quote is lifted is well worth reading and thinking about. The key feature of cryptocurrencies and blockchain apps is the distributed nature of the ledger, the idea that you can trust the system without having to trust anyone in the real world.

But eventually, the system has to interact with the real world, where you do have to trust somebody to accomplish anything, and have meta-governance of the system as a whole in the real world. In the real world, there is no such thing as a ‘trustless system.’

And once you have to trust someone, in nearly all cases you might as well build a much simpler and more effective centralized system with an appropriate trust model.

That statement is a bit abstract, so here is a constructive example of how one can offer the key advantages of blockchain and distributed ledger technology, without blockchain.

AWS now offers a centralized immutable ledger, an append-only database similar to the blockchain’s immutable ledger history of all transactions. The difference is, unlike the Bitcoin blockchain, which is distributed among all the Bitcoin miners and uses the consensus algorithm to resolve any conflicts and commit updates permanently, the centralized immutable ledger has one endpoint that processes requests and commits valid updates in the order they are received.

Any sufficiently advanced database system has a language, like SQL stored procedures, to extend it with more complex business logic. Let’s add a language to our centralized database. Suppose the language is Python. And suppose we make the codebase immutable. You can add new code, but you can’t delete any old code.

In other words you can create a Python app, like a restaurant reservation service with a REST API, or a Web UI, and write code that supports all the necessary operations, like add_restaurant, add_diner, add_reservation, cancel_reservation, etc. And then once you publish the app, that code can never be changed.

If that sounds weird, imagine you store the code in an append-only structure, the way Git version control keeps every version ever committed. And every time you commit a new version of the code, the immutable ledger, where it stores restaurant data, is forked. So the app is immutable in the sense that you can create new versions which inherit the underlying data, but the old version always remains.

Now Bitcoin runs as long as it has enough miners to keep the ecosystem going. For argument’s sake, suppose our hypothetical AWS service adds says, the app users split the hosting bill, and users commit to some maximum monthly cost, $1, $10, whatever, and AWS keeps it running as long as the cost of hosting is covered.

There could be more complex models, premium tiers, profits directed to the developers or a foundation, but the key points are: no one can extract rents beyond the hosting cost or what is initially agreed; no one can change the software unless everyone migrates to the new fork; and AWS is the trusted third party that guarantees that.

The way this is similar to blockchain is that once an app is published, it can’t be changed unless all the users agree to fork it. If the developers write something new, they can publish it, but as long as any users keep using the old system, it’s still around and no one can force anyone to change.

The way it’s different from blockchain is that you have to trust AWS. And pay them instead of running distributed mining rigs. And running one centralized server is far simpler, cheaper, more efficient, you can have instantaneous transactions at a high rate for low cost.

A key benefit of a blockchain app is elimination of certain agent-principal problems. Back in the day there was an open-source app called CDDB. You put a CD in your PC, and CDDB would read it, look up a hash in a remote database and give your audio client all the metadata, artist, album titles, song titles. And if your CD wasn’t in the master database you could contribute metadata and everyone else could use it.

Then one day the developer cashed out and sold the database for a few million. All the work that everyone had done maintaining the open source database was captured, and the people who had created it now had to pay license fees to access it. Classic bait-and-switch: gain lock-in on false promise of a free service, make it painful to switch all the apps that used the CDDB API, then extract maximum rent.

With our immutable application architecture, that debacle could never happen. Everyone could just keep using the old version of the database. No one can ever start charging for access to the data, or change the deal à la Darth Vader, because no one individual has control. And that’s the primary benefit of blockchain for a lot of use cases. It’s an open source cloud architecture, anyone can improve on it and fork it, but no one person can control it or fence in the commons.

But eventually, you have to trust somebody where the ‘trustless’ part of the system interacts with the real world. The minute you are trading with a broker/exchange, or buying something with Bitcoin, you have to trust that the exchange won’t get hacked or the seller won’t scam your money, or the restaurant where you made a reservation won’t give your table away or shut down.

Logically, if you trust someone like Amazon for e.g. payments/fulfillment at the edge of the system, then you lose nothing extending trust to having them run the blockchain infrastructure, and then you lose nothing if they build a high-performing centralized system with the same functionality but without all the blockchain hassles.

As long as you trust AWS to be an honest broker and charge only reasonable hosting, and never try to extract the value of the underlying app, you don’t really care about distributed ledger vs. centralized ledger, you just want it to work. And centralized will always be simpler and have better throughput than distributed; it just works better. And if you can trust AWS and the real-world legal system, you can write a contract and governance structure that provides the benefits of a distributed ledger.

Blockchain is almost always useless. The exception is where you can’t trust AWS. More to the point, where you can’t trust the government. If you want to create an app that the government may want to block, collect taxes on, enforce capital controls, protect government monopolies, censor forms of expression, then you can never trust a central nexus. The government can show up and seize servers, shut it down and go after the users.

But the government can maybe block distributed ledger services too. China has shown that they can do a pretty good job of policing the decentralized Internet. Few things are so decentralized that a strong government can’t drive it deep underground. The US banned private gold ownership in 1933 and it was mostly effective. People could still have gold necklaces and maybe trade with them, but without coins and bars it was no longer feasible to store and exchange value on a large scale. If the motivation exists, governments can crack down on anything.

In my opinion Bitcoin will never be a legitimate, largely unregulated medium of exchange for the masses in the US. There’s too much at stake for the government not to control it. It’s too important for the US government to maintain control of the payments system for taxation, seignorage (ability to print money), financial stability (FDIC, intermediaries that don’t go bust because someone hacked and made off with the money), and national security (i.e. leveraging the world’s dominant currency for political purposes).

And the US is still a very high-trust society, so there’s not a lot of reason for most people to use a black-market type of payment system, without the benefit of the government to enforce it and backstop intermediaries with e.g. FDIC insurance.

In a nutshell, the US government’s technical ability and motivation to regulate Bitcoin (or Libra or whatever) exceeds the average consumer’s ability and motivation to circumvent the US government.

But this isn’t true everywhere. You can draw a 2×2 matrix:

  Strong State
Able to repress crypto
Weak State
Unable to repress crypto
High trust society
Low demand for crypto
USA
Europe
Japan
??? Island paradises ???
Micronesia?
Bhutan?
Low trust society
High demand for crypto
China
North Korea
Venezuela
Somalia

 

I think only the Venezuelas of the world, with low trust in local currencies and financial institutions, which are also weak states hard-pressed to universally enforce their will, will see significant adoption of Bitcoin. And maybe in many cases gold and USD may be more practical for black markets.

Satoshi Nakamoto’s paper and Bitcoin launch happened in 2008…over 10 years ago. The iPhone was launched in 2007. If blockchain really is a platform that will change everything, it’s a real sleeper success story. Typically you see killer apps on a new platform a lot quicker. Web 1.0 launched Amazon and Netflix within the first couple of years. Where are all the blockchain apps?

The only industries that have really been impacted are ransomware, money laundering, and facilities for exchanging and speculating on tokens.

There are unloved monopoly ledger companies like OpenTable and Ticketmaster. Where has any centralized application actually been disrupted? If distributed ledger technology can’t disrupt OpenTable, what use is it?

My best guess is that blockchain and Bitcoin adoption will remain a curiosity and a niche phenomenon linked to black markets, illicit activities, weak states with unreliable payments and money.

And blockchain apps will migrate to more efficient centralized systems that use governance and trust architectures to offer benefits similar to distributed ledgers…for anyone who has to trust the government.

TL; DR Anything you can do with blockchain you can do better without it. Except maybe in low-trust environments, but if the reason for low trust is strong state enemies, they can probably repress blockchain applications they don’t like. Eventually you have to trust somebody. So might as well pick the right parties to trust, and build applications people love.

Bury it in the desert. Wear gloves. – xkcd

There ain’t no such thing as a free option

I would not give a fig for the simplicity this side of complexity, but I would give my life for the simplicity on the other side of complexity. – Oliver Wendell Holmes

Keep it simple, stupid – Anonymous

This post is motivated by this story of automakers up in arms about Trump deregulating emissions standards. Surely reducing regulation cannot harm automakers? What would that say about free markets?

Absent the regulatory rollback, manufacturers have to make low-polluting vehicles. If the Feds deregulate, they have a choice of making low-polluting or high-polluting vehicles. And in any optimization problem, removing or easing constraints can only improve an outcome. Why would they prefer an outcome that doesn’t make them better off?

Here’s the rub: California will keep stringent standards. Some manufacturers will produce high polluting vehicles and exit California, where the market will be less competitive and car prices may increase. The market will be split into California-legal and non-California-legal. People on the border will register cars with their friends in Oregon. There will be multiple regulatory regimes and an unholy mess, and the likelihood that future administrations will tinker with it.

It would be better for everyone, including the car manufacturers, if everyone could find a way to agree on the same standard.

