Netting risk, "The Hopeless", John Arnold and more
Friends,
A grab bag of practical finance nerd stuff today.
Behind the paywall, subs get an exclusive complimentary pass to the 3-hour commodity trading seminar I did for QuantInsider. It’s especially useful for equity folks looking over the fence (especially relevant these days with oil in the news.)
🧵A thread on COIN vol since the inclusion in the SP500 (moontower)
Note Vivek’s reply (I won’t doxx who he is but many of you can guess):
✍🏽More Grit, Less Drip (Market Jiujitsu)
Ari watched me and Mark Phillips’ TSLA Covered Call video:
"If you do nothing else, go watch the video. These are two professionals and the dialogue between them is as important as the research itself"
He draws the lessons that we were trying to convey:
Why is this different than doing technical analysis and/or doing backtests out the wazoo until you p-hack a bad solution? They are not optimizing. They are doing a research project that forces them to ask questions and iterate through. In fact, this roughly the education process that Darrin Johnson took to build himself into the trader that he is today and the inspirational figure that he is, too.
General James Mattis has told us “If you haven’t read hundreds of books, you are functionally illiterate, and you will be incompetent, because your personal experiences alone aren’t broad enough to sustain you.” The same goes for the market where there are not (exactly) books telling you what may work or not. In fact, if you want the good stuff, the stuff that works that is not blandly generic (yet still high quality), then you have to roll up your sleeves and go to work. The key part is that you are not doing this research to lead to an answer. You are doing it to lead toward the tools and the expertise that will lead to a winning trading strategy. This is what Spitznagel refers to as the roundabout strategy in the Dao of Capital. You don’t do this for the immediate reward of a working and foolproof money machine. You do it so that you know the questions to ask and what factors and what risks may lie in wait for you. Then you take that information and strive to either go deeper again or work on the strategy.
If you find this sort of thing interesting, that is a good indication that you are cut out for this. On the other hand, if you find that all you want is the “answer” then maybe this is not for you.
There is quite a bit in the video. There is more that I would like to talk about. I’m sure if I listened again, I’d get more out of it. I feel like this sort of thing is more valuable than doing something more statistical. The actions of doing this for one stock are, of course, living in the land of small numbers. But the crafting of this and the looking at the data and the asking of questions sets the stage for doing something that is more statistical. By statistical, I mean coming up with a set of criteria. But to even get to this point, you need a certain amount of experience and I think it is worthwhile to have a mentor — or a co-worker as these two did.
🎙️Why Asset Allocators Love Multi-Strategy Hedge Funds (Odd Lots)
Ronan Cosgrave, a partner at Albourne, gave a masterclass about multi-strat pod shops on Odd Lots. The whole interview is great but here’s a few topics that I want to highlight because are a big deal if you work at a fund but underappreciated by the casual finance observer.
✔️When Diversification Isn’t a Free Lunch: The Fee Structure Problem in Pod Shops
The traditional hedge fund model charges a management fee plus a cut of the gross performance of the portfolio as a whole.
In a pod shop, things get more complicated.
First, the management fee may be structured as a pass-through, meaning it isn’t fixed—it flexes with the underlying costs, which are often generously defined. Second—and more importantly—the performance fees are charged at the level of the individual PM, not the fund. That means the economics of diversification break down.
Diversification is not a free lunch when you’re paying performance fees on the components of the portfolio. It costs you money—real money.
✔️Pod PMs Actually Get Paid By the LP NOT the GP
Each PM in a pod shop runs their own individual P&L. All expenses—Bloomberg terminals, analysts, trading costs—are charged against that PM’s book. If there's positive net performance, the PM receives a share of those gains from the fund manager. Importantly, investors themselves don’t pay PMs directly—the manager does. But this structure creates a disconnect between fund-level performance and fee drag.
✔️Netting Risk: When Winning PMs Get Paid and You Still Lose
In a traditional HF like where I used to work, there is “netting risk”.
Here’s a simple example:
PM A is up $10
PM B is down $10
At the fund level, you’re flat. But PM A is owed their their 20% cut—$2—from the overall fund. This is a “netting” problem.
The GP loses money even with flat fund performance, so is inclined to not pay the PM’s that are up what they should.
Pod shops can poach talented PMs who made money but didn’t get paid.
In the pod shop model, the economics are different—and risky for LPs as they still pay the winning PMs even if the overall fund is flat!
The netting risk is shifted from the GP to the LP.
But this risk is not without a benefit…the pod shop will be better diversified than the traditional HF. The reason comes from incentives as you will see in the next section.
✔️Quantifying Netting Risk
Albourne has modeled this dynamic. Across simulations, the average cost of netting risk is about 1% per year. That’s 1% of the management fee being paid to PMs who made money, even when the rest of the fund didn’t.
That cost changes behavior. If you’re a traditional manager, you might start to prefer more correlated risk across pods—everyone wins or loses together—because it minimizes internal netting drag. But that undermines diversification.
To reduce the business risk of netting, you reduce dispersion. But with less dispersion, you get lower Sharpe.
