Longreads + Open Thread

Longreads

• Anton Howes on the story of salt: it's a commodity with inelastic demand, it's consumed everywhere but only extractable economically in certain places, and it's straightforward to store and transport. So, basically the oil of the pre-oil world. Howes argues that this actually shaped modern states: places with abundant salt resources tended to be fragmented, while the coherent shape of a larger state required both salt-importing and salt-exporting regions whose supply and demand (roughly) balanced out. This is akin to the political ties that often start with economic ones, like the US's relationship to East Asian manufacturers, or the European Coal and Steel Community kicking off the process that led to the EU.
• Gary Stevenson on working as a trader at Citi. This is a fun piece; the main crux is his early-2010s observation that aggregate macro data showed growth, but that growth was skewed towards rich people with a low marginal propensity to spend. A common heuristic is that if anecdotes disagree with the data, go with the anecdotes, but there's a refinement here: when anecdotes seem to disagree with the data, consider which anecdotes you're collecting (everyone on the trading floor is doing fine—the economy must be great!) and consider whether you're interpreting the data correctly. Every surprising anecdote you encounter is evidence that your model of the world is off, but the right reaction to this is to refine the model, not to throw it out.
• Giuseppe Paleologo has career advice for quants. This piece is broadly applicable career advice for other research-focused fields. For example: be aware of how many jobs there actually are (his estimate is 15k, and about 700 new hires per year); focus on the more technical and mathy aspects of what you're trying to do, on the grounds that you'll learn the particulars on the job and shouldn't prematurely optimize; consider not just the job you're getting, or the job after that, but the job after that, i.e. optimize for the longest-term growth path.
• An absolutely wild story from The Insider claiming that the COO of Wirecard, which collapsed in an accounting scandal in 2020, was also a Russian spy. A useful model of the world is that people vary in how honest they tend to be, and if you catch someone in a minor lie it's almost certainly not the only lie they've ever told. They may, in fact, deliberately select into different fields (lying to investors, spying for Russia) that make use of these talents, and may even find ways that their different projects complement one another. This is part of what short-sellers rely on: bad news is serially correlated because liars keep lying until they get caught. This piece also does some intelligence-gathering of its own, liberally quoting from text messages and travel itineraries. Yet another feature of investigating the actions of unreliable people: they often have similarly unreliable friends who are happy to turn over information they've promised to keep confidential.
• And on the topic of signals intelligence, Wired has a good writeup of governments using private data brokers to track people. Detailed location data trivially deanonymizes just about everyone, and ad-supported services collect this information automatically. The upshot of which is that someone who buys Grindr ad viewing data can triangulate someone based on their home and office and immediately have blackmail material.
• In this week’s issue of Capital Gains, we cover the wealth effect: when asset prices go up, spending goes up, too. But the magnitude depends on how much the owners of different assets want to spend.
• And this week in The Riff, we talked growth investing, alternative data, why investigative journalism benefits from looser IPO restrictions, and why the market in extremely good executive assistants will never perfectly clear.

Books

Trading at the Speed of Light: How Ultrafast Algorithms Are Transforming Financial Markets: A great history of high-frequency trading, starting with the gradual move from floor trading to electronic markets and culminating in the eternal race for lower latency. In most fields, "at the speed of light" means "fast enough to be instantaneous," but in HFT it's a nagging annoyance that certain fundamental constants of the universe limit just how quickly information can be transmitted from the futures markets in Chicago to equities markets in New York. This leads to immense investment in infrastructure so traders can get slightly faster, which feels like a wasteful and somewhat arbitrary competition—but it actually just replaced an earlier and equally-arbitrary competition, where one way to get preferential access to the order book was to be tall, loud, and willing to throw elbows.

The lower latency gets, the simpler algorithms have to be. You can't do some incredibly complicated math if it adds too many microseconds to the process of sending or canceling the next order, but in this domain, you don't really have to: at this pace, just having higher-than-average confidence that the next order will be a buy or a sell is enough to win. That also makes the business utterly brutal: in most fields, you're competing along multiple axes and can compensate for deficiencies in one place with advantages elsewhere. But if the goal is pure speed, you're either winning or losing. And as connectivity providers have learned to specialize in serving the needs of HFTs, they've raised prices—so firms pay a high fixed cost just to participate, and are either amortizing that cost over an enormous number of profitable trades or are bleeding to get to second place. (This dynamic works in the opposite direction, too; the longer-term the strategy is, the less concentrated investment management becomes, because there are so many more variables to think about over those scales, and the relevant variable changes.)

HFT is really a story of visible waste and invisible benefits: it's true that a great deal of mental horsepower has been expended in programming FPGAs to be slightly faster, building more microwave towers, carefully measuring the traits of individual fiber optic cables in order to make sure they have identical latency, etc. But all of that is automating away human labor that was zero-sum at the same level. So arguing about whether HFT is good or not means answering the intrinsically hard-to-answer question of whether one extremely clever electrical engineer forcing twenty quick-thinking floor traders to find another profession is a net win or net loss for humanity. (There are many fields where quick mental math comes in handy.) Those questions are fundamentally hard to answer without some sort of distributed system for collecting and synthesizing information that's widely distributed, i.e. exactly the sort of markets that HFT makes more efficient.

Open Thread

• Drop in any links or comments of interest to Diff readers.

Diff Jobs

Companies in the Diff network are actively looking for talent. See a sampling of current open roles below:

• A private credit fund denominated in Bitcoin needs a credit analyst that can negotiate derivatives pricing. Experience with low-risk crypto lending preferred (i.e. to large miners, prop-trading firms in safe jurisdictions). (Remote)
• A systematic hedge fund is looking for portfolio managers who have experience using alternative data to develop systematic strategies (NYC).
• A company building the new pension of the 21st century and building universal basic capital is looking for a GTM / growth lead. (NYC)
• A concentrated crossover fund is looking for an experienced full stack software engineer to help develop and maintain internal applications to improve investment decision-making and external applications to enable portfolio companies. (SF)
• A crypto proprietary trading firm is actively seeking systematic-oriented traders with crypto experience—ideally someone with experience across a variety of exchanges and tokens. (Remote)

Even if you don't see an exact match for your skills and interests right now, we're happy to talk early so we can let you know if a good opportunity comes up.

If you’re at a company that's looking for talent, we should talk! Diff Jobs works with companies across fintech, hard tech, consumer software, enterprise software, and other areas—any company where finding unusually effective people is a top priority.