Is anything interesting happening in quant finance right now?

[involution]

@ronin



Thanks for your very interesting perspective. Are you saying that using alt-data to estimate "known unknowns" (sales, margins, growth, etc...) is likely to constitute insider trading? Is that because such data sold by vendors may be prohibitively expensive (and hence not considered publicly available?). What do you think about the widespread use of expert networks (also quite expensive)?

[chiral3]

Today is a great day. It feels... normal again.

[ronin]

@involution,



Public companies have to release their earnings to all investors at the same time. You can look them up on their websites, on the relevant exchange websites, on yahoo finance etc. But generally speaking, the fact that there may be a subscription fee attached doesn't change the status of the data. If it is published to paying subscribers, it is considered to be in the public domain and it can be traded on.



To answer your first question, if you can reliably reconstruct confidential numbers, then there is a breach somewhere. Either you are effectively an insider even though you may think you are not, or the company has published some information that it shouldn't have. Either way, the legal risk is high.

[jslade]

Fintech is an astounding shit show. People in this space are insanely, preposterously clueless as to very basic concepts like risk models ... hell linear regression and discounted cash flow models. They're still eating up business of classical finserv companies simply by putting it all on a website and burning VC money. If you want to make a big difference as a quant, go fix one of these companies.



Crypto: most of the shit you see are, as you expect, speculative bubbles on bullshit. I still have made ridiculous returns on it; I'm kind of in shock this is how I've managed to make my number. The trading stuff; there is plenty to do here. Old fashioned algos which doesn't work in equities or futures any more work just fine in crypto for trading. There are also ordinary services to provide; making options markets or finding liquidity for example. For trading, the biggest problem is exchange risk. It's actually a very big problem; the Bitfinex/tether/goofytoken thing is only scratching the surface of what's going on here. It may come out eventually; I should encrypt something and stick it here so I can say "I told you so" in two years without giving it away. There are also legitimate ways to use this technology to bring banking services to underserved communities (I'm advising a company doing this). And of course, it's also useful having a sort of distributed SMTP for money.



Old school quanting: there are at least 2 newish things in stock market "analysis" -one of which chiral alluded to (using machine learning on regulatory/filing/sentiment stuff), the other of which is basically modeling cash flows in different kind of corporations using Bayesian techniques, aka knowing what your ops cash flow and accrual components are and taking positions on these. Probably 1-2 others I haven't heard of; side info and so on -I'm mostly an outsider here, but people talk to me. I think most of the juice in stock market stuff now requires fairly large organizations to take advantage of; aka no more 5-10 man crews at SAC. I still think there are lots of interesting but illiquid opportunities in futures and options for teams of this size.



FWIIW I started writing papers in quantum computing in 2003. I don't think the field has matured at all since then, and I'm coming around to the belief that it's all a socially gamed cargo cult the way noodle theory was. If this was real, people would invest in hardware which works, but none of the hardware seems to work any better than it did at the Dartmouth Gordon conference I went to back in 2001 or 2002 or whenever it was. Meanwhile hundreds of half frauds bloviate theory articles on this subject; achieving tenure in a subject which is about an imaginary object that may never exist. Imagine if there were hundreds or a thousand computer programmers or "computer scientists" before anyone actually built a usefully functioning computer. That seems the present situation in this field. Would be happy to be proved wrong. Would also love to see a historical example of any actually existing technology which has developed in this way.

[deeds]

jslade - thanks for the excellent insights



"basically modeling cash flows in different kind of corporations using Bayesian techniques, aka knowing what your ops cash flow and accrual components are and taking positions on these."



Possible to give any references or pointers for this type of theory/practice in context you mention?



Are these no arbitrage (risk neutral etc) approaches or forecasting approaches?



thanks



EDIT: added question

[mtsm]

Also, what is your take on DLT jslade? Thanks.

[Osiris2]

Here's a thing ...

Lots of what we are calling "fintech" companies, which I would more say are more accurately specialty finance non-bank lenders, are selling their capability to improve on legacy credit underwriting and analysis. As of now, this remains 60% hype, 40% bullshit, and approximately 0% reality.

But that won't remain the case forever. The industry and it's biggest players have reached the scale where they will have to deliver on this promise soon, or else acknowledge they are just certain pieces of a bank with a shiny front-end and give back all the "value" they created by claiming otherwise.

That's a quant problem, and a hard one. Anyone that can improve on the predictive power of current credit scoring, do it at scale, and do it for lower marginal cost (whether in consumer unsec., real estate, or SME) should be able to turn that into real money.

[NeroTulip]

It recently occurred to me that not much interesting is happening, or I am out of the loop... A friend is doing a PhD in machine learning after a dozen years in trading, and is looking for a research topic applying deep learning to finance. I was like "meh". Am I missing something?

[jslade]

@deeds I don't have any pointers beyond "get good at Bayesian modeling." A dumb starting point would be something like Steve Skienna's book on modeling horse races.

@mtsm I don't know what DLT means. If it's some new shitcoin, I haven't thought about it at all.

[mtsm]

Distributed Ledger Technology. I am being told it's no longer civilized to talk about blockchain, for whatever reason.



I meant to ask if you had a take on start ups pushing this sort of tech in a proprietary way for various applications, first and foremost smart contracts for finance I suppose?