Free-market fetishists are going to say, well, that’s an artifact of crazy regulation, a free option can only be a good thing, and if you give people freedom of choice, they will choose the right solution. WRONG! Time for some game theory…

Consider Braess’s Paradox. You have a road network and 4000 cars traveling from START to END over 2 different routes, say on either side of a body of water. Initially, the equilibrium is 2000 cars over each route and a travel time of 65 minutes. (Traffic/100 = 20 minutes + 45 minutes.) It’s a Nash equilibrium because no one has an incentive to switch. If for any reason more people take one route, it slows down and people have an incentive to switch back to the faster route.

(via Wikipedia)

Then, we add a bridge between A and B. Suppose it’s instantaneous to keep things simple. You can even suppose it goes both ways, but no one will choose the 90 minute option.

Initially, it’s faster to take the connection (40 minutes). But eventually, too many people take it. What happens when 3300 people take the new route? The time is 66 minutes, slower than before! And the people going along the other routes are even slower so they switch too and slow it down further! Amazingly, the new Nash equilibrium is 80 minutes, everyone takes the bridge.

  • 4000/100 + 4000/100 = 80 minutes (4000 people)
  • 45 + 4000/100 = 85 minutes (0 people)
  • 4000/100 = 85 minutes (0 people)
  • 45 + 45 = 90 minutes (0 people)

Adding this route option cost everyone 15 minutes. (If you add more people, eventually above 4500 people all the new people switch to the 90 minute route. If you don’t follow or want to know more, read the Wikipedia article, also Evolution of Trust is the most awesomest intro to game theory ever.)

The thing is, changing the rules changes the game, which can change the whole equilibrium. The law of unintended consequences. Letting everyone act freely according to their own best interest does not lead to the best outcome. You also need to set the game up right, with a market design that is engineered to be fit for purpose. (And even then, no guarantees of optimal outcome). In this case, everyone should agree to blow up that bridge between A and B.

There ain’t no such thing as a totally free market. You have to come up with a market design that achieves the desired objectives. If you choose not to decide, and let the market be designed to protect the right of the stronger instead of other objectives, you still have made a choice. There is no spontaneous order except that which sensible people work very hard to engineer. (See also this on Silicon Valley pseudo-libertarianism.)

Apocryphally, Cortés burned his ships so he would not have the option to retreat. The history is more complicated, but eliminating your own options can be a winning strategy. If you are in a game of chicken with a maniac, both of you driving toward each other at 100 mph, the one who throws the steering wheel out the window first usually wins, by credibly eliminating the option to swerve.

In college I took a course from Seymour Melman (Columbia’s Noam Chomsky) where I had to read the nuclear doomsday theorists, Herman Kahn, George Kennan and whatnot. I was a little shook up when I read about Kissinger saying “I would say in retrospect that I wish I had thought through the implications of a MIRVed world more thoughtfully in 1969 and 1970 than I did.” and “In retrospect, I think if one could have avoided the development of MIRVs, which means also the testing of MIRVs by the Soviets, we would both be better off.” You had one job, Henry. You’re not supposed to have a conscience but you’re supposed to understand strategy FFS.

The thing about the MIRV (Multiple Independent Rentry Vehicle) is … suppose each side has 1000 missiles and they have 50% effectiveness. One side launches all its missiles, catches the other side napping… and only destroys 50% of the other side’s missiles, while using all of its own.

Now suppose you have 10 warheads on each missile. The side that launches first destroys all of the opponent’s missiles with only half of its own.

The Nash equilibrium shifts from, ‘nobody launches missiles’, to ‘everyone launch your missiles first, or immediately without fail at any detection of launch from the other side.’

If the schmartest man in the world can screw this up, what hope is there for the rest of us?

You can’t just assume that giving yourself more options is a good thing. You build a bomb because the Nazis might do it first. You use it because it might save millions of lives compared to an invasion. You build a powerful military because the world is a dangerous place, you don’t want Germany and Japan re-arming, and an overwhelmingly superior military in the hands of a stable democracy is a great thing for global security. Then one day you have a Điện Biên Phủ and a commander in chief can be faced a very tough choice. You have to do everything in your power to save American lives, right? Even at the cost of escalation and possible retaliation down the road, right? Or one day a stable Western democracy isn’t quite so stable, you have people in charge who say, what’s the point of having nuclear if you can’t use it to get what you want? A no-first-use commitment seems like a good idea, but then the other side has more tanks … it all gets very complicated.

It pays to simplify.

To make the right move, you have to understand the game, the meta-game, the game beyond the meta-game. You need second-level thinking to succeed, you need to think strategically. You also want positive convexity, situations that that have the potential to go really really well but don’t cost you much downside. (Think the poker equivalent of calling from the big blind with 6-7 suited).

But in poker, in investing, in life, you also need keep it simple, stupid. You are often better off limiting your options. Even if you think you’re the smartest player at the table, you want to avoid marginal situations, where you may have to make a big decision in an unclear situation. And if you’re not the smartest or most confident person at the table, the people who are will force you to make a very tough decision at a time when they have the edge.

Reality is too complex, best-laid plans of mice and men, a pound of principle is worth a ton of guile.

And IMHO Western ethics are mostly strategic thinking carried to their ultimate conclusion. If you are strategic but your goal is in the Kantian sense to do what would be best if everyone did it, strategic thinking is indistinguishable from altruism. In an iterated game, on a long enough time horizon, the most altruistic is the most strategic.

Ethics = strategic thinking + love. If you care so much about your fellow players that their payoff is your payoff, you get to altruism.

In the real world, people are boundedly strategic and boundedly rational, but the more we can build market designs and institutions that unite the two, the better off we will be.

If you want cooperation, security, stability, you need strength but also honesty and clarity of purpose and communication, looking for win-win situations… you don’t go to the mat against friends for small victories. What goes around, comes around. Sow the wind, reap the whirlwind. But that is a topic for another day.

On the end of the StreetEYE experiment. And what I learned about the bias/variance tradeoff in news, social, and life.

The StreetEYE mad science experiment came to an end on 3/31/2019. Many thanks for supporting StreetEYE over the years!

A few people have asked about alternatives.

The closest alternative is Nuzzel. Nuzzel is essentially what Twitter Moments should have been. See the Nuzzel page for TheLinkfest, log in with your own Twitter account, scroll down to ‘news from’.

Or follow some or all the top people @StreetEYE follows (or @TheLinkfest, the short list) – browse them with our visualization tool here – and make your own custom newsfeed.

StreetEYE used an algo to find people to follow, and that part is missing, along with a bit of spam-filtering, but the Nuzzel experience is very good.

A few people asked about the reasons for shutdown. Answer: lack of traction, technical debt, platform shenanigans (sites blocking StreetEYE from spidering/scraping, reducing API limits). It needed a reboot but it really didn’t have enough traction to justify it. Also, the platforms might have killed it off regardless. It was always playing in someone else’s garden.

Why now and not a few years ago? Because I loved the FinTwit community.

I hope StreetEYE let FinTwit find people to follow, learn from, build professional relationships with, and brought a smart audience to the best news stories, publishers and social accounts. Thanks especially to people who reshared and evangelized it.

If StreetEYE let people find news that mattered, credit belongs to the people who created and shared stuff that mattered. If it didn’t, sorry! Regrets to everyone who didn’t like being aggregated, thought it followed the wrong people, blocked me for being a jerk, or otherwise thought it sucked.

I believe it was Clay Shirky who said, if your business model involves big expensive buildings with Greek columns, there’s a good chance you are about to be disrupted. Because fundamentally businesses like that are about trust and an aura of exclusivity, and the Internet allows new social and trust hierarchies.

But a lot of incumbents that maybe seem ripe for disruption aren’t cooperating with that theory: Harvard, Bloomberg, Goldman Sachs.

In fact new entrants are building big trophy buildings, Bloomberg, GS. Web companies are hiring starchitects, paying for naming rights, building brick-and-mortar presence as an attractive customer acquisition proposition.

One of the things Harvard, Bloomberg, GS, YC have in common is they are all essentially social networks, with rites of admission, imprimatur of approval. There’s a lot of proof of work in Eugene Wei’s words, if you are an employee or even a mere paying client.

And they have been disrupted, in the sense that if you look at the people who are there today vs. 30 years ago, they are a lot smarter, harder-working, tougher. A lot more people know how the sausage is made, and don’t just buy something because Merrill said so.

It’s a better world. Some of the monopoly rents may still be there, but a lot of the prestige has gone out, and it’s a lot more competitive. Look at research reports from the early 1980s, good grief.

Social status is a classic bias/variance problem. If you get a Harvard MBA or Ph.D. your proof of work is the ability to jump through academic hoops over the long haul, and look and sound a certain way. That leads to strong biases in the types of people who successfully navigate them, or exploit them, like Elizabeth Holmes.

Disruption, new social networks, allow people who don’t fit the mold rise to the top. But things can go to the other extreme. When you have social network ‘influencers’ who do it all on their own, sometimes they have a lot to offer, sometimes very little.

For a functioning society, you want status indicators and ‘proof of work’ that allow new people to rise to the top. But institutions also have to select people who work hard, have skills, decency, judgment. Criteria developed over centuries, like nobility, or all their white male ancestors went to Harvard, aren’t fit for purpose when the world is rapidly changing.