“Alt Data Manipulation”
I just thought it worth posting the full excerpt from Matt Levine’s Tuesday Money Stuff:
We talked yesterday about the wait time for delivery from pizzerias near the Pentagon, which arguably predicted Israel’s attack on Iran, and which more generally is arguably correlated with oil prices. The busier those pizzerias are, the busier the Pentagon probably is, which probably means some geopolitical stuff is going down, which probably means oil prices are going up. None of those things is absolutely true. Maybe some unrelated business near the Pentagon needed a lot of pizzas; maybe the Pentagon’s softball championship is that day; maybe the geopolitical stuff will reduce the price of oil. But it would not be shocking if there is some positive correlation.
I, like, one-quarter-jokingly suggested that hedge funds should pay for a direct data feed of Pentagon pizzeria wait times, since that would be a valuable signal to their commodity trading models. Fine.
Three readers independently emailed me with variants on what in retrospect is sort of an obvious question, which is: “Is it market manipulation to order like 200 pizzas to an office near the Pentagon, and then buy calls on oil?” A few points:
Not legal or investing advice!
This assumes that it would work, which in turn assumes that hedge funds are trading on this data. My thesis yesterday was something like “the oil futures market does not move immediately in reaction to Pentagon pizza delivery wait time data, so if you traded on that data, you would be ahead of the market and make a profit.” My readers’ implicit thesis is something like “since it was published in Money Stuff, now canonically the market will move immediately in reaction to Pentagon pizza delivery wait time data, so if you manipulated that data, you would be ahead of the market and make a profit.” But obviously if nobody trades on that data then you’ve just wasted money on pizza.
More generally, “manipulate alternative data that is correlated with some security prices, expecting sophisticated hedge funds to trade on that data, and trade the correlated securities ahead of them” seems like a rich field for study in modern finance. As people seek more obscure sources of information, data sources that are moderately correlated with asset returns rather than leaks of merger news, there are more opportunities for both manipulation and plausible deniability. We have talked a few times about “shadow trading,” which is the related practice of (1) getting inside information about some company and (2) trading some correlated security (rather than the company’s stock, which will obviously get you in trouble). The field of alt-data manipulation is broader, though — if hedge funds are reading tweets, you can write a lot of tweets, etc. — and less obviously illegal. Trading one security with inside information about another security seems bad in some fuzzy but obvious way; ordering too many pizzas to trick people into buying oil is murkier. “Park 100 cars in the parking lot of some retailer announcing earnings next week, and buy calls on the company,” that sort of thing: You were misleading someone, probably (the hedge funds examining satellite images of that parking lot), but why did they think they were entitled to rely on that parking lot for their trading?
🎙️John Arnold on Conversations with Tyler (transcript)
Highlighting these excerpts that have to do with requisite trader skills and his strategy of concentrating on a closed system (and the risk with such a strategy).
Before we get into skills, a reminder from SIG brass:
COWEN: What do you feel is the skill you had that your other traders didn’t have to the same degree?
ARNOLD**:** I’ve always had a difficult time answering this question. I think part of it is, trading is a team sport for sure. I always took the view of to get around smarter people, and listen more than talk.
In terms of traits, here’s what I would offer as my traits:
Number one is this detachment from emotion. There’s a lot of talk about fear and greed driving markets. To the extent that fear and greed change your process, the more you can remove those emotions, I think, the better.
I think there is a component of first principles trading, where first principles of how you look at information. Don’t accept the information as is, but really test all assumptions that go into it.
I think there is a component of being on the perfect point of the confidence spectrum. You have to be confident in order to say the market is wrong, and I’m right, that other people are wrong, because I think efficient market hypothesis is fairly true. But if you’re overconfident, you’ll blow up quickly.
[Kris: can’t agree more. See If You Wait For All The Info You’ll Be Too Late]
There is this notion of being quantitative enough to build the long-term models, but being quick with numbers in order to jump on the trades as they happen.
There’s this aspect of, I think, a chip on my shoulder. Really having this passion for it. You have to have the love of it. That this is the most important thing. I ate, breathed, and slept it. I would be thinking about it first thing in the shower in the morning. I would be dreaming about it. After work, I’d go out with people in the industry and talk about it. It was that real devotion to the markets.
I think there was a timing component, that I always had great timing in my career. Then, certainly, luck’s a part of it.
…
Part of my career and part of the success, I think, was that the business plan I developed was to be an inch wide and a mile deep in this. It was, find this niche and try to be best in the world at it. Don’t expand the focus. It was North American Gas and Power. At some point, LNG started to become relevant and put a small team in Europe, but mostly for information flow for the North American Gas and Power group. There were numerous opportunities to get into oil, to get into metals, or agriculture. Or start trading energy equities, for instance. Every time I considered it, but, stick with the niche and just focus here.
I think the upside was, if we were successful with that, if that plan worked, and we were best in the world, it was going to be enormously profitable. The downside is that the intellectual curiosity starts to sag.
COWEN: In your niche, do you think the skills needed to be a commodities trader, in particular, are different from other kinds of trading? Or it’s just the same?
ARNOLD: I think it’s pretty similar. One of the great things about the natural gas industry for a long time — and it’s still largely true now — is it was a closed system. You could figure it out. It also had this forcing mechanism twice a year. The fundamentals had to align with price more or less twice a year — at the end of the injection season and end of the withdrawal season of gas. So, where price could deviate away from fundamentals for a time period, it had to come back at a certain time. It was a system that was conducive to being modeled. Apply smart trading on top of that, and it created a lot of opportunity.
That’s a perfect preamble to the commodity seminar…