What's the Point of Capital Markets?

Note: This piece will use “capital” in the popular sense, i.e. as a synonym for “money”.

Plenty of people argue that some or all of the modern finance industry is engaged in zero-sum games. In particular, speculators, high-frequency traders, and broker-dealers are frequently vilified in this manner.

I don’t particularly care about moralizing, but as someone who’s interested in making money from the capital markets, I’d much rather play a positive-sum game than fight over a fixed-size pie. If there’s real economic value to be generated, then I don’t necessarily have to outsmart everyone else in order to turn a profit. Thus the question: does the high finance industry generate real economic value, and if so, how?

The following sections explore ways to create real economic value through finance. Each section starts with a way to create value in a more intuitive market (grain), and then moves to capital markets by analogy.

I will omit the standard explanations of both banking and insurance, since they are explained just fine elsewhere. That said, bear in mind that the functions of both banking and insurance are not exclusive to institutions with “bank” and “insurer” on their business cards - both borrowing/lending and risk pooling occur in capital markets more generally, and real economic value is created accordingly.

Gains from Trade

Let’s start with the simplest possible econ-101 example.

A farmer grows some grain, and wants money. A consumer is hungry, has five dollars, and for some reason has a hankering for unprocessed wheat. A bushel of wheat is worth more than five dollars to the consumer, and five dollars is worth more than a bushel of wheat to the farmer. They trade, and each is happier - real economic value has been created.

What’s the analogous scenario in a capital market?

A company wants some capital, e.g. to buy a new oven. Somebody saving for retirement has some money, and wants to invest it. The company issues some stock to their newfound investor, in exchange for the money.

Now it starts to get interesting. With the farmer’s wheat, it was pretty clear how both sides benefitted from the trade: the farmer had lots of wheat, the consumer was hungry. But what about the stock example? In order for the company to benefit, their capital investment (e.g. the oven) must boost their earnings enough to justify the new stock issued. So for instance, if the company had to issue 1% more stock in order to raise capital for the oven, then the oven must boost their earnings by at least 1% to be a worthwhile investment.

On the other side, in order for the company’s stock to be a good investment for our hypothetical retirement-saver, the company’s earnings (using their new oven) must yield some return.

The main takeaway is that capital markets generate gains from trade by taking some capital that isn’t being used - e.g. retirement savings - and using that capital for something useful - e.g. a new oven. This is the basis of all value creation in finance, including all of the examples to follow. If you’re ever unsure whether some part of the finance industry is creating real value, remember: at the end of the day, it’s all about using spare cash to finance investments in real business assets. Follow the capital flow, and see what it’s ultimately invested in.

So there’s definitely gains from trade in capital markets. But this scenario completely omitted the actual finance industry - they’re mostly middlemen. How do the middlemen add value?

The Middlemen

Consider the middlemen in an oversimplified grain market. They buy grain from farmers, and sell it to consumers. Their value add is straightforward: they save farmers the hard work of finding a buyer, and they save consumers the hard work of finding a seller.

The finance industry is no different.

When a company issues stock, lining up buyers is a lot of work. Investment banks typically handle that work. Same with bonds, mortgage securities, etc. Just like middlemen in any other market, these institutions create value by saving companies the work of finding capital-sellers, and saving investors the work of finding capital-buyers.

That said, these particular middlemen have some SERIOUSLY entrenched rent-seeking. Imagine that all the grain middlemen managed to form an industry group, lobbied a bit, and their industry group was given the legal power to regulate their own industry - that’s FINRA. Unsurprisingly, they made it illegal for would-be investors to sell capital directly to companies without going through the FINRA-member middlemen, and also made it difficult for new middlemen to enter the space. They still offer SOME real economic value, but they’re also getting a lot by rent-seeking.

Anyway, those are just the most direct middlemen - those who sit directly between companies and investors. There are others, too.

Warehousing

Ever since ancient times, people have stored grain. It can be quite a good business: buy grain right after the main harvest when it’s abundant and cheap, store it for a while, and sell it when grain supplies run low. This can create huge amounts of real economic value, e.g. by preventing a grain shortage (a.k.a famine).