But there’s definitely an adverse selection problem that social networks haven’t really figured out, how to make the good stuff rise to the top, filter the noise, and also let new voices be heard. And that’s something I tried to work on.

Marty Baron who edits the Washington Post is a 1000x curator. He’s a rare breed, and there are a lot of great curators out there. Social media doesn’t really reward the great curators. On Reddit, if you’re first to post something that gets traction, you get a lot of karma. You don’t get any karma for voting early for quality winners. And quality curators don’t get more influence.

We have an Idiocracy problem, and 0.1x curators drown out the Marty Barons with cat pictures. Or worse, Jacob Wohl or the Infowars guy with manufactured viral nonsense.

(I hasten to add, Reddit lets good stuff float to the top and kills a lot of bad stuff and has proved resilient where its Usenet ancestor collapsed. But toxoplasma of rage ends up being what it’s famous for.)

Anyway, I think that’s a big challenge, and Ray Dalio is onto something important. We need to figure out what real trust looks like in the social media age. We need better filters that identify quality content and curators. And I think StreetEYE actually worked pretty well most of the time.

Another challenge is the user experience. Being ‘too online’ is a little crazy-making.

There’s too much stuff. There’s a time dimension, a topic/context dimension, and a quality dimension. I think this UX trilemma screws it all up. On Reddit, if you post a brilliant reply deep in a thread, it gets buried despite being high quality. And on Twitter, the algorithmic feed surfaces tweets out of context when they get a lot of engagement. Sometimes they get totally misunderstood and ratioed, or worse. So the ideal tweet becomes this self-contained ‘fortune cookie’. The medium truly is the message and people adapt to it. And in a lot of cases it’s completely crazy-making, toxic. We are building a dystopia just to make people click on ads.

Anyway, software is eating the world, and a lot of the social experience is mediated by these devices, and we will need to get better at figuring it out. AI is amazing and algorithmic feeds seem to be how the world is going.But I wonder if it might be time for some of those weird sci-fi 3D user interface visualizations to navigate social, and the massive amounts of online info and complex relationships.

Looking forward, at one end of the spectrum you have crypto, which is kind of about making do without trust. I think it’s useful in some cases, like Venezuela, or some coordination problems where you don’t want a central authority and all the agent-principal problems that go with it. But mostly I think the idea of a trustless society is completely dystopic.

At the other end you have a surveillance state, where we are all watched over by central machines of loving grace. Social combines the worst of 1984 and Brave New World. We don’t just have Siri and telescreens in our bedrooms, we carry them everywhere and are addicted to them. And then the algos rank our ‘social credit.’

I think innovation that finds great content and people, connects them and builds high-trust communities is possible. It made FinTwit possible. But I think we kind of messed it up. I have no doubt the next iteration, maybe Social 2.0, will be better than the last.

If you want to build something great, the first step is to build something that sucks a little. I think that’s true for StreetEYE, and for social in general. May all our next iterations be better!

Mostly, many thanks for being part of this experiment, and look forward to continuing to interact in other ways online, and in person!

How I learned to stop worrying and love quantitative tightening

Many people are talking about ‘quantitative tightening’ and ‘balance sheet reduction’, and some people are blaming it for market volatility, discussed here, here, here.

IMHO, blaming balance sheet reduction for market volatility is cargo cult mumbo jumbo.

What is QT? During the crisis, a lot of bad loans were made and a lot of risky bonds got issued. When the market had its Minsky moment in 2008, everybody simultaneously realized they had too many risky assets and needed to adjust portfolios. When everyone tried to sell at once, prices overcorrected. Worse, there was the risk of a debt-deflation spiral, where the financial crisis tanks the economy, making even more mortgages and bonds unsound, in turn further worsening the crisis and tanking the economy.

Driving base rates to zero seemed inadequate to the task of restoring equilibrium. So the Fed stepped in with quantitative easing: buying the securities no one else wanted to own, taking toxic assets off of private sector balance sheets onto its own balance sheet. Effectively out of the system.

By pegging overnight rates at 0, committing to keeping them at 0 for an extended period, and buying at the long end, the Fed was expanding the zero interest rate policy (ZIRP) down the yield curve.

Today, when the Fed has stopped doing QE, every day the portfolio on the balance sheet shortens by a day, and some of it rolls off of the short end.

When a bond on the Fed’s balance sheet matures, it’s (sort of) the reverse of QE, so some people call it quantitative tightening. (A more exact opposite of QE would be selling long-term securities into the market.) The maturity is a forced sale of an overnight obligation. The maturing bond used to be a long-term instrument, but now it has rolled down the yield curve so it’s short-term. The Fed gets cash, the bond obligation is canceled.

It’s a little like when a bond matures in your brokerage account, broker calls and ask what you want to do.

Maybe you say, roll it into a T-bill or money market fund, which is the most neutral thing to do.

Technically, when the bond matured, it changed your positioning.

But the maturity didn’t constrain your position at any time, you could offset it any way you wanted, you could sell it, swap it. Rolling into another short-term position would be the most neutral action.

It’s the same for the Fed. In the Fed’s case the maturity drained liquidity. The market lost a cash asset when it paid off the bond, and lost a corresponding cash obligation when the bond was paid off.

The maturity reduces the Fed’s balance sheet. But doesn’t constrain monetary policy in any way. The Fed pegs base rates like Fed Funds. Every day it can add or drain liquidity to keep short rates close to target.

So, to sum up: QT is like a scheduled sale of a short-term security.

And short rates are pegged.

So QT doesn’t affect short rates. The Fed will do whatever is necessary to maintain the peg, including adding liquidity to offset any drain from QT.

And QT doesn’t really affect long rates.1

Monetary policy affects the market and the economy through interest rates, which tell you everything you need to know about liquidity and expectations. How else would it work? If it’s not impacting rates, it’s not impacting policy, the economy, the stock market.

So why are people hand-wringing about QT? It’s nonsense. Voodoo economics.

What do QT hand-wringers want the Fed to do, not let the drain happen at the short end of the curve? Indeed, the Fed doesn’t necessarily drain, because they peg the short end of the curve. They will end up buying back whatever is needed to maintain the peg, adding liquidity.

And there are $1.5t in excess reserves, and via interest on excess reserves, any bank can increase or reduce deposits to the Fed at a fixed rate. If the Fed does nothing to offset the drain when a bond matures, all that happens is excess reserves get drawn down. (Bank customer who issued the bond instructs the bank to pay the Fed. Bank instructs the Fed to debit their excess reserve account at the Fed. Deposits go down, excess reserves go down.)

A (partially valid2) criticism of QE was that if the Fed adds liquidity, but people just park it back at the Fed in the form of excess reserves, it doesn’t impact the real economy. The same applies when the process is reversed. If the Fed drains liquidity, but people just draw down their excess reserves, it doesn’t impact the real economy.

Do QT hand-wringers want the Fed to buy at the long end? That’s ridiculous, the yield curve is flat. It would be nutty to be tightening and simultaneously doing QE.

Do the QT hand-wringers think Fed tightened too much and should ease? There they may have a point, but that has absolutely nothing to do with balance sheet reduction and QT per se. For any given tightening posture, it’s essentially a technical matter whether the Fed implements it by automatically rolling stuff off the short end of the balance sheet, or automatic adjustments via excess reserves and IOER, or discretionary open market operations at the short end.

The Fed statement responding to market concerns about the balance sheet reduction and QT seemed a little like telling the market ‘oh yes dear, anything you say’ but just doing same thing they’ve been doing all along.

It seems like some board members thought they had to say something but there’s nothing to say, so they’re like ‘ok fine whatever’.

I think Timaraos in WSJ and Duy on Bloomberg are on the money. There are always talking heads blaming something for market gyrations. If it’s not QT, then it’s ETFs, risk parity, algos. It’s just mumbo jumbo to explain movements that are pretty stochastic. And QT is not really a thing, except to the extent that a technical side effect of policy normalization is balance sheet reduction.

The worst part is, a lot of the QT hand-wringers are free market fetishists who were probably complaining about QE in the first place. They sure seem to think the market is pretty effing fragile… OMG QE! OMG QT! Algos! Risk parity! ETFs! They are are messing with the function of the free market! If the market has a cow and can’t deal with this crap and has a crisis every 10 years, maybe it’s not all it’s cracked up to be.

The market runs on bullshit. It goes up, people find a reason. It goes down, people find a reason. Today, QT is the story it fixates on, but it’s mostly bullshit.

I believe in complementarity, the idea that you can’t necessarily have one deep consistent model of reality, possibly the best you can do is many shallow models that are applicable in overlapping circumstances.

But it’s still pretty impressive that markets work as well as they do most of the time, when so many people are mostly applying simplistic heuristics that don’t reflect underlying reality.

I could write a series a la Penn Jillette’s “Bullshit!” and go all Lewis Black/Andy Rooney on why easing does not cause deflation, there is no such thing as a chronic safe asset shortage, and MMT is not really a new thing.

But I won’t. Just stay off my lawn!

1Suppose the bond that matures is a 10-year, and the issuer floats a new 10-year. If you have a lot of new issuance, that would pressure long rates. Hence the policy of allowing at most $50b per month to mature without replacing it at the long end.

