In general I think working on taxonomizing failure modes is valuable. In the case of one of the meta generators of failure modes, proxy divergence, even more valuable.
Formalization generators: I often find it useful to think about which kinds of distinctions I can make in order to decompose a category. A few high level ones: split into variant and invariant parts, past/future asymmetry, descriptive/prescriptive parts, continuous vs discrete representation, implementation/algorithmic/functional level (Marr’s levels), complexity classes (in particular some strategies forcing other strategies into worse complexity classes), breadth vs depth first search spaces, and strategies differing due to beliefs about payoff distribution shape (incl. type 1 and 2 error penalties).
With that last one an object level example: knowing that the payoff distribution has changed before others because you’re the one who changed it (caused the proxy to diverge).
I like the generator of how markets might clear under some adversarial conditions and wonder what models quants have of this they might be willing to share.
I like the generator of how markets might clear under some adversarial conditions and wonder what models quants have of this they might be willing to share.
In the preprint paper - https://arxiv.org/abs/1810.10862 - I discuss a few examples of these failure modes that occur in practice. In finance, most of the discussed failures are ways to create “momentum ignition.”
Also, having done policy work on HFT, I found it’s really really hard to get quants to share any details about strategies. I suspect this would be doubly-true if it’s about manipulative strategies!
(And thanks for the other thoughts. I’m still working through what those generators’ failure modes would look like.)
In general I think working on taxonomizing failure modes is valuable. In the case of one of the meta generators of failure modes, proxy divergence, even more valuable.
Formalization generators: I often find it useful to think about which kinds of distinctions I can make in order to decompose a category. A few high level ones: split into variant and invariant parts, past/future asymmetry, descriptive/prescriptive parts, continuous vs discrete representation, implementation/algorithmic/functional level (Marr’s levels), complexity classes (in particular some strategies forcing other strategies into worse complexity classes), breadth vs depth first search spaces, and strategies differing due to beliefs about payoff distribution shape (incl. type 1 and 2 error penalties).
With that last one an object level example: knowing that the payoff distribution has changed before others because you’re the one who changed it (caused the proxy to diverge).
I like the generator of how markets might clear under some adversarial conditions and wonder what models quants have of this they might be willing to share.
In the preprint paper - https://arxiv.org/abs/1810.10862 - I discuss a few examples of these failure modes that occur in practice. In finance, most of the discussed failures are ways to create “momentum ignition.”
Also, having done policy work on HFT, I found it’s really really hard to get quants to share any details about strategies. I suspect this would be doubly-true if it’s about manipulative strategies!
(And thanks for the other thoughts. I’m still working through what those generators’ failure modes would look like.)