Most of the players we think of in the stock market are in the business of storing capital. They buy capital when it’s abundant (i.e. sell stocks when prices are high), store that capital, and sell it when the capital supply is low (i.e. buy stocks when prices are low). This can be a bit confusing, since buy/sell are kind of backwards compared to how we usually think of them. Fundamentally, that’s because capital is the product, whereas usually the other thing is the product and capital is just a means of exchange. But it’s the same concept as warehousing grain, and it creates value in the same way: by preventing a capital shortage (a.k.a. market crash and economic recession).

Different kinds of investors do this at different time scales. Unlike grain, there’s very little overhead when warehousing stocks, so day traders and high-frequency traders can warehouse small amounts for short times. This sort of activity prevents small market crashes - more day traders and high-frequency traders means less volatile prices on short time scales. Larger investors do the same thing at longer time scales - pension funds warehouse stocks for months or years at a time.

This is also where the information aspect of markets first kicks in. If you’re in the business of warehousing, then anticipated future abundances or shortages of capital (and of financial assets) is key to producing real economic value - and to making a profit. Again, this is counterintuitive at first. In the futures markets, for instance, we’re used to thinking about making money by forecasting abundances or shortages of commodities. To understand capital markets more broadly, we need to think of capital as the commodity.

Forecasting

Finally, we get to the usual picture of high finance: forecasting company performance. As before, we’ll start with the grain market, but with a twist - now there’s barter.

Rather than buying grain with money, imagine that consumers barter for grain, paying with chickens. Different consumers presumably have chickens of varying quality, so their chickens will buy varying amounts of grain.

Now, suppose some middleman comes along and realizes that a new consumer - let’s call her Alice - raises chickens of higher quality than most people realize. Everyone will figure this out eventually, as people eat Alice’ chickens and word gets around, but for now the secret is still fresh. Accordingly, this middleman offers extra grain for Alice’ chickens. Does this create real economic value?

It all depends on the elasticity of Alice’ chicken supply.

If Alice only raises fifty chickens, no matter what, then fifty of Alice’ chickens will eventually be consumed, no matter what. There’s the usual gains from trade, but the middleman isn’t increasing those. The middleman’s gain comes entirely from other traders’ ignorance of the quality of Alice’ chickens; the middleman’s gain is exactly equal to the other traders’ missed opportunity.

But the story is different if Alice responds to the higher price by supplying more chickens. Then, real economic value clearly is created. By making prices better reflect the true value of Alice’ chickens, the middleman has enabled more chickens to be created.

Now let’s switch to capital markets.

When people imagine making money in stocks, the usual picture is:

• Find a company whose stock is “under-valued”
• Buy their stock
• ...
• Profit!

Does this create real economic value?

It all depends on the elasticity of the company’s outstanding shares.

If you buy the stock from an investor, then you’re simply gaining money which that investor would otherwise have gained. It’s zero sum: their missed opportunity is exactly equal to your gain. But if you buy the stock from the company (either directly or indirectly), then it’s a whole different story. You’re supplying a bit of extra capital to the company. That will create real economic value when the company can invest the capital better than the competition - i.e. this company can get more value out of an extra oven than some other company would get out of an equivalent investment.

Put differently, suppose a middleman buys some stock in Millisoft. How much additional capital does Millisoft receive, compared to a scenario where the middleman did not buy any stock? Then, how much additional value does that capital create, when it’s used by Millisoft rather than whoever might have used it otherwise? That’s where the real economic value comes from, when forecasting company earnings.

When you think about it like that… well, forecasting earnings (and other similar activity) probably creates some real economic value, but probably not much, especially at the margin. Most of the gains from this sort of activity really are zero-sum.

Conclusion

I don’t have numbers on this, but I would guess that most of the real economic value created by the high finance industry comes from warehousing. Ironically, it’s largely from the day traders and high-frequency algo traders who are so often vilified.

Institutions like investment banks also create real economic value, but they are rent-heavy. Looking for places where investors’ expectations are inaccurate can create value in principle, but it’s probably mostly zero-sum.

From Personal to Prison Gangs: Enforcing Prosocial Behavior

Background: This is part of a short series on high-level principles relevant to political/social issues. The first post set up some ground rules for the general approach.

David Friedman has a fascinating upcoming book on alternative legal systems. One chapter focusses on prison law - not the nominal rules, but the rules enforced by prisoners themselves.

The unofficial legal system of California prisoners is particularly interesting because it underwent a phase change in the 1960’s.