2QE pushed rates down at the long end. By pegging overnight rates at 0, committing to keeping them at 0 for an extended period, and buying at the long end, the Fed was pushing ZIRP down the yield curve. QT doesn’t directly impact rates at the long end, although maturing bonds maybe be refunded with new issuance. Given that the yield curve is flat, and credit spreads pretty normal, seems like no big deal.

The Top 100 People To Follow For Financial News On Twitter, January 2019

It’s been more than a year since we posted our last list of people to follow on Twitter for financial news. Time for an update!

Without further ado, here is this year’s list (click on headers to re-sort):

Rank Screen Name Name Influence Score Relevance Score Order by Topic
1 pdacosta Pedro Nicolaci da Costa 6.78 7.39 149
2 TheStalwart Joe Weisenthal 10.0 2.48 122
3 ReformedBroker Downtown Josh Brown 8.87 2.99 87
4 ritholtz Barry Ritholtz 7.68 4.1 86
5 felixsalmon Felix Salmon 8.82 2.73 470
6 Noahpinion Noah Smith 5.12 6.07 310
7 Frances_Coppola (((Frances Coppola))) 3.64 7.48 49
8 hblodget Henry Blodget 6.43 4.59 252
9 crampell Catherine Rampell 5.26 5.34 338
10 sdonnan Shawn Donnan 3.23 6.94 366
11 moorehn Heidi N. Moore 3.96 6.06 401
12 paulkrugman Paul Krugman 6.91 2.44 358
13 M_C_Klein Matthew C. Klein 7.45 1.74 333
14 davidmwessel David Wessel 5.96 3.22 364
15 matt_levine Matt Levine 8.36 0.81 121
16 tracyalloway Tracy Alloway 8.45 0.62 123
17 IvanTheK Ivan the K™ 5.78 3.29 263
18 edwardnh Edward Harrison 4.01 4.75 146
19 Nouriel Nouriel Roubini 6.29 2.42 183
20 BCAppelbaum Binyamin Appelbaum 6.98 1.7 351
21 carlquintanilla Carl Quintanilla 5.19 3.33 261
22 acrossthecurve across the curve.com 0.92 7.53 131
23 MarkThoma Mark Thoma 5.09 3.27 317
24 AdamPosen Adam Posen 4.18 4.07 295
25 JustinWolfers Justin Wolfers 6.75 1.48 356
26 jennablan Jennifer Ablan 5.48 2.75 162
27 elerianm Mohamed A. El-Erian 5.85 2.15 151
28 tomkeene tom keene 7.09 0.87 148
29 CardiffGarcia Cardiff Garcia 6.57 1.31 332
30 jasonzweigwsj Jason Zweig 6.28 1.56 114
31 delong Brad DeLong 🖖🏻 5.2 2.62 345
32 ObsoleteDogma Matt O’Brien 6.6 1.23 355
33 greg_ip Greg Ip 6.67 0.94 107
34 RobinWigg Robin Wigglesworth 5.5 2.1 5
35 JacobWolinsky Jacob Wolinsky 2.29 5.31 238
36 TimOBrien Tim O’Brien 2.94 4.63 442
37 izakaminska Izabella Kaminska 7.34 0.1 178
38 tylercowen tylercowen 5.62 1.8 307
39 NateSilver538 Nate Silver 6.52 0.87 344
40 jbarro Josh Barro 4.7 2.65 339
41 jessefelder Jesse Felder 2.04 5.23 130
42 ezraklein Ezra Klein 6.0 1.26 340
43 EddyElfenbein Eddy Elfenbein 4.16 3.05 138
44 johnauthers John Authers 6.08 1.13 1
45 TabbFORUM TabbFORUM 0.2 7.0 42
46 AmyResnick Amy Resnick 2.32 4.75 394
47 activiststocks Activist Stocks 0.81 6.13 198
48 eisingerj Jesse Eisinger 4.77 2.14 435
49 TimHarford Tim Harford 4.37 2.47 14
50 Neil_Irwin Neil Irwin 6.1 0.71 350
51 Jesse_Livermore Jesse Livermore 4.69 1.97 89
52 carney John Carney 5.72 0.91 265
53 mims Christopher Mims 🎆 3.46 3.13 448
54 conorsen Conor Sen 3.96 2.61 142
55 lisaabramowicz1 Lisa Abramowicz 4.32 2.16 128
56 JohnCassidy John Cassidy 3.92 2.55 441
57 economistmeg Megan Greene 5.12 1.34 292
58 Austan_Goolsbee Austan Goolsbee 4.67 1.78 357
59 lopezlinette Linette Lopez 2.75 3.7 264
60 prchovanec Patrick Chovanec 2.2 4.22 279
61 mark_dow Dow 5.23 1.18 262
62 DLeonhardt David Leonhardt 5.72 0.65 354
63 niubi Bill Bishop 2.35 3.89 275
64 interfluidity Steve Randy Waldman 4.4 1.84 304
65 karaswisher Kara Swisher 3.55 2.68 453
66 tomgara Tom Gara 2.45 3.74 375
67 Convertbond Lawrence McDonald 5.48 0.64 135
68 AlephBlog David Merkel 2.13 3.96 129
69 SamRo Sam Ro 3.9 2.19 137
70 katie_martin_fx Katie Martin 4.51 1.54 23
71 JimPethokoukis James Pethokoukis 3.59 2.44 368
72 EpicureanDeal TED 3.05 2.97 411
73 TimDuy Tim Duy 3.18 2.83 144
74 abnormalreturns Tadas Viskanta 4.78 1.22 94
75 ianbremmer ian bremmer 4.95 0.92 284
76 rortybomb Mike Konczal 3.92 1.94 303
77 matthewstoller Matt Stoller 1.79 4.07 378
78 georgemagnus1 George Magnus 3.87 1.98 281
79 Alea_ JC Kommer 1.87 3.96 170
80 ModeledBehavior Adam Ozimek 3.55 2.24 325
81 PekingMike Mike Forsythe 傅才德 1.21 4.57 287
82 kadhimshubber kadhim (^ー^)ノ 2.16 3.59 27
83 morningmoneyben Ben White 3.89 1.83 428
84 NinjaEconomics Ninja Economics 2.17 3.54 312
85 BaldwinRE Richard Baldwin 2.45 3.25 297
86 valuewalk ValueWalk 3.49 2.2 197
87 LaurenLaCapra Lauren Tara LaCapra 2.17 3.52 404
88 ryanavent Ryan Avent 5.04 0.63 331
89 D_Blanchflower Danny Blanchflower 3.28 2.36 150
90 danprimack Dan Primack 3.97 1.67 475
91 cullenroche Cullen Roche 4.58 1.03 88
92 scottlincicome Scott Lincicome 1.11 4.46 367
93 MikeIsaac rat king 2.58 2.87 454
94 jmackin2 James Mackintosh 4.81 0.64 17
95 LorcanRK Lorcan Roche Kelly 4.93 0.51 67
96 alex in Providence alex 0.72 4.72 477
97 MattGoldstein26 Matthew Goldstein 2.65 2.79 431
98 AnnPettifor Ann Pettifor 2.52 2.88 50
99 modestproposal1 modest proposal 3.91 1.48 243
100 rodrikdani Dani Rodrik 4.26 1.12 318

A word about methodology:

1) Start with a few highly-followed accounts, e.g.: pdacosta, TheStalwart, ReformedBroker, ritholtz, felixsalmon

2) Determine who they follow! Traverse the Twitter graph using the API.

3) From these 5 people you can get a pretty great starting list:

  • About 14,700 users
  • But only 112 users who are followed by all 5, or 4 out of the 5

4) That would be a great place to start, but we can do a little better:

  • Cull the non-financial users, like darth or <insert political figure>
  • Iterate and create a new Twitter graph starting from the users remaining

In general, from a given list of users, get a better list by

1) First expanding it, by finding who these users follow

2) Then ranking and filtering the new list by

  • Influence: how many people in the list follow them, and recursively how influential they are (PageRank)
  • Relevance: how frequently they post financial content (financial sites, tickers, topics, and recursively, items that hit the StreetEYE frontpage)
  • Timeliness: how often they are first to post something that later gets popular

Iterate a few times, and you get a pretty good list of people to follow.

In the past I’ve generated a graph of the users, and this year I really went ham on it and created this magnificent beast:

If you click here you can explore the graph interactively:

  • Roll over and get detailed info on each FinTwit personality
  • Word cloud (roll over)
  • 3 most similar accounts, using topic analysis of what they post about, who they share same URLs as, who they follow and are followed by. P.S. I LOVE THIS FEATURE!
  • Each user’s most frequently shared domains, hashtag, tickers, other FinTwitterers they mention
  • Who they follow/are followed by (roll over ‘followed by’/’followed’)

I hope this helps everyone find great new FinTwit BFFs to follow.

It’s always a bit arbitrary, where to cut off people who aren’t relevant. Some people may find the influential tech or political accounts a bore, but I try to find a balance.

