Why Do Equities Build So Much Wealth?
Just about everyone knows that equities historically have higher returns than other asset classes, and that these higher returns are associated with higher risks. What’s slightly less well-known, but intuitively plausible, is the observation that this risk-reward relationship is pretty linear: at some level, investors can trade off between equities and bonds-levered-until-they're-as-volatile-as-equities, such that both categories get roughly the same return. And in fact Antti Illanen's Expected Returns notes that on a risk-adjusted basis, US equities and corporate bonds have exactly the same aggregate performance over the entirety of the data we have (since 1900 for equities and since 1973 for bonds); in both cases, the asset class generates 0.37 percentage points of return above the risk-free rate for every percentage point of annualized standard deviation.
This raises an interesting theoretical point. There are around $10tr in corporate bonds outstanding, $12tr in bank loans, $17tr in total consumer debt, $24tr in treasury bonds. The credit market is, in the aggregate, bigger than the stock market. And it generates a similar risk-adjusted return. And yet fixed income billionaires, whether they got that way by managing money for other people or compounding it themselves, are surprisingly rare. Meanwhile, not only are there many rich people who got that way by managing portfolios of equities, but the single most common source of net worth for the ultra rich is maintaining a highly concentrated equity portfolio consisting mostly of their shares in a company they founded. (This means we get to double-dip in a few cases: technically an equity hedge fund billionaire made their money from owning equities in a business also focused on owning equities.)
There are, of course, bond fortunes: Citadel started out focused on convertible bonds, Bridgewater has done very well with fixed income, and Appaloosa had many of its early wins in distressed credit. But run down the Forbes 400 list's finance section and those investors are outnumbered by the equities-focused managers. And by the private equity fund managers, whose asset allocation is technically long equities and short bonds. And many of the funds that started out bond-focused have since diversified into equities (of course, some of the equity people diversify into bonds, too).
Two asset classes, similar risk-adjusted returns over multiple generations, and it's easier to get rich owning one and shorting the other than focusing entirely on the wrong asset class. It's an interesting puzzle!
One plausible explanation goes back to the list of fixed income asset sub-classes above. Many of these are most cost-effectively owned by banks and insurance companies, both of which trade a favorable cost of capital for strict regulation governing their growth. These institutions can do well for themselves over time, of course, but a prudent regulator should look at any regulated financial institution whose returns can lead to truly generational wealth as a potential source of systemic risk.
There are a few factors that make equities a bigger source of true outlier wealth creation:
- The private-to-public markup: Most people who get generational wealth from founding a company and taking it public will accrue most of their net worth after IPO. But as one such person once put it, the first billion is the hardest; that later growth is partly easier because it's compounding at a lower rate off a larger base. It's very hard to come up with some kind of risk-adjusted return for entrepreneurial activity, since we can't easily measure the size of the investment (is it the dollars that go into a company or the opportunity cost of early employees' time—and how do you measure that opportunity cost other than by the value they created?), the value of the result (until the IPO), or the volatility. As a company evolves from scribbles on a whiteboard into a late-stage pre-IPO business, analyzing its risk-adjusted return becomes more tractable, and many investors do run the numbers on whether they should allocate more capital to nine-figure growth rounds or to similar already-public companies.
- Persistence: There are great equity investors who have achieved sustained outperformance with low-turnover strategies. This can't be done in bonds, aside from a few edge cases which are themselves functionally equivalent to equity investing. Someone who is compounding their money at 20%+ in fixed income is almost certainly trading in and out of different opportunities; a distressed debt investor who is making money on both interest and capital appreciation is eventually no longer a distressed debt investor because their assets have appreciated to the point that they're not distressed. But growth investors can and do sit on the same portfolio for years and years; once you've figured out how Transdigm, Fastenal, Danaher, Google, or MasterCard work, your job is to keep an eye out for what might make them stop working so well, not to trade in and out.
- Liquidity and breadth: There are parts of the fixed income world that are liquid, and there are parts where it's plausible to identify long-lasting, repeatable sources of alpha. These largely do not interact; in treasury bonds, you have effectively infinite liquidity, so alpha comes from a) savvy systematic strategies, and b) macro calls; systematic strategies tend to decay over time, and a macro thesis is partly obsolete the moment it's right. Corporate bonds trade in a more fragmented market, with wider bid/ask spreads, which means there are more opportunities for small-scale alpha from making a market but there's a drag on large-scale alpha; it's difficult to accumulate a large position quickly without accidentally informing just about everyone of what you're up to.
