How the oldest hedge fund model is bigger and more profitable than ever.
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"Hedge fund" is an incredibly broad term—"compensation scheme masquerading as an asset class" as the saying goes—but when people talk about hedge funds, there's a very good chance that what they're referring to is some kind of multi-manager fund where managers pick stocks and other assets without relying primarily on quantitative signals. Think Citadel, Millennium, Balyasny, and Point72 (disclosure: I worked for Point72 long ago, but this piece is about what's common with the model generally and not about any specific practitioner). This type of hedge fund is also known as a platform, or a "pod shop."
The model has some very old antecedents. The first hedge fund, A.W. Jones & Co. (est. 1949), used a fairly similar model, where individual analysts tracked specific industries and came up with pairs of stocks to trade, one long and one short so they’d capture performance differences between specific companies. But it's been relentlessly refined over time. If there is such a thing as a machine for producing good risk-adjusted returns that mostly aren't correlated to the broader market, it probably exists in the pod shops.
The way it works is roughly like this: the overall fund raises some money (usually quite a lot), borrows substantially more, and then allocates piles of it to dozens (or hundreds) of specific teams. So a fund with $20bn in assets under management might have $100bn in capital to allocate to teams, with some teams running, say, $200m and others managing billions.
Each team typically gets a set of stocks, or sometimes a set of strategies. And their job is to relentlessly monitor these and a) detect incremental shifts in investor sentiment, and b) outline every market-moving catalyst, and either have a view on how it will affect the price or have a very good reason not to.
Within this model, there are brutally strict limitations. The gentlest limitation is requiring portfolios to be beta neutral. In other words, the portfolio manager needs to look at how their positions correlate with the S&P 500 to ensure that the expected change in the portfolio (given a change in the S&P) is, roughly, zero.
This could mean having $250m of long positions and $250m of short positions, but a portfolio that's long a set of high-volatility names (think Wayfair, Carvana, or Chewy) while being short an equal dollar value of stable, slow-growth ones (e.g. IBM, Oracle, and Walmart) would be balanced in dollar terms, but would still tend to move with the market. Those smaller and more volatile long positions tend to move with the market, only more so, while the bigger companies trade at a more sedate pace. So mandates typically require a manger to balance their long and short positions on a risk-adjusted basis instead, so they’re betting on specific stocks instead of trying to time the overall market.
This can be taken to more extremes, by breaking the portfolio's exposure down based on factors like buying statistically cheap stocks, chasing momentum, or going after dividend yield. And it can even be done by industry: if you're excited to own shares of a restaurant chain, you'll need to find a way to be roughly equally excited to short a different restaurant chain. The goal is basically to run the same kind of regressions quants do when the quants are looking for factors that predict outperformance. But in this case, the goal is sidestep those factors entirely. Since “alpha,” or excess risk-adjusted return relative to a benchmark, is literally an error term in a linear formula, all these risk guidelines are a way to systematically capture the kind of skill that can’t be reverse-engineered.
And aside from these guidelines, there are performance-based rules, which can be summed up as: perform, or you're fired. Along the way, an underperforming manager of a pod will probably get the amount of money they're allocated reduced, and will have some harrowing conversations with higher-ups about why they're underperforming and what they plan to do about it. (Often, a mid-single digit percentage loss is enough to get a manager's allocation cut in half, and a high-single digit or 10% loss from peak is enough for them to be asked to leave the firm.)
So what do teams look for in this kind of environment? The core of the job is mapping out catalysts, figuring out how other investors are positioned, and estimating how the stock will react. Catalysts vary across industries and have changed over time, but they include:
- Earnings, of course, as the most important update and the biggest source of large single-day moves.
- Conference presentations: many companies speak at industry conferences and take questions from investors, and those can drive sentiment. In some cases, companies' conference presentations include incremental performance updates, but other times investors are listening for tone—not just whether management sounds excited about the business, but whether other investors are asking questions that indicate boundless optimism or grim realism.