The biggest problem is churn. There are some people who are highly followed who don’t really post relevant stuff any more. There are people who are pretty relevant but it takes a very long time to break through and get influential. I could expand the panel, but the more you expand it the more the common denominator is… all Trump all the time.

Then, I guess I could use topic analysis to try to downvote Trump and politics… use noise cancellation to determine what is popular out in the broad population and penalize it … but it’s turtles all the way down the rabbit hole.

That’s it! If you’re looking for top blogs for your daily list or Feedly reader check out the July listicle of the most shared financial blogs.

Not to be too thirsty but if you like it don’t hesitate to share!

(Never say never again, but very probably never doing this graph again. Fun mad science, but disproportionately time-consuming. Twitter makes the API more restrictive every year, knucklehead sites block me, the graveyard is full of algorithmic news apps and news bots. C’est la vie!)

What I Learned From Watching The Sting And Reading David Maurer

Wall Street never changes, the pockets change, the suckers change, the stocks change, but Wall Street never changes, because human nature never changes. – Jesse Livermore

Confidence men are hardly criminals in the usual sense of the word, for they prosper through a superb knowledge of human nature; they are set apart from those who employ the machine-gun, the blackjack, or the acetylene torch. – David Maurer

There are old-time classics every investor should read: Reminiscences of a Stock Operator by Edwin Lefèvre, Extraordinary Popular Delusions and the Madness of Crowds by Charles Mackay. Maybe Theory of the Leisure Class, The Way We Live Now, Only Yesterday.

We can add David Maurer’s 1940 The Big Con. It’s an incredibly rich sociological and linguistic study of the underworld of grifters and marks in their heyday, and the basis for the all-time great movie The Sting.

Wall Street is the land of castles in the air. Every few years, you get a con job seemingly as brazen as The Sting: John Law, Gregor MacGregor, Charles Ponzi, Ivar Kreuger, Tino De Angelis, Eddie Antar, Bre-X, Barry Minkow, Jordan Belfort, Bernie Madoff, Sam Israel, Allen Stanford, the list goes on. And those are just the big ones; small-time schemes and vanilla accounting scandals are a dime a dozen.

Let’s review the elements of the big con as exemplified by The Sting, and see if it teaches us how to avoid becoming victims. 1

The Mark:

Every con needs marks, of which there is no short supply. The keys to being a good mark are overconfidence and greed.

You think you are a better judge of investments and people than most. You think you deserve something for nothing.

You put unwarranted trust in people who look and act the part (authority, social proof), who like you and flatter you (liking).

You may be pretty clever, but you think you are a little more clever than everyone else. Perhaps not perfectly honest and amenable to a little chicanery, willing to cut a corner to make a buck.

Religion can make people more vulnerable. You believe the supernatural holy man or psychic, you think God has granted you this heaven-sent opportunity. Faith can go hand-in-hand with trust of authority, affinity scams. You think you are more deserving than the wicked, an instrument of vengeance against the evil casino, bookmaker, or Rothschild conspiracy.

Lack of self-awareness and ability to give in to strongly motivated reasoning makes you more vulnerable. When you really need the money, when you had a life-changing event that causes you to not think clearly, that helps.

Marks are typically older, typically men. A high status bumpkin, maybe. But no one has a monopoly. Maybe a salaryman with a gambling problem, who thinks he deserves the good life more than his bosses. Maybe a woman with too much faith in the good in people and her intuition about them.

Brains don’t immunize you. Marks con themselves. The smarter you are, the more ability you have to conceive a complex narrative and make yourself believe it. The more confidence in your understanding of the world and attachment to the positive outcome (greed), the more vulnerability to exploitation.

“There’s a mark born every minute, and five to trim him and five to knock him.” Knock, meaning try to warn him. Usually unsuccessfully, because you can’t knock a good mark. Marks have gone to court to defend the con artist who has taken their money, say he was set up, innocently vilified, the authorities messed up a good thing.

The Grifters:

The stars of the show are the roper and the inside man.

The roper locates and researches well-to-do victims. (Puts the mark up.) The roper gains the victim’s initial confidence and passes him to the inside man for a percentage. (think, the sketchy broker and the fraudulent CEO).

Good con men have elements of the dark triad: narcissism, psychopathy, Machiavellianism. (Via Maria Konnikova’s The Confidence Game, a thorough study of the psychological elements of the con.)

Narcissism: Grifters get off on putting one over on everyone, showing they are superior and everyone else is a mark.

Psychopathy: Inability to feel empathy for the marks. But a con man needs enough empathy to get inside the mark’s head, to relate, and to fake it.

Above all, Machiavellianism: It’s a dog eat dog world and it’s just being realistic that everyone is doing their best to fleece everyone else. People just play the hand they were dealt as best they can to survive. Don’t hate the player, hate the game. Never mind that they are playing completely outside the rules of civilized society and spreading misery. The marks probably had it coming.

Psychopaths and posers abound in legitimate business, con men just go the distance. Maurer: If confidence men operate outside the law, it must be remembered that they are not much further outside than many of our pillars of society who go under names less sinister. They only carry to an ultimate and very logical conclusion certain trends which are often inherent in various forms of legitimate business.

The Hook:

The key to a good hook is to understand what will motivate the mark into a frenzy of greed. Doyle Lonnegan’s Five Points insecurity, false pride, and ruthless drive for revenge are his undoing.

You see that fella in the red sweater over there? His name’s Donnie McCoy. Works a few of the protection rackets for Cunnaro while he’s waiting for something better to happen. Donnie and I have known each other since we were six. Take a good look at that face, Floyd. Because if he ever finds out I can be beat by one lousy grifter, I’ll have to kill him and every other hood who wants to muscle in on my Chicago operation.

Billie (Eileen Breenan) picks Lonnegan’s pocket and takes his wallet. Gondorff (Paul Newman) cheats Lonnegan at cards and embarrasses him in front of his peers. Then Hooker (Robert Redford) returns the empty wallet.

This hook is genius:

– Losing and being unable to pay off the bet humiliates Lonnegan, puts him in a vulnerable position.
– Hooker returning the wallet, ostensibly against his own interest, restores order, flatters him, establishes trust.
– Lonnegan’s first reaction is a rage to kill Hooker and ‘Shaw’ (Gondorff), but Hooker’s payback plan hooks him (natch).
– Birds of a feather: Hooker pretends to be from Five Points, like Lonnegan. Someone from the old hood wouldn’t con you, you’re familiar with their ways and can tell they are straight, right?

If you want to hook someone, do them a favor, like returning a wallet out of the goodness of your heart (reciprocity). Even better, let them do you a favor, like showing mercy and not killing you. If I’m helping you, it’s because you’re worth it, right? Once I do a small favor, I’m vulnerable to a slightly bigger ask (consistency and commitment). That’s why the subway panhandler makes a ludicrously tiny ask, like a dime, a nickel, or penny. You don’t want to be the cheapest bastard, right? And now you’re hooked.

In for a dime, in for a dollar. The hook just needs to get the mark to the next stage of the con.

The Rope, The Tale:

The rope is where the roper steers the hooked mark to the inside man, who tells the tale, a great story, a sure thing, so they rush to the inevitable conclusion on their own, or so they believe. Make the mark feel like he is in control.

Show the mark a way to get what he wants most. Show them the shady clerk and Western Union office where the tips generate. Make them see it’s a foolproof plan. In The Sting, Hooker and Gondorff share roper/inside man duties. In the classical, archetypical big con, the roper happens to know a guy with a foolproof plan, who takes over. And then we’re all in this together, sticking it to the real bad guy.

The Convincer:

The pump-and-dumper lets the early suckers take some profits, to rope them into even bigger investments, get them to tell their friends. When you let the mark make a small score to set up the eventual touch, that’s the convincer. In The Sting, they have to do it twice to convince Lonnegan. The second time, he demands to pick the time, and they don’t have the money, so they stall him at the window until he misses the post time. Sometimes a near miss excites you more than a win, which is why slot machines show a lot of near-jackpots.

The Big Store:

For the convincer, you move the mark to the big store, a casino, bookie, brokerage, a place that puts the mark a little outside his comfort zone, gives the con men the home field advantage, gets the mark emotional and fired up. There is noise and confusion, time constraints (scarcity). Buy now because it will double tomorrow! There is social proof – other people making huge bets and winning big. The 3-card monte dealer has a crowd, a shill or two, placing bets and making money. The mark wants to show he’s a big shot, that he knows the lingo, that he can run with the big dogs.

The Breakdown, The Send:

Next thing you know, it’s the mark’s idea to bet half a million on Lucky Dan to win. This is the breakdown: negotiating how much the mark will get fleeced for. The small con just takes you for everything you have on you. The big con takes you for everything you can scrape together. The difference is the send, getting the mark to cash in, to beg, borrow or steal all they are good for.

Taking Off The Touch:

Finally, the mark gets fleeced, but somehow in a way that has plausible deniability. It was all just bad luck! Or it was the mark’s fault, like Lonnegan betting on the horse to win instead of place.

