HackerTrans
TopNewTrendsCommentsPastAskShowJobs

fractionalhare

no profile record

comments

fractionalhare
·5 年前·discuss
Pension funds are not buy and hold as a rule. They also invest in a variety of other vehicles, including hedge funds with different strategies. I guess you could say they buy and hold in the hedge fund, but that's different. Very conceivable that a pension fund had a meaningful uptick because one of the funds it invests in timed GME's crash well.
fractionalhare
·6 年前·discuss
Seems like another case where a successful Chrome extension was bought out so it could be used for either:

1. Mining the users' traffic and reselling it as market research, or

2. Using the users' computers as a pool for a residential proxy service, or

3. Replacing and inserting ads into users' browsers.

This is unfortunately quite common.
fractionalhare
·6 年前·discuss
Yeah, yeah. That controversy has been