Nobody really knows, except for a few people who aren’t telling. There’s some clues in the mathematical backgrounds of the kind of people they hire though. Fifteen years ago they were snapping up experts in stochastic calculus. Nowadays, I’m not sure whom they’re hiring.
They're not exactly forthcoming, but it's not the hyperbolic mystery the mythos would have you believe. You can piece together what matters to them - in the abstract - by searching MathSciNet for papers published by their researchers prior to joining. For example, it's pretty easy to see they hired an information theorist away from Princeton in 2017.
But obviously that's not enough to reproduce everything they do, or else they wouldn't still be legendary. As it happens a lot of what makes them successful is not the sophistication of their trading algorithms and research, but also the sophistication of the execution and reliability.
As an aside, from friends there Renaissance hires researchers in three primary ways:
1. They have a small network of professors who they solicit for promising new PhDs willing to leave academia each year.
2. They watch professors and postdocs in specific specializations, and reach out to those whose research meaningfully interacts with a thesis they're interested in internally.
3. They send small groups to conferences to poach people working elsewhere in industry (particularly tech) whose work is applicable to their own.
They also do hire people who directly apply of course, but most hires are reactive. They especially like to hire people whose work or research looks like it might begin to encroach on their own, or is just notable and impressive. The math is certainly important to them, but that's just one dimension of it.
Would you say that their edge in algorithms comes mostly from signal processing or from ML? I'm guessing it's mostly signal processing and signal extraction?
I would be surprised if rentech's profits are based purely on algorithmic advantages. More likely, IMO, is they've curated some really good sources of alternative data and combined it with otherwise standard mathematical and technical trading techniques. They’ve probably also got a well-engineered pipeline for identifying that data, systematically evaluating it, and bringing it to production.
This is of course pure speculation. But I doubt all that alpha would come from mathematical differences alone. The market isn’t magic... it’s just a question of having access to the right information and the ability to capitalize on it.
Two Sigma has an entire team devoted to alternative data collection, with much worse results. Alternative data isn't as useful as you think- short term truth is only one variable in market prices.
I’m not saying they have access to a single magical data source. Rather, they’re very good at identifying when a source of data is valuable to them, and milking it until it runs dry.