- Favorable leverage characteristics: The core argument for why different asset classes should have similar risk/reward relationships is that leverage or holding on to cash can give any asset class the same volatility as any other asset class. T-bills are a lot less volatile than the S&P, but if you're willing to lever up enough, you can get the same volatility. But in a sense it's lower-quality volatility: if you own shares of a company, and that company ends up with assets worth less than its debts, your worst-case scenario is a zero, but with a levered portfolio you can be forced to sell when positions move against you. And even when a company is insolvent, shares still have option value, and can recover. This justifiably legendary tweetstorm looks at the phenomenon from the perspective of a short seller, but from the viewpoint of a speculative buyer, the main point is that you'll get access to leverage on more favorable terms if the leverage you get is a limited-liability equity stake in a levered or otherwise high-risk entity.
There is a reason most personal finance advice does not target Forbes 400 membership: even the people who do everything right face long odds, because part of "doing everything right" means accepting high variance and the potential for failure. It's still a good way to look at which fields can create generational wealth in a fairly short time period, and we can invert the criteria above to describe what the process of converting a decent risk-adjusted return into a nonzero chance of massive success looks like: shy away from any activity where risk/reward can be easily quantified upfront; focus on a field where you can keep doing the same thing and continuously improve in it—even if specific skills have a short half-life, the meta skills have a long one; avoid domains where there's some kind of inefficiency that compounds faster than your own success (or, if you're not avoiding those fields, specialize in routing around the inefficiency; Thomas Peterffy's attitude towards 90s exchange regulations is the ideal here, the perfect synthesis of the STEM skill of building a robot and the liberal art of carefully interpreting a vague rule); and, since you're almost certainly going to use other people's money, find a way to do it such that you can be down over 100% without getting a margin call.
One objection you might have is that most investors do not sit down with a list of asset classes and determine their sharpe ratios in order to select exposure. True! But there are three arguments to bear in mind: First, opportunistic investors will end up overweight whichever asset class offers the best returns, even if they're looking deal by deal and never consciously think about their asset allocation; second, even if the median investor doesn't do this, bigger investors do; and third, the investors who are reasonable about their asset class allocation—the ones who, in 2021, started saying "I'm not sure there are all that many undiscovered startups that can't get funding, actually," will, over time, constitute more of the market since they'll be right. ↩︎
While those were the two asset classes I set out to compare before writing this, a look at the tables shows a wide range of performance for other kinds of fixed income. 0-3 month treasuries, for example, allegedly generate a sharpe ratio of 1.58, while 10+-year treasury bonds are at 0.22. The general point that the risk/reward tradeoff is in the same ballpark across different asset classes stands. And if you have two asset classes with similar risk-reward, one of which gets nonstop coverage online and in social media and has many dedicated apps, products, and books, you’d think that the exciting upside would be in the other asset class. ↩︎
Applying this criterion strictly would mean putting limits on Berkshire Hathaway, which would be unfortunate. On the other hand, it would have meant paying a lot more attention to subprime-focused commercial banks and the investment banks that worked with them in the mid-2000s, as well as Fannie and Freddie, which would have been nice. ↩︎
Suppose a company has $1bn of senior debt, $1bn of junior debt, and its assets are probably worth $1bn such that the junior debt gets basically zero. The junior debt will perform almost exactly the same way equity would perform if the junior debt slice did not exist, i.e. if the company adds $100m in value to its assets, the junior debt and the hypothetical equity are both worth a little bit more than $100m. The equity starts acting differently if the value goes way up, of course. ↩︎
"Partly" because someone who understands what caused a recession, or what caused growth to reaccelerate after one, is more likely than average to understand how things will play out later on. What typically offsets this is the psychological difficulty of trying to make money in exactly the opposite way you just made a lot of it; people who were temporary put option millionaires circa March 2020 can relate. ↩︎
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Is the end of a rate-hiking cycle good news or bad news? The historical verdict is clear: no clue! The market usually goes up slightly (but less than its historical rate, but outcomes range from a 30% gain in six months to a 38% loss in two ($, FT). Building any kind of meaningful market signal is hard, but it's especially hard when the predictor is responsive to sentiment: one thing that can affect the Fed's rate path is where markets go, since that's a proxy for growth expectations. So rate cuts tend to happen when bad news has already started to accumulate (and it's shown up in prices)—and prices discount this reaction to the prices themselves.