Prior to the 1960’s, prisoners ran on a decentralized code of conduct - various unwritten rules roughly amounting to “mind your own business and don’t cheat anyone”. Prisoners who kept to the code were afforded some respect by their fellow inmates. Prisoners who violated the code were ostracized, making them fair game for the more predatory inmates. There was no formal enforcement; the code was essentially a reputation system.

In the 1960’s, that changed. During the code era, California’s total prison population was only about 5000, with about 1000 inmates in a typical prison. That’s quite a bit more than Dunbar’s number, but still low enough for a reputation system to work through second-order connections. By 1970, California’s prison population had ballooned past 25000; today it is over 170000. The number of prisons also grew, but not nearly as quickly as the population, and today’s prisoners frequently move across prisons anyway. In short, a decentralized reputation system became untenable. There were too many other inmates to keep track of.

As the reputation system collapsed, a new legal institution grew to fill the void: prison gangs. Under the gang system, each inmate is expected to affiliate with a gang (though most are not formal gang members). The gang will explain the rules, often in written form, and enforce them on their own affiliates. When conflict arises between affiliates of different gangs, the gang leaders negotiate settlement, with gang leaders enforcing punishments on their own affiliates. (Gang leaders are strongly motivated to avoid gang-level conflicts.) Rather than needing to track reputation of everyone individually, inmates need only pay attention to gangs at a group level.

Of course, inmates need some way to tell who is affiliated with each gang - thus the rise of racial segregation in prison. During the code era, prisoners tended to associate by race and culture, but there was no overt racial hostility and no hard rules against associating across race. But today’s prison gangs are highly racially segregated, making it easy to recognize the gang affiliation of individual inmates. They claim territory in prisons - showers or ball courts - and enforce their claims, resulting in hard racial segregation.

The change from a small, low-connection prison population to a large, high-connection population was the root cause. That change drove a transition from a decentralized, reputation-based system to prison gangs. This, in turn, involved two further transitions. First, a transition from decentralized, informal unwritten rules to formal written rules with centralized enforcement. Second, a transition from individual to group-level identity, in this case manifesting as racial segregation.

Generalization

This is hardly unique to prisons. The pattern is universal among human institutions. In small groups, everybody knows everybody. Rules are informal, identity is individual. But as groups grow:

• Rules become formal, written, and centrally enforced
• Identity becomes group-based.

Consider companies. I work at a ten-person company. Everyone in the office knows everyone else by name, and everyone has some idea of what everyone else is working on. We have nominal job titles, but everybody works on whatever needs doing. Our performance review process is to occasionally raise the topic in weekly one-on-one meetings.

Go to a thousand or ten thousand person company, and job titles play a much stronger role in who does what. People don’t know everyone, so they identify others by department or role. They understand what a developer or a manager does, rather than understanding what John or Allan does. Identity becomes group-based. At the same time, hierarchy and bureaucracy are formalized.

The key parameter here is number of interactions between each pair of people. In small groups, each pair of people has many interactions, so people get to know each other. In large groups, there are many one-off interactions between strangers. Without past interactions to fall back on, people need other ways to figure out how to interact with each other. One solution is formal rules, which give guidance on interactions with anyone. Another solution is group-based identity - if I know how to interact with lawyers at work in general, then I don’t need to know each individual lawyer.

In this regard, prisons and companies are just microcosms of society in general.

Society

At some point over the past couple hundred years, society underwent a transition similar to that of the California prison system.

In 1800, people were mostly farmers, living in small towns. The local population was within an order of magnitude of Dunbar’s number, and generally small enough to rely on reputation for day-to-day dealings.

Today, that is not the case [citation needed].

Just as in prisons and companies, we should expect this change to drive two kinds of transitions:

• A transition from informal, decentralized rules to formal, written, centrally-enforced rules.
• A transition from individual to group-level identity.

This can explain an awful lot of the ways in which society has changed over the past couple hundred years, as well as how specific social institutions evolve over time.