- Many data points get released intra-quarter, both from the government (housing starts are a meaningful number for homebuilding stocks) and from data providers (disclosure: I've worked for a few of those as well).
- Companies going through legal issues or ongoing negotiations will have some kind of continuous news flow. A great deal of effort over the summer went into handicapping Elon Musk’s odds of walking away from the Twitter deal.
- Companies are generally either net issuers of stock, through stock-based compensation and secondary issuance to fund their business, or they're net buyers of their own stock. Having a good mental model of how they think about this is a useful tool for thinking about the stock. For example, suppose there's a company that generates lots of cash flow, has room to borrow more money, and frequently buys back its shares. If the management team seems obsessed with keeping their stock up, they'll tend to react to price weakness by stepping up their buyback, which makes buying the stock a somewhat asymmetric trade.
And, of course, there are sometimes events that come out of nowhere: events where the advantage is not in predicting the event itself but in figuring out the second-order impact.
You may recall a pandemic, for example, and how one of its effects was a massive, temporary economic contraction, while another was an explosion in demand for video conferencing, home exercise equipment, and meal delivery. Being among the first people to say "Hm, what does all this death and disruption mean for the office furniture market?" is definitely a source of performance. That’s why thinking about the economy as interconnected supply chains is helpful.
One part of the work is figuring out reality: carefully parsing SEC filings, digging deep into datasets, crafting elegant Google Alerts, doing endless rounds of phone calls with industry experts, etc. But that's only part of it.
The other part is figuring out what the rest of the market thinks: if you know what's going to happen, or, more realistically, if you have a well-informed 60/40 view on something widely believed to be a 50/50 proposition, how will everyone else react if you're right (or if you're wrong)? And the way to do that is to talk—to people at other funds, to analysts, to industry experts, to anyone who can give you a real-time view of the consensus. Which means it's exhausting work! An analyst is on the clock when they get up at 5am and read up on all the news that's happened overnight; they're still on the clock when they meet a friend from another fund after work and start swapping ideas. This dynamic means that pod shops are the most densely-connected nodes in the data/news/rumors ecosystem.
It's hard for any one person to do this well on more than a small number of stocks, so the typical coverage for an analyst at a pod shop is a few dozen stocks. Automating your way to more stocks isn’t easy—sure, you can automatically parse information and to make analysis easier, but can you tell which investors from which funds are attending which conference presentations? Can you tell which analysts seem more pessimistic lately?
Another reason why pods prevail has to do with scale advantages that come with good operations. Pods can 1) afford massive analytics teams to figure out who is performing well and why, 2) get good deals from counterparties, and 3) separate back-office responsibilities from the front-office work of actually figuring out the right trades to make. If there's a strategy that gets decent returns on its own and fits in with pod risk parameters, it will tend to do much better within a pod.
So, who does this matter to? There are a few audiences who should care:
- If you're interested in working for the platforms, it's a good idea to know what you're getting into. Burnout is a real risk, and it's worth asking yourself if you can become truly, deeply obsessed with thirty somewhat randomly-selected companies in retail, industrials, financials, etc.
- If you're a market participant, there are two things to think about. One is that this strategy is very hard to apply without the scale advantages of the pod shops. It's difficult to get into all the right conferences, read all the right research, and subscribe to the right datasets on a smaller-scale budget. On the other hand, this kind of strategy could be applied in companies that are too illiquid to fit within the pods' risk parameters.
- And on the other side of this, if you're not trying to duplicate the model, you can feel reasonably assured that there's a continuous race to spot and exploit short-term market inefficiencies. The flipside of the increasingly competitive pod shop space is that the opportunities are smaller, which means that missing them entirely is less costly.
Because of their frequent trading, pod shops are basically the main mechanism by which incremental new information gets incorporated into asset prices. And since markets don't care about who participates in them, another way to look at this is that the pod shops, as a side effect of making lots of money for their investors, managers, and employees, are making a massive unpriced contribution to the market efficiency that underpins indexing. A lot of work and a lot of stress goes into making it easy for investors to get a decent return on totally passive assets.
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