They automated signal discovery in what kind of data? I have heard their use of unconventional data sources is the source of their success, not automated signal discovery.
No, every quantitative firm uses unconventional sources of data. That doesn't meaningfully differentiate them (at least, not anymore). For example, Two Sigma has an entire division devoted to sourcing and processing "alternative data." But Two Sigma is not at all comparable to firms like RenTec.
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.
You mentioned these firms are typically smaller in headcount, use private capital, and generate similar returns. Any insight on how they're targeting asset capacity for their meta-strategy approach? The medallion fund, until recently was capped around 3-4 billion [1]. I wonder, if the quality and quantity of data itself, and the models built on that data, are subordinate to the data processing IP as you say, can this type of trading be done at smaller scale, either as an individual or handful of people? I've been out of the industry for a bit, but infrastructure and data costs were the main (financial) hurdles for startups, particularly in the high frequency space.
Are there any firms outside of finance using an analogous approach to data processing and signal construction?
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.