It's interesting how they've already spotted a number of possible problems with using an algo to do VC, but there isn't really a compelling solution yet.
There are a number of parallels with the time when I was trading fixed income at a hedge fund. We had a senior guy looking at the output of various opportunity scanners, and deciding what to do.
There's several problems with this approach.
- The human is always out to prove himself. If you don't override the system now and again, what's the point of you? This means the humans are always on the looking for some special one-off condition they can claim.
- The algo dev stops short of where he could go with it. You ought to be fully automating it, but you don't because you need to leave something on the table. There's a number of data problems that you just don't get around to solving because it's tedious and you aren't going to use it.
- The VC guys have a much worse data problem, by the looks of it. Not every startup will fill out the form. If they don't need your money, no form. If they crash early, no form. After they fill out the form, how do you track what happened to them? Seems like a big problem. Also if you're going to use ML you need a fairly large number of rows. Not just filled out forms, but also labels for how things turned out. And the more features you collect, the more labelled rows you'll want.
So there's a real risk of falling into the pseudo-systematic hole here. You take the data that you have and make conclusions that are very close to your initial priors. Basically you end up with stylized "facts" that aren't necessarily true, just believed.
Seems like a they've thought about these things though, will be interesting to see what happens.
I'll defend pseudo-systematic, for the sake of it if nothing else :)
Investment types with many rows of data, enabling truly systemic decision making are securities markets. Whether you are using ML, or a human analyst with a theory, the economic conclusion is the same. A securities market has lots of data. This lets traders price systemically. Pricing, investing & trading are the same thing, in a securities market.
Startup investing is not like that, generally. A person using their subjective faculties is heavily involved, biases and all. This exists because human's subjective cognitive abilities are not just delusion, they are a real cognitive ability even if flawed^.
Systemic & non-systemic systems have their strengths and weaknesses. Human biases are a big weakness for the systemic side. "Searching where the lights (data) are" is the big systemic one. A pseudo-systemic system is basically just a compromise. If the weaknesses of a non-systemic system are a major problem (the whole premise here is that it is), then it is not a stupid idea necessarily.
It doesn't even have to be all that sophisticated. Manual overides are not necessarily a bad system. They make it clear where subjective judgement was used, how often. At least you are aware that it has taken place.
I think the blind orchestra is a good metaphor. It identifies the kind of human bias you are trying to minimize. If one orchestra looks like a Viennese orchestra and the other looks like a hillbilly high school, you want to hide this from human judges. You want human judges to focus their subjective brains elsewhere. You don't want to create an objective test of "good music" and "bad music," because the definition you would be forced to use would suck. At least, the definition would be different, like a sport with rules. So, compromise.
I don't think it's impossible to think of pseudo-systemic investment strategies, based on a similar compromise.
In the end this is still an investment, and the stock purchase agreement will probably include terms that would require companies to upload their P/L each quarter. They could easily track progress based on that.
That doesn't tell you how firms you didn't invest in are doing. Presumably you want to know how well your selection algo is doing, and you can't without having an idea of how your non investments are going.
There are a number of parallels with the time when I was trading fixed income at a hedge fund. We had a senior guy looking at the output of various opportunity scanners, and deciding what to do.
There's several problems with this approach.
- The human is always out to prove himself. If you don't override the system now and again, what's the point of you? This means the humans are always on the looking for some special one-off condition they can claim.
- The algo dev stops short of where he could go with it. You ought to be fully automating it, but you don't because you need to leave something on the table. There's a number of data problems that you just don't get around to solving because it's tedious and you aren't going to use it.
- The VC guys have a much worse data problem, by the looks of it. Not every startup will fill out the form. If they don't need your money, no form. If they crash early, no form. After they fill out the form, how do you track what happened to them? Seems like a big problem. Also if you're going to use ML you need a fairly large number of rows. Not just filled out forms, but also labels for how things turned out. And the more features you collect, the more labelled rows you'll want.
So there's a real risk of falling into the pseudo-systematic hole here. You take the data that you have and make conclusions that are very close to your initial priors. Basically you end up with stylized "facts" that aren't necessarily true, just believed.
Seems like a they've thought about these things though, will be interesting to see what happens.