How are non-quant quant portfolio managers possible?
I've recently been interacting with a few quant PMs who run decent sized books at tier 1/2 MMs. These guys are all from the old school days of bank prop trading desks (they also used to have quant prop teams). One bizarre phenomenon I've noticed is none of these PMs seem remotely technical (i.e. they couldn't tell you basic things like Lagrange multipliers or what a SVD of a matrix is) and yet they've had long careers in the industry.
I guess my question is two-fold:
1) What is the value add of such a PM to quant strategies? Surely their books must have some discretionary, "feel-based" component for these PMs to be useful. If so, then you're in some hybrid of systematic and discretionary right?
2) What the fuck were the interviews like in the 90s to get on the quant prop trading desks of bulge bracket banks? Was it all non-technical stuff? How did these desks sustain themselves/differentiate their strategies from the other non-quant prop desks if this was the case?
Real question is:
How knowing the Lagrange multiplier helps u make money ?
You're missing my point - lagrange multipliers here is just a placeholder for basic math concept you might learn in a college math degree.
Yes and I am asking you how those theoretical concepts would help u make money ?
There are a few very sophisticated quants out there doing intraday/HFT trading on the buy side. If u interview with them don't worry they will ask you super technical questions. Got an interview where I was handled a sheet of paper and had to do maths and demonstrate things. So this exists.
But most quants use basic maths and the hard thing is to understand the markets and find the potential drivers.
Moreover managers/heads of teams no longer code nor do maths. So he may have known 10/15 years ago and forgot. I have been working for 7 years and already forgot a lot of things.
Have you ever considered that it actually takes a discretionary decision to decide whether to implement a quant strategy?
This is new to me - I was always under the impression that you backtest some signal and it shows promise then it's probably a good candidate for implementation. Can you elaborate on this?
Yes ur idea is the right one.
The way to define "it shows promise" is the discretionary part
Quant is clearly better than non-mathematical humans when trading highly liquid products at short-to-medium frequencies. But it's not at all clear if quant is as good as humans on longer horizons, or trading around events like earnings announcements where humans may have market color unavailable to a quant algo. A discretionary trader who plays golf with the right people can still find alpha in places that quants can't reach.
re 2nd question, it's just a more developed field now with more competition to get an edge. 30yrs ago you had john daly winning PGA events while being an alcoholic and chain smoking, now half the tour is jacked from hitting the gym every day so that they can squeeze out a few extra yards on their drive.
Man being a quant starting out in the 90s must have been great
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