Effectively an outsider in finance who gathered a bunch of other outsiders (aka big mathematicians), and decided to start a hedge fund that takes zero interest in the actual companies and trades solely on math. Which makes sense, since none of the main people involved in its creation had any corporate or finance experience, but tons of math experience and knowledge.
This is oversimplifying it like crazy, but I recommend anyone to anyone with even a passing curiosity for this look up the details (or read “The Man Who Solved The Market”, which is documenting the beginnings and growth of RenTech, as well as that of Simons; very enjoyable read).
Successful quant stratégies tend to hit capacity limits...
Eg selling insurance can be seen as a zero sum game, but it's a genuinely useful product for people, even when the expected value for them is negative. It works, because utility is not strictly proportional to money.
Similarly, market making delivers liquidity-on-demand for a fee.
In practice, people take out insurance even for events that would not put them out of business.
Btw, if you are talking about 'value-generating enterprises', ie businesses as buyers of insurance, then your argument doesn't really work either, or at least not without caveats:
When a business suddenly has a large liability, and it goes bankrupt, all that happens is that the equity owners are wiped out and the creditors take over. The underlying business can and often does continue uninterrupted, and has approximately the same value as a going concern as before.
Also, being able to run as a going concern is of finite value to a business. If your business can take a 51% chance of either doubling in value or alternatively going bust, then that _might_ be a good gamble to take if your shareholders are well-diversified. For example, if index funds are your main shareholders.
Humans need considerable better odds before they consider such a gamble. But people do regularly put their life on the line in return for very finite benefits. Eg every time you leave the house, and drive a car. Or even more stark, any time people conveniently 'forget' to put their seatbelts on.
I am not saying that ALL insurances provide values. Like any other kind of trades, you can lose values if you make a bad decision. That does not make insurance inherently zero- or negative-sum.
> When a business suddenly has a large liability, and it goes bankrupt, all that happens is that the equity owners are wiped out and the creditors take over. The underlying business can and often does continue uninterrupted, and has approximately the same value as a going concern as before.
This assumes the original owner brings no value to the business. Even in that case, the disruption itself is harmful, not to mention the assumption that bankrupted business can restructure rather than closing down.
Closing down the business doesn't necessarily mean very much: all the machines, and workers and real estate and building still exist, whether the business closes or not.
One of the genious thing that rentech did was long out of the money bonds, and short newly issued bonds. Seems like such a simple strat, but when you crank up the leverage you can make alot of money.
It's easy to make a few high margin dollars, hard to make a lot of high margin dollars.
They also got into some tax trouble with uncle sam and had to pay 7b in back taxes (https://www.wsj.com/articles/james-simons-robert-mercer-othe...)
According to industry rumors, RenTech is somewhere between $10-20bn AUM (assets under management, i.e. the capital used for trading), and the profit that they make, they can't reinvest, they have to take it out as profit.
I know literally zero about this stuff!
This is of course a completely theoretical proposition, because in reality you don't know what the "fair price" is. You don't even have probabilities, because those are also unobservable, you only see one version of "history".
In practice, what happens is that if you trade "too much", "shit goes wrong". Both of these things require empirical estimation and are easy to get wrong.
The most obvious is the market liquidity, which you can observe at e.g. BitStamp TradeView [1] - there's only so many orders at a given price, so the more you trade, the worse price you get (the average/marginal trade).
No professional of course trades like that, especially not HFTs, but similar problems happen at every scale - you're competing with other traders, they might have better information, there's limited amount of stock in the market, the edge/alpha/expected profit you can earn decays over time as the price moves, if you trade too much you move the market and inform other participants who can then trade against you, ...
I guess you will not be getting a job there
But in seriousness, when you become so big relative to the market, you become the market.
I guess the theoretical limit to how much money you could make in the market is "the sum of all volatility", but I wonder how realistically possible it would be to even dream of beating 62% yearly.
The returns of modern HFT market makers are even higher. With their unfair “business” advantages such as PFOF, privileged dark pool and block trade access, and military internet infrastructure.
Think 60%+, per year, at least. Over 10-20 years, of course.
They wouldn't hire me either!
The returns of a child's lemonade stand are even higher...
Market makers and lemonade stands are mostly about paying for labour (and ideas etc, but let's call that 'labour', too). Capital requirements are rather low. So taking all the profit and attributing it to capital returns tends to give you weird numbers.
Why does it matter? Returns are returns. Money in, money out.
