Do you know how these algo funds keep a competitive edge for so many years (e.g. Renaissance 30+ years)? I am trying to understand their "kind" of product innovation as in what are the biggest factors they are continuously trying to improve?
When most funds say they're "quantitative", what they really mean is that they use huge amounts of data to inform fundamentally manual trading strategies (this includes most of the places widely considered to be "top" firms). They develop trading algorithms, and those trading algorithms are often successful. But the algorithms are developed manually and then deployed. Their researchers and engineers actively seek out new sources of data and try to compete on novel sources of untapped information. But the reality of what happens is that they simply drown in the data. They can't clean it or process it nearly fast enough to maintain long term trading strategies, nor can they even begin to find a way to automate the trading strategy extraction. If you're working with hundreds of terabytes of data, you cannot selectively formulate hypotheses and test them. It's far too slow. You will find dramatically fewer novel insights than a fully automated process.
In other words, they're a step above traditional "fundamental" hedge funds, but they focus on the wrong problem (but not for lack of trying!). In contrast, the truly successful quant funds have automated the data processing and feature extraction pipeline end to end. The data is a pure abstraction to them. They don't bother with forming hypotheses and trying to find data to test them, they allow their algorithms to actively discover new correlations from the ground up. So many quantitative funds advertise how much data they work with, and how they have all these exotic sources of data at their disposal...but the data does not matter. The models for the data do not matter. The mathematics of efficiently processing that data are what matters.
As a result of their consistent profitability, most of the jobs you see listed for the really successful funds (if they have a website) are not "real" in the strictest sense of the word. You can apply to them, but they only keep active careers pages to attract the best researchers. Their only incentive to hire is to 1) keep someone who is actually exceptional from joining a competitor or 2) keep an academic researcher from re-discovering their work when they seems like they're getting close to it. This is why they primarily focus on quantitative PhDs in information theory, high energy physics and computational mathematics (especially information geometry).
To be completely frank, Renaissance is an outlier, but not just because of their returns. They're an outlier because of how public they are. Most of the funds with comparable returns not only don't take any outside investor capital, they only have 25 - 50 employees. They virtually never hire because they don't have to. If your work is fundamentally interesting, novel and applicable to what they're doing (even if you can't immediately see why), they will call you.
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1. Other more secretive (but equally successful) funds have done this, but they are much more under the radar.
The funds I'm talking about (including RenTec) take in as much unstructured data as they can possibly find, almost indiscriminately, and they tune their processing pipeline to the point that it requires neither manual classification nor munging. In most cases, a trading strategy is sufficiently multidimensional that any particular set of data can be completely public. Exclusive data is helpful, but not required. In many cases people become too dependent on exclusive data and lose sight of the methodology.
This is precisely what I mean: many people think that these firms differentiate based on the sources of data they use. They do not. They differentiate on their ability to automatically extract signals hiding in plain sight. Whether or not the data is public makes very little difference, because the signals come from tens of thousands of indicators combined together.
Are there any firms outside of finance using an analogous approach to data processing and signal construction?
[1] https://www.bloomberg.com/news/articles/2017-08-16/renaissan...
Second, you mention knowing the names of various comparable firms to RenTech. Can you name some of those here?
1. I'm friends with multiple people who used to work at Renaissance, and I've directly spoken with folks who are currently there (among other, similar firms).
2. I've read the research published by professors and post-docs before they were hired.
3. I have first hand experience developing forecasts for various market research firms and many hedge funds. I've seen first hand what the difference is between the firms that say they're quantitative and the ones that are quantitative.
Not to discourage you (and you probably already know this) but: since you've already worked in the industry, Renaissance is very unlikely to take you seriously as a candidate. I'm not sure if that's what you meant when you said you're interested, but I figured I'd put it out there.
In any case, contrary to popular belief it's possible to connect the dots on what firms like RenTec do. But just having a high level idea of how they work isn't nearly enough to replicate their success. Their success relies on a large number of interdisciplinary scientists cooperating with the support of an incredibly specialized infrastructure. Getting the cliff notes on how they achieve e.g. dimensionality reduction doesn't come close to cutting it.
Do you have any other keywords? “Learning from few examples” comes to mind...
But reputedly they neither allow external money into Medallion nor do they let employees compound their existing stake in the fund but pay out ginormous 20%-40% annual dividends exactly because the strategy is not scalable.
The holy grail of investing would be a quantitative model which automatically changes and consistently outperforms everyone else.