To take just a few examples…

• Regulation. As people have more one-off interactions, reputation becomes less tenable, and we should expect formal regulation to grow. Conversely, regulations are routinely ignored among people who know each other.
• Litigation. Again, with more one-off interactions, we should expect people to rely more on formal litigation and less on informal settlement. Conversely, people who interact frequently rarely sue each other - and when they do, it’s expected to mess up the relationship.
• Professional licensing. Without reputation, people need some way to signal that they are safe to hire. We should expect licensing to increase as pairwise interactions decrease.
• Credentialism. This is just a generalization of licensing. As reputation fails, we should expect people to rely more heavily on formal credentials - “you are your degree” and so forth.
• Stereotyping. Without past interactions with a particular person, we should expect people to generalize based on superficially “similar” people. This could be anything from the usual culprits (race, ethnicity, age) to job roles (actuaries, lawyers) to consumption signals (iphone, converse, fancy suit).
• Tribalism. From nationalism to sports fans to identity politics, an increasing prevalence of group-level identity means an increasing prevalence of tribal behavior. Based on this, I predict that social media outlets with more one-off or low-count interactions are characterized by more extreme tribalism.
• Standards for impersonal interactions. “Professionalism” at work is a good example.

I’ve focussed mostly on negative examples here, but it’s not all bad - even some of these examples have upsides. When California’s prisons moved from an informal code to prison gangs, the homicide rate dropped like a rock; the gangs hate prison lockdowns, so they go to great lengths to prevent homicides. Of course, gangs have lots of downsides too. The point which generalizes is this: bodies with centralized power have their own incentives, and outcomes will be “good” to exactly the extent that the incentives of the centralized power align with everybody else’ incentives and desires.

Consider credentialism, for example. It’s not all bad - to the extent that we now hire based on degree rather than nepotism, it’s probably a step up. But on the other hand, colleges themselves have less than ideal incentives. Even setting aside colleges’ incentives, the whole credential system shoehorns people into one-size-fits-all solutions; a brilliant patent clerk would have a much more difficult time making a name in physics today than a hundred years ago.

Takeaway

Of course, all of these examples share one critical positive feature: they scale. That’s the whole reason things changed in the first place - we needed systems which could scale up beyond personal relationships and reputation.

This brings us to the takeaway: what should you do if you want to change these things? Perhaps you want a society with less credentialism, regulation, stereotyping, tribalism, etc. Maybe you like some of these things but not others. Regardless, surely there’s something somewhere on that list you’re less than happy about.

The first takeaway is that these are not primarily political issues. The changes were driven by technology and economics, which created a broader social graph with fewer repeated interactions. Political action is unlikely to reverse any of these changes; the equilibrium has shifted, and any policy change would be fighting gravity. Even if employers were outlawed from making hiring decisions based on college degree, they’d find some work-around which amounted to the same thing. Even if the entire federal register disappeared overnight, de-facto industry regulatory bodies would pop up. And so forth.

So if we want to e.g. reduce regulation, we should first focus on the underlying socioeconomic problem: fewer interactions. A world of Amazon and Walmart, where every consumer faces decisions between a million different products, is inevitably a world where consumers do not know producers very well. There’s just too many products and companies to keep track of the reputation of each. To reduce regulation, first focus on solving that problem, scalably. Think amazon reviews - it’s an imperfect system, but it’s far more flexible and efficient than formal regulation, and it scales.

Now for the real problem: online reviews are literally the only example I could come up with where technology offers a way to scale-up reputation-based systems, and maybe someday roll back centralized control structures or group identities. How can we solve these sorts of problems more generally? Please let me know if you have ideas.

IQ Scores: What are they good for?

Response to http://slatestarcodex.com/2017/09/27/against-individual-iq-worries/ and https://www.scottaaronson.com/blog/. This post can be read standalone.

I recently encountered two articles arguing against making too much of one’s own IQ score. Both of them mostly boil down to “IQ tests are a REALLY noisy measure of g, and on an individual level the noise is going to mask a lot of the signal”. This is totally 100% correct, and is probably responsible for most of the hand-wringing those two authors encounter.

But let’s cut past the noise: suppose you’ve taken an IQ test, and the SATs, and maybe throw in some other measures too. That’s all Bayesian evidence for your underlying g, so you can put them all together to get a hopefully-less-noisy estimate. The result tells you something about your intelligence relative to the rest of the population. What should you do with this information? In particular, if the number is lower than you’d like, what’s the right response?

There are multiple good answers to that question, most notably relative advantage - pick up an intro microeconomics text if you want to know more about that one. But in keeping with my usual policy of “don’t write things that somebody else already wrote”, I’ll focus on an answer which I haven’t seen used in this context before: strategic variance.

The Underdog Strategy

Suppose you’re in an oversimplified one-on-one basketball game with three rounds. Strictly speaking, your opponent is a better player than you: they average 24 points per round, while you average 22. But you have a trick up your sleeve: your opponent is a one-trick pony, while you have multiple play styles. One play style is conservative and consistent: in one round, you’ll score 22 points and your opponent will score 24, consistently. Your other play style is more aggressive, with more variance: in one round, your opponent will score 24, and you’ll score either 14 or 30, with a 50% chance for each.

For both play styles, your opponent averages 2 more points per round than you do. But you can still win more often than not. Here’s the strategy:

• Round 1: play aggressive. You end up ahead by 6 points (50% chance) or behind by 10 (50%).
• Round 2 & 3: If you’re ahead, play conservative; if you’re behind, play aggressive. If you wound up ahead in round 1, then you’ll play conservative for the next two rounds and win by 2. If you wound up behind in round 1, then you’ll play aggressive for 2 rounds and have a 25% chance of a comeback.
• Put that all together, and your chance of winning is 62.5%.

Despite your opponent scoring more points on average regardless of strategy, you can still win more often than not!

The example is somewhat artificial, but the idea generalizes:

• When you’re “ahead”, play conservative - avoid risk, minimize variance.
• When you’re “behind”, play aggressive - take risk, maximize variance.

The principle generalizes easily to practically any game with some way of keeping score - virtually all sports, board games, card games, and so forth. (On a side note, it also applies to bacterial chemotaxis.) Let’s apply it to real life.

The Underdog Strategy in Real Life

Suppose my goal in life is to solve some major open problem in math/science - we’ll use the P-NP problem as an example. Then my own intelligence - g, IQ, whatever measure we’re using - is a very useful indicator of how far “ahead” or “behind” I’m starting.

Consider Terence Tao - presumably he’d be starting way “ahead” by this criteria. If he spent a year or two focussed entirely on P-NP, he’d probably be one of the top 5 smartest people ever to invest that much effort in the problem. There’s a reasonable chance that he could solve it by brute force of intellect - by being smarter than anyone else who’d tried. Maybe P-NP is straightforward for anyone who’s up-to-date with known circuit complexity lower bounds and has sufficiently high g, and the problem is just waiting for someone smart enough to come along and put the pieces together. There’s a realistic chance that Terence Tao could be the first person to come along who’s smart enough.

When you look at it like that, if Terence Tao decides to seriously tackle P-NP, then just spending a year pushing current approaches would be a very reasonable starting point for him. He’s starting out “ahead”, so a conservative low-risk strategy makes sense as a first thing to try.

But what if I want to solve P-NP?

I’m smart, but I wouldn’t be in the top 100 or probably even the top 1000 smartest people who’ve tackled P-NP. I will never be able to solve P-NP by brute force of intellect, by taking the standard approaches and throwing my own intelligence at them. I am not that smart.

I’d be starting “behind” in this game, my chance of winning is low a priori, so to maximize my chances I need to add variance. In this context, that means trying weird approaches. Investing effort in tools which may or may not be useful but are definitely different from what everyone else is doing, and applying those tools to the problem. On average, any particular random thing is less likely to be the key to P-NP than lower-bounding circuit complexity. But I, personally, am more likely to solve P-NP by applying some weird technique from probability or physics or economics or even biology, than by pushing already-popular strategies. (That doesn’t mean I shouldn’t get up to date with current research, just that I shouldn’t invest much effort pushing past the cutting edge in that particular direction.)

Conclusion

One last comment to wrap it up: strategic variance only applies to binary problems, where you either clear the bar or you don’t. If your goal in the oversimplified one-on-one basketball game is to improve your average score, then variance won’t help. If your goal in life is to maximize your expected earnings, then again, variance in and of itself will not help. On the other hand, if your goal in life is to become a billionaire, then strategic variance - i.e. risk taking - will help.

Generalizing further: as humans, we tend to invest less effort than we should in high-level life strategy. Things like estimating your own g/IQ are useful mainly to inform that strategy. The more strategic you are, the more value you can extract from that information. Strategic variance is just one class of strategies. Relative advantage is the main underlying strategy - e.g. in the oversimplified basketball game, you win by exploiting your relative advantage of being able to adjust your own variance. Study relative advantage, and pay attention to which tradeoffs are cheaper for you than for others.