After all, people compare HYSA bank interest with TreasuryDirect bond returns with equity ETFs like VTI and QQQ. Each with vastly different capital mechanics.
Good luck trying that with one of those very profitable market makers and funds: they don't want your capital; or at least they don't want it at the same price (= returns) that we are quoting here. Which suggests that those returns aren't attributable to that capital at all (even though for tax reasons they might structured it so that legally these are counted as capital returns, but that just obscures the underlying economic reality).
This is very similar to observing that a particular company pays a lot of money for some very simple job; but then we notice that the job is only available for the son of the CEO. We can conclude that the extra pay isn't really for that simple job.
Or when we notice that a government contractor officially charges 5000 dollars for a hammer. Unless you and me could rock up and steal market share by offering to sell hammers for 4000 dollars, it's very likely that the 5000 dollars aren't really for the hammer at all; but just some accounting shenanigans.
It's kind of inspiring. I don't know a ton about finance or trading algorithms, but I know a fair bit (I think) about math and CS, and because of RenTech I've formulating my own trading strategies (just paper trades). Thus far all I've been able to do is lose all my pretend money by trying to play options, but it's still fun to try.
Will I be successful and make billions? Almost certainly not, but it's an excuse to play with different types of math that I don't play with very often, but RenTech proved that you can beat the market by taking advantage mathematics.
Simmons was one of the first to realize the advantage of collecting and analyzing vast sums of data to identify patterns in financial markets. They were digitizing magnetic tapes and collecting more data than they could even process given technical limitations of the time.
Basically, no.
They're different groups, I respect them now and feel dumb for not respecting them before.
It changed at some point during the '80s, probably to no little degree thanks to Renaissance.
The PhD I'm in is from a more prestigious university [1], and I guess FAANG experience isn't enough to snag an interview with them.
[1] University of York, though I don't know if that counts as "top tier" either.
Google harasses anyone with a live linkedin profile.
Getting to an onsite interview is a different matter though.
I know 3 people at google, all senior, either SE or people managers.
None had brilliant academic records.
One took almost 10 years to finish his sociology adjacent undergrad.
He's the most "successful" and has been there for almost 15 years, 10 in the states.
The others have been there for like 5.
Make of this info what you wish...
None had impressive intelligence either.
Both of them only really cared more about work history and my ability to solve whiteboard problems. Pretty much all the interviews ended up "what's another clever way to use a hashmap?"
Most organizations would have choked themselves on tens of thousands of bad hires long ago.
Combine that with how most the company spend all their time thinking about making money and that's probably why the company never succumbed to bloat
I also think they have sufficient career progression (in terms of problems solved and $$$ earned) that nobody feels the need to build a big team. Pure speculation though, I know nothing about RenTech except that the pay is... generous.
This is different from most orgs who can grow revenue through expansion of some sort, in which case the incentive often favors adding new employees. Not to mention the tendency for people in tech to be evaluated by how many people are in their org, further incentivizing adding headcount to signify your importance.
Obviously the number of people working on the super secret stuff is smaller, not to mentioned each project has a compartmentalized staff, but keeping secrets at this size is going to be a tough ask. They seem to do a pretty good job, but we know they’re not infallible at it. I imagine RenTech at 1/100th the size would have a vastly easier time.
Also just reading about Jim Simons' being an already-very-successful mathematician dropping everything to start a hedge fund and ending up extremely successful at the end of it was a bit of a wakeup call. Clearly this was an extremely smart dude (he was the chair of the math department at Stony Brook!), and so if this is interesting enough for someone like him, then it's probably something worth looking into.
I read through a book on basic trading strategies and I thought it was pretty interesting [3], though I've gone in a pretty different direction from what they taught.
[1] https://youtu.be/A5w-dEgIU1M
[2] https://youtu.be/xkbdZb0UPac
[3] https://www.amazon.com/Machine-Learning-Algorithmic-Trading-...
https://www.goodreads.com/en/book/show/43889703
https://www.goodreads.com/en/book/show/25733505
And some of the myths you have may be dispelled :-)
I mentioned in the very comment that you're replying to that I was wrong.
I had previously thought of HFT and Quant as a bunch of "finance bros", and kind of dismissed it as "not real CS" [1]. Reading about RenTech and Jim Simons made realize that there's actually a lot of really cool and interesting math and CS that goes into this stuff.
Jim Simons being a respected mathematician who just decided to change trajectories has always fascinated me, and it's sad that he's gone.
[1] I don't believe this anymore and I feel dumb for thinking it in the first place.