Amazon has cut costs and increased delivery speeds by reorganizing its US logistics network, shifting from a national model to a regional one ($, WSJ). Logistics is partly a real-world problem of actually getting products to where they're supposed to be, but it's also, increasingly, a computational problem of figuring out the optimal place for inventory. As Amazon's offerings have broadened, that problem gets harder—you can offer an order of magnitude more SKUs than the competition, and you can offer two-day or next-day delivery, but it's very hard to cost-effectively offer that speed of delivery for slow-moving items. Some of this gets solved through price signals: when Amazon makes storage expensive, but also makes it harder to convert customers without using Amazon's own logistics, they basically penalize merchants who offer low-turnover goods. (If you've ever wondered why used books on Amazon used to usually cost about $2 plus shipping and now seem to go for $20-$200 unless you Buy The Dip on something that was hyped but turned out to be forgettable, there's your answer.) But even price signals have their limits when coordinating a complex system, and sometimes the only way to address this is to break one complex system into several smaller ones and a meta-system for keeping the rest balanced.
Disclosure: Long AMZN.
AngelList has some good data on early predictions of startup success: if an angel investment doesn't get marked up within a year, it's probably a zero. (They note the obvious caveat, that a company that either raises a round meant to last for a long time, or that instantly achieves profitability, will go a long time before raising again.) It's a good reminder that the better an early-stage portfolio performs, the more likely it is to converge on being a single-company portfolio. The option to reinvest later on ends up being the most valuable part of the early-stage portfolio, so tracking which companies are taking off is important.
The New Geography
Conferences are coming back, and attendance at in-person conferences in Q1 this year is higher than it was in Q1 2019 according to one event software provider ($, WSJ). The Diff argued that this would be a long-term effect of the pandemic in 2021 ($): Covid has made some people more mobile generally, moved others around, and shaken up local networks. But in-person interactions are still valuable, so it's increased the utility of actually getting people together in the same room, leading to a corresponding increase in the locations that are Schelling Points for that.
Marking to Market
Tiger Global, which famously reshaped the venture market in the last few years with its rapid-fire dealmaking, is planning to sell some of its private company stakes in the secondary market ($, FT). This is important, less for what it says about Tiger—they could be doing this for many reasons, including funds reaching their end of life or investors pressuring them to get liquidity from older funds as a condition for raising new ones—but because it means more companies will get cleaner mark-to-market valuations. One of the annoyances when tech investing slows down is that private companies have so much room to structure rounds (and many counterparties are happy to come up with fun structures), so it's unclear whether stated valuations are remotely close to reality. A company raising a round at exactly the same valuation they raised at in 2021 is like an academic paper that reports a P-value of 0.049: it's evidence that someone worked hard to hit a specific threshold and that the real results are worse than that. If a few more companies get accurate marks, that might be bad news for them. But it also means that fewer companies will want to delay IPOs and fundraising, dismiss mergers, or elaborately structure deals in order to preserve artificially high valuations.
Companies in the Diff network are actively looking for talent. A sampling of current open roles:
- A hedge fund is looking for an experienced alternative data analyst who can help incorporate novel datasets into systematic strategies (NYC).
- 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)
- A company building zero-knowledge proof-based tools to enable novel financial arrangements is looking for a senior engineer with a research bent. Ideal experience includes demonstrations of extraordinary coding and/or math ability. (NYC or San Diego preferred, remote also a possibility.)
- A well funded seed stage startup founded by former SpaceX engineers is building software tools for hardware engineering. They're looking for a UX/frontend engineer interested in designing and developing software collaboratively with satellite, rocket, and other complex machine engineers. (Los Angeles)
- A company building ML-powered tools to accelerate developer productivity is looking for a mathematician. (Washington DC area)
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.