The Blow-Off:

After the flim-flammers have the mark’s bankroll, they need put distance between themselves and the mark before he gets wise. They need to distract him so he doesn’t know what just happened. Your broker got sick, got called away on business and he can’t return phone calls. The boss or the authorities won’t let him do anything for you, the victim. In The Sting, it’s having the law on everyone’s tail after a shootout.

Plausible deniability is the best blow-off. If the loss were just bad luck, or the mark’s fault, betting to win instead of place, the mark may still think the grifters are their best friends, and go back to raise more money for a second touch.

The Fix:

The fix is how the grifters immunize themselves from the law. In the old days, paying off the local cops to give the mark a runaround was a standard trick of the trade.

Cons where the mark does something outside the law make it hard for them to go to the cops. A con where the mark does something stupid, like paying a psychic, cheating on his wife, buying worthless junk, makes him reluctant to go public, because it costs him valuable reputation in real life.

Conclusion:

Via Maria Konnikova: our need to believe, to embrace stories that explain our world, is as pervasive as it is strong. We’ve done most of the work for them. We want to believe in what they’re telling us. Their genius lies in figuring out what, precisely, it is we want, and how they can present themselves as the perfect vehicle for delivering on that desire.

Trust is evolutionarily favored. Humans have evolved to operate in and identify with groups, Ayn Rand notwithstanding. Societies with more trust function better, experience more economic growth. Evolution has programmed us to learn heuristics that make us feel confidence in certain situations where it may be unwarranted.

People who can fool themselves into thinking things are for the best function better, are more disciplined, start and finish more projects. Athletes who can visualize success win more medals. And people are predictable, reasonable, good…most of the time.

If you think you are not susceptible to being conned, you are probably wrong. It’s like being hacked, it can happen to the best of them. And if you really are relatively invulnerable to the con, you may be less trusting than is optimal. You can’t play your friends like marks, or expect them to act like con men. If you lose, sometimes them’s the breaks. Better to sometimes be cheated than never to trust.

Probably no one is immune, given the right motivation, circumstances, and an expert con artist. But demanding good independent due diligence, heeding red flags, knowing the history, knowing the elements of persuasion and the tricks of the trade, and above all self-awareness to slow down when you are getting carried away, will make most con artists look for better marks. A Madoff isn’t going to pitch a Warren Buffett. He’s going to pitch the guy who thinks he deserves the Warren Buffett return without going the extra mile.

The con is an evolutionary counter strategy, a game theory exploit that works if people are more trusting than is warranted. The cuckoo that deposits eggs in another bird’s nests, the insect that mimics ant queen noises so ants take care of them, are nature’s con artists.

There’s a natural balance, where there is enough trust to make society work, and enough skepticism to limit con artists’ ability to destroy civilization.

In the heyday of the big con, the world was small, communities were built on a large degree of trust, they didn’t communicate with each other, there was no central FBI. The big con thrived.

In a strange way, the Internet has given the edge back to some con artists. Communities have splintered. Water-cooler and stoop interactions that used to be private are now exposed to interlopers. By connecting people in a ‘global village’, gossip and rumor travel fast, some social dynamics have returned to the old days of pitchfork mobs and public shaming.

Photoshop, even audio and video, can be faked. So, are we doomed to a world of perpetual grift and fakery, and diminished trust? Yes and no.

Yes, because there has always been grift. As long as there are marks with a little too much trust, and con men with the psychology of the dark triad, there will be abuse. There is never full immunity.

But no, because tools evolve, people evolve. Remember when we thought the Internet meant the spread of democracy and color revolutions everywhere? Now we get right-wing populism in democracies, and always-on digital surveillance in autocracies and mafia states.

The system hasn’t developed immunity from fake news fueling extremism, not because immunity is impossible (although perfect immunity is), but because we haven’t really tried yet, by identifying the markers of what fake news looks like, and bad actors who spread it, the difference between viral extremism and astroturf fakery.

The natural balance between marks and grifters has been upset. Over time, we’ll evolve the tools and the skepticism to restore the balance. One hopes, without losing our freedom or grip on reality. In the meantime, be wary.

Referenced books (and a couple more)

The Big Con: The Story of the Confidence Man, by David Maurer

The Confidence Game: Why We Fall for It…Every Time, by Maria Konnikova

Influence: The Psychology of Persuasion, by Robert B. Cialdini

The Grifters, by Jim Thompson

Money: A Suicide Note, by Martin Amis

Grifter movies. (I would add The Last Seduction).

The Most Shared Financial Blogs 2018

The list:

VoxEUsubscribe
ZeroHedgesubscribe
FiveThirtyEightsubscribe
World Economic Forum
Project Syndicate
Brookingssubscribe
Marginal Revolutionsubscribe
ValueWalksubscribe
ForexLivesubscribe
Seeking Alphasubscribe
coindesksubscribe
PIIEsubscribe
Dealbreakersubscribe
The Reformed Brokersubscribe
A Wealth Of Common Sensesubscribe
Calculated Risksubscribe
Bruegelsubscribe
The Big Picturesubscribe
mainly macrosubscribe
CATO Institutesubscribe
Visual Capitalistsubscribe
Real Investment Advicesubscribe
StockTwits
Cointelegraphsubscribe
Brad DeLongsubscribe
CityLabsubscribe
The Irrelevant Investorsubscribe
American Economic Association
AEIsubscribe
Washington Center for Equitable Growthsubscribe
Royal Economic Society
Stumbling and Mumblingsubscribe
CEPR
The Motley Fool
Alpha Architectsubscribe
Conversable Economistsubscribe
Marc to Marketsubscribe
ProMarketsubscribe
Collaborative Fundsubscribe
AVCsubscribe
FN London
The Incidental Economistsubscribe
EconLog
Investopedia
CFA Institute
Evonomicssubscribe
Pragmatic Capitalismsubscribe
Farnam Streetsubscribe
Howard Lindzonsubscribe
A Dash of Insightsubscribe
Economist’s Viewsubscribe
Benzingasubscribe
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Bespoke
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Stratechery
OilPrice.com
Tax Policy Centersubscribe
Mercatus Center
NIESR
CCN
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globalinequalitysubscribe
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About once a year I’ll post the top Twitter accounts to follow. It’s a fun piece of social media analytics, and I’ll try to do it again later this year, after seeing if I can sidestep Twitter’s efforts to cut me off.

One thing I never did before is post the most popular sites shared by the contributor panel. So here it is, three different ways.

At the bottom is a raw dump of the 1000 most shared sites, with the emphasis on raw. It includes all your most popular mainstream media sites, social media sites like YouTube and Instagram, official government primary sources, service providers, a lot of sites that are not strictly financial blogosphere.

Above that are the sites that most often hit the front page, either via the AI filter or your faithful human editor.

Finally, at the top of this post is probably what’s of most interest, the 150-odd most shared financial blogs, the raw dump filtered by ‘arbitrarily considered by me to be a financial blog.’

I took out the media sites, popular social media platforms, tech blogs, service providers. I left the ones that are primarily economic, financial, market commentary, and ‘generally considered to be part of the financial blogosphere and not mainstream media.’

I left out the top tech blogs, but left in Stratechery and Electrek, which are must-read if you follow e.g. Apple and Tesla. I left in some aggregators that are pretty popular in the financial blogosphere but left off others that are maybe less central or closer to mainstream media. Maybe some others are questionably finance-adjacent and I couldn’t bring myself to delete because I thought they are must-read. It’s necessarily a little arbitrary but hopefully not too arbitrary.

But if you are so inclined, you can peruse the full 1000 most shared and make your own list of favorites to follow or add to your RSS reader.

Thanks to all my friends who make the fin-social-twit-blogosphere still interesting and crazy after all these years. I hope this helps you find some new folks to follow and helps you gain some new fans.

Any suggestions, complaints, let me know.

 

Top 500 Most Front Page Appearances


Bloomberg   New York Times   Wall Street Journal   Financial Times   Washington Post   Reuters   Vox   Politico   The Guardian   CNBC   Business Insider   CNN   BuzzFeed   The Economist   Axios   Recode   BBC   The Atlantic   Medium   New Yorker   Quartz   MarketWatch   The Telegraph   Daily Beast   NY Post   Associated Press   The Hill   New York   Project Syndicate   Federal Reserve   NBC News   Huffington Post   The Verge   Fortune   Vanity Fair   Wired   A Wealth Of Common Sense   Forbes   European Union   The Reformed Broker   TechCrunch   LA Times   Yahoo   ZeroHedge   YouTube   Slate   Foreign Policy   The Times   SEC   The Intercept   Marginal Revolution   The Independent   FiveThirtyEight   New York Fed   Dealbreaker   USA Today   The Onion   Barron’s   mainly macro   Vice   NBER   NPR   The Hollywood Reporter   PIIE   ProPublica   Brookings   Mail Online   The Irrelevant Investor   IMF   CBS News   Gizmodo   coindesk   Technology Review   Variety   Stumbling and Mumbling   HBR   Collaborative Fund   Mother Jones   A Dash of Insight   Time   Google   Go   Visual Capitalist   LAWFARE   Ars Technica   Council On Foreign Relations   NY Daily News   National Review   South China Morning Post   ValueWalk   Calculated Risk   Institutional Investor   McClatchy   Bank of England   Econbrowser   Page Six   AVC   Columbia Journalism Review   Talking Points Memo   Washington Center for Equitable Growth   The White House   US DOJ   World Economic Forum   Stratechery   Newsweek   Washington Examiner   Sky   St. Louis Fed   ThinkProgress   Alpha Architect   Boston Globe   Digiday   Fast Company   Economist’s View   Bruegel   Pragmatic Capitalism   Bank Underground   Poynter   Miami Herald   Brad DeLong   Conversable Economist   Nieman Lab   The Information   SSRN   Fox News   ProMarket   Gallup   LSE   Prospect   Business Wire   Facebook   Noahpinion   Gawker   Rolling Stone   Fox Business   The Grumpy Economist   Morningstar   Nikkei   Macro and Other Market Musings   The Week   CBO   The New York Review of Books   documentcloud.org   Musings on Markets   longandvariable   San Francisco Chronicle   STAT   New Republic   Of Dollars And Data   Deadspin   Fusion   VentureBeat   Evening Standard   ETF.com   BIS   Breitbart   Pew Research Center   ICIJ   FactSet   Marketplace   City A.M   International Business Times   Federal Reserve Bank of San Francisco   CityLab   ABC News   Reason   GQ   Tim Harford   SnappyTV   U.S. House of Representatives Permanent Selection   Scientific American   The Cut   Backchannel   NY Observer   The Spectator   ESPN   The Motley Fool   efinancialcareers   Harvard   The Investor’s Field Guide   Real Investment Advice   Haaretz   CFA Institute   Chicago Tribune   Inc.com   The Nation   Tax Policy Center   Handelsblatt   MacroMania   PIMCO   Talking Biz News   Toronto Star   Monday Note   AQR   The Big Picture   FN London   Naked Capitalism   AEI   Globe & Mail   The Sun   www.gov.uk   Mediaite   U. of Oregon   Roger Farmer’s 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Top 1000 Most Shared Sites


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Economist 1843   Dropbox   shanghaiist.com   globalinequality   fitchratings.com   ajc   Condé Nast Traveler   Infowars   Washingtonian   TinyLetter   The National Interest   Jalopnik   Popular Science   Our World in Data   end-times-synapse.blogspot.com   Abnormal Returns   Artsy   sagepub.com   Noahpinion   oecd.org   Global Times   Hot Air   ICIJ   medcitynews.com   Merriam-Webster   pri.org   Cornell   due.com   The Wire   Morning Consult   Crain’s New York Business   Reductress   Patreon   The Overspill   EW.com   xkcd.com   Middle East Eye   Political Wire   VOA   The Investor’s Field Guide   Breaking Energy   The American Prospect   Pitchfork   Haas News   Longreads   chinadaily.com.cn   Chicago Sun-Times   The Awl   DataViz   CapX   minneapolisfed.org   CDC   The Fiscal Times   Grub Street   Bon Appetit   Teen Vogue   Capitalogix   Daily Business   advisorperspectives.com   insidehighered.com   UPI   Yonhap   FactSet   theregister.co.uk   War on the Rocks   JSTOR Daily   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The “Spygate” outrage

‘Freedom is the freedom to say that two plus two make four.’ – George Orwell

When Trump tries to brand the swirling swampy maelstrom around him as ‘Spygate’, it gives the game away. If you get busted by the cops with Russian entanglements in your campaign, one way to go is to claim ignorance and that it was somehow a misunderstanding or above board.

But if you’re saying the counterintelligence guys, the media, the people don’t even have the right to ask questions about Russian operatives, you doth protest too much. The clear implication is that the answers to those questions are really really bad. It’s like a perp saying “I was framed!”

Tell it to the judge.

What would you say FBI counterintelligence does? They look into efforts by Russian operatives to gather information and create mayhem in the US. So, here are some Russia/Trump contacts:

  • Papadopoulos (pled guilty): told Australian High Commissioner (ambassador) in the UK that Russians had Hillary’s emails and were going to share them to help Trump. And when that actually happened, Aussies said, hmmh, that’s interesting, briefed the FBI, and the investigation started. (Also numerous other Papadopoulos contacts with Russian agents, Mifsud, a ‘relative’ of Putin’s, etc.)
  • Manafort (indicted): in deep with Oleg Deripaska and Ukrainians, laundered money like it was going out of style.
  • Cohen (about to be indicted unless he’s singing like a canary): Emailing with Putin’s office about building Trump Tower Moscow (when he’s not paying off ‘a hundred’ Trump affairs and probably abortions, and taking massive bribes from likes of Novartis, UAE, KSA, and a sketchy Russian oligarch, and using the funds to benefit Trump via the hush-money slush fund.)
  • Sessions: many Kislyak meetings, which he lied about to Congress
  • Donald Trump Jr.: Veselnitskaya meeting about ‘adoptions’ a/k/a lifting sanctions in return for campaign assistance. Meeting with Russian NRA money-funneler. Claim that Russians are funding Trump’s properties. Numerous contacts with Wikileaks
  • Roger Stone: numerous contacts with Wikileaks, made specific requests and predictions about what they would release when
  • Carter Page: ’nuff said
  • Kushner: Kislyak meetings, secret meeting with VEB bank right after election to establish ‘back channel’ to Putin
  • Flynn (pled guilty) negotiations regarding Obama’s sanctions, i.e. undermining US foreign policy before Trump took office
  • Erik Prince in secret negotiations in Seychelles

Are you really saying the FBI isn’t supposed to look into this and get informants to discretely ask questions? If not, what would you say they are supposed to do around here? Not ask any questions that are embarrassing to the President because he is above the law?

How Russians meddled is a question of overriding national importance. If you can’t answer reasonable questions about it from the FBI, Mueller, the press, and the American people, you clearly cannot operate effectively as President (as evidence has repeatedly shown).

Between the giant Cohen bribes, the likelihood of Cohen either flipping, or his files incriminating Trump, and Trump directly asking Sessions to oversee the Russian inquiry after he recused himself, which is prima facie obstruction, the wheels have fallen off. We know how this is going to end. It blows my mind that Trump thinks that just repeating ‘Spygate’ will obscure that, or that ~40% of Americans approve of this half-wit wannabe mobster shitshow.

Weaponizing reporters’ superhuman good faith and desire to appear fair, in order to make relentless lying and manufactured insane bullshit appear credible, and exploiting supporters’ unwarranted and unreciprocated loyalty to destroy truth and institutions, is not going to save you from truth and judgment.

Losing the meta-game

I suggest a new strategy, R2. Let the Wookiee win.C3PO

Jeff Bezos tells a story about how he gave his grandmother a hard time about smoking until she cried, and his grandfather took him aside and said, “You are going to figure out one day, that’s it’s harder to be kind than to be clever.”

Interestingly, Bill Gates has a similar story about being taken to a psychologist as a child because he was being disruptive. He told the psychologist that he thought it was unfair that he had to live by illogical rules and he deserved to win the argument with his parents. The psychologist told him kids always win in the end because they outlive their parents and he should cut his parents a break.

The lesson here is: don’t focus so much on winning the game that you forget the meta-game. And there is always a meta-game. Don’t miss the big picture. And there is always a bigger picture.

The dumb bear runs away when you jangle your keys. The smart bear gives you a look that says, you idiot, those are just keys. And keeps coming back messing with your trash cans and your car and your house. And the smart bear gets shot.

There’s a saying on Wall Street, “always leave something on the table.” The casino will do their level best to kill you at the tables, but they’ll do it with a smile and a free drink and give you 30% back in comps to keep you coming. Acting like it’s just a big party and we’re all in this for a good time goes a long way, even for a fundamentally extractive business.

The worst people to deal with on Wall Street (or anywhere) are the ones who aren’t looking to generate more value than they extract, are only looking for an angle, to make maximum profit while blowing up the relationship and moving on.

In the US we’ve become really good at prospering in the short run while losing the meta-game. The financial crisis was a moral crisis: make the liar loans, make them look AAA, hit the numbers, get the bonus. And then the whole thing blows up.

People should think more about the meta-game. Libertarians think, you give people freedom, that solves everything. Left-wingers think the right policy directive can achieve any outcome. It would be better to think more about engineering the meta-game of markets for goods and services, markets of ideas, so they work effectively and maximize freedom.1

Facebook and Google are re-engineering society. But they can’t really admit it. They’re not supposed to measure the consequences their designs have in the real world. There has to be a pretense that it all happens organically and driven by the free choices of the users. Otherwise the right-wingers go nuts that they are being censored and manipulated. And the left-wingers go nuts that corporations are privatizing and monetizing civil society and data.

The founding fathers knew that freedom works only to the extent people are decent, and they had to create a form of government and institutions that allowed society to function while preserving the maximum amount of freedom. We seem to have forgot that and take maximalist positions on freedom and/or achieving objectives regardless of the Constitution, sometimes at the same time. Instead of sophistic rhetoric and partisan talking points, it would be better to start talking about engineering our institutions and decent society, and creating win-win games.

Trump is the king of approaching everything as win/lose and losing the meta-game. You make a lot of seemingly great deals with other peoples’ money, take big bucks risk-free to attach your name to Trump University, then one day you’re broke and exposed as a flim-flam artist. Not having skin in the game makes you the worst kind of dealmaker. And in the long run gives you the worst kind of skin in the game.

You can break the deals you don’t like, make the base happy, undo an Obama signature initiative, get a sweet payoff from Sheldon Adelson and Sheikh MBZ. But you can’t simultaneously pursuing a nearly-identical deal and expect counterparties to trust you and act in good faith). Deport veterans, make a torturer CIA chief, pander to the white supremacists, and maybe you think you’re triggering your opponents and putting them on the back foot, but you’re losing the meta-game.

Acting decently isn’t some rose-colored pie-in-the-sky do-gooder dream world. Ethical conduct is the ultimate meta-game. A lot of US power exisits because we (mostly) tried to act decently, be a beacon of stability, freedom and democracy, create and support multilateral institutions. And we’re killing the goose that laid the golden egg.

So much winning. We’re not getting bored, that’s for sure. But we’re losing the meta-game. Bigly.

1Maybe they do? Maybe a lot of libertarianism is just a meta-game for powerful interests wanting the freedom to rig the game to give them even more power and wealth? Maybe a lot of egalitarianism is a meta-game ivory-tower philosophers leverage to grab power? If so, all the more reason people need to think about the meta-games.

Quantitative Fun With Fund Names

Word cloud

There are a number of hard problems in investing, for instance:

    1) Finding alpha.
    2) Finding clients and assets — especially if you can’t 1) consistently find alpha.
    3) Finding an awesome name for your fund.

The investing blogosphere is all over the first two. Now, for something completely different, we help you with the last one! Inspired by Sloane Ortel’s post, we’ll run some analytics on a dataset of investment firm names, culminating in our very own algorithmic fund name generator.

Assembling data from various sources, scrubbing and deduplicating, we built a set of about 20,000 names.

As a warmup, here are the most frequent words found in company names:

most-popular

The most frequent bigrams or 2-word combinations:

most-popular-bigrams

 

Word2Vec is an algorithm which, given a corpus of text, maps words to vectors of floating-point numbers, magically distilling syntactic and semantic attributes of each word.1 Vector representations of words are used for machine translation, sentiment analysis, intelligent personal assistants like Siri and Alexa, and other natural language processing applications. Word2Vec word vectors can be uncannily accurate in representing meanings and relationships between words.

We could train our own vectors, but this is not a large corpus, and there are many pre-trained vector sets based on the Web, Wikipedia, different languages and corpora. Let’s load the set of vectors trained on Google News, map our frequently used words, and cluster them.

Cluster 0 (522 words)
[‘management’, ‘partners’, ‘co’, ‘services’, ‘global’, ‘international’, ‘research’, ‘new’, ‘national’, ‘mutual’]
Cluster 1 (444 words)
[‘inc’, ‘sa’, ‘hong’, ‘kong’, ‘de’, ‘al’, ‘deutsche’, ‘nv’, ‘ma’, ‘adv’]
Cluster 2 (545 words)
[‘life’, ‘fidelity’, ‘golden’, ‘royal’, ‘legacy’, ‘millennium’, ‘beacon’, ‘fortune’, ‘republic’, ‘heritage’]
Cluster 3 (336 words)
[‘capital’, ‘investment’, ‘asset’, ‘wealth’, ‘investments’, ‘holdings’, ‘fund’, ‘financial’, ‘company’, ‘ag’, ‘funds’, ‘trading’, ‘markets’, ‘investors’, ‘managers’]
Cluster 4 (180 words)
[‘pacific’, ‘north’, ‘creek’, ‘river’, ‘west’, ‘sun’, ‘south’, ‘northern’, ‘summit’, ‘ridge’]
Cluster 5 (150 words)
[‘eagle’, ‘tiger’, ‘lion’, ‘falcon’, ‘wolf’, ‘arrow’, ‘peregrine’, ‘owl’, ‘fur’, ‘fox’]
Cluster 6 (223 words)
[‘street’, ‘hill’, ‘estate’, ‘park’, ‘property’, ‘square’, ‘spa’, ‘lane’, ‘bridge’, ‘road’]
Cluster 7 (656 words)
[‘asia’, ‘uk’, ‘singapore’, ‘canada’, ‘pvt’, ‘japan’, ‘india’, ‘morgan’, ‘europe’, ‘australia’]
Cluster 8 (725 words)
[‘limited’, ‘holding’, ‘the’, ‘first’, ‘real’, ‘point’, ‘us’, ‘blue’, ‘one’, ‘old’]
Cluster 9 (237 words)
[‘alpha’, ‘amp’, ‘matrix’, ‘quantum’, ‘meridian’, ‘sigma’, ‘dimensional’, ‘constellation’, ‘symmetry’, ‘parametric’]
Cluster 10 (106 words)
[‘china’, ‘rock’, ‘stone’, ‘silver’, ‘kg’, ’emerald’, ‘steel’, ‘gold’, ‘diamond’, ‘mill’]
Cluster 11 (207 words)
[‘oak’, ‘tree’, ‘brown’, ‘wood’, ‘cypress’, ‘pine’, ‘harvest’, ‘grove’, ‘cedar’, ‘maple’]
Cluster 12 (1413 words)
[‘llc’, ‘ltd’, ‘lp’, ‘pte’, ‘pty’, ‘corp’, ‘gmbh’, ‘shanghai’, ‘hk’, ‘ubs’]
Cluster 13 (108 words)
[‘harbor’, ‘compass’, ‘banca’, ‘marine’, ‘spinnaker’, ‘anchorage’, ‘shipping’, ‘port’, ‘motor’, ‘mariner’]
Cluster 14 (276 words)
[‘advisors’, ‘group’, ‘advisory’, ‘private’, ‘corporation’, ‘trust’, ‘associates’, ‘counsel’, ‘consulting’, ‘advisers’]

 
We distinguish clusters related to countries, geographical features, animals, trees, minerals and materials, nautical concepts, scientific and financial concepts, positive metaphors like ‘fidelity’ and ‘heritage’.

Plotting popular words from each cluster, and connecting words that are frequently co-located in the same firm names, we get

circle

We can explore related words using Google’s Embeddings Projector, which generates 3D images of word relationships that look like this:

Embeddings projector

  • Click on the link for the embeddings projector, wait for it to load the data.
  • Start entering a word at top right to search for it
  • Select the word you want in the list that gets populated
  • Click “Isolate data points”
  • Drag the image around with your mouse to see related words
  • When you’re done, hit “Clear selection”

It’s a bit like taking a word and algorithmically free-associating similar words that are used in fund names.

Finally, we can train a machine learning algorithm to automatically generate realistic-sounding new names based on our corpus! Use this link or the form below to generate names based on a starting string (or leave blank).

 
Some names may be similar to existing names in the corpus, which are the property of their respective owners.

Other names are a bit random…use them for inspiration for your next corporate entity…or if you need to generate random realistic-looking text for testing purposes or to fool a spam filter. We make no representation about the regulatory compliance, appropriateness, or marketing value of generated names!

The code is here.

We hope this will free valuable time from the fund naming problem to let managers focus on generating alpha.

1 How does Word2Vec work? It’s like a Netflix movie recommendation system, but for words. The Netflix recommender maps each user and each movie to a vector. It tries to find

1. A vector to represent each movie and
2. A vector to represent each user such that
3. When you multiply those two vectors, you get a number that predicts how the user will rate the movie.

As users rate a lot of movies and the system trains and improves the vectors, different vector components start to represent movie features, like action-adventure, rom-com, scifi, etc.

Now to see how Word2Vec assigns vectors to words, substitute the statement ‘user u likes movie m‘ with ‘the word goldman frequently occurs in same context as the word morgan‘.

By using a large corpus to train vectors which predict what words arise in similar context, we arrive at vectors that represent a sometimes-shockingly complex knowledge about each word, bordering on understanding. For instance, we might find that the word vector closest to ‘Paris’ – ‘France’ + ‘Germany’ is ‘Berlin.’ In other words, Word2Vec in some sense understands that ‘Paris is to France as Berlin is to Germany.’ This post is a good intro.

2 Computer-generated names are generated as follows:

  • Split each firm name into all its initial substrings.
  • Identify the character following each substring. In other words, for “Wiley E. Coyote Investment Management”, generate pairs like
    (‘Wile’, ‘y’)
    (‘Wiley E. Coy’, ‘o’)
    (‘Wiley E. Coyote Investment Managemen’, ‘t’)
  • Use these pairs as a corpus to train a recurrent neural network to predict the next character following an initial fund name substring.
  • Get like 75% accuracy – RNNs are unreasonably effective, or language is surprisingly predictable.
  • To predict, seed the algorithm with a chunk or an empty string, predict the following character.
  • Add the predicted character to the end of the string, predict with the resulting chunk.
  • Get a large number of silly names that resemble the training corpus, with some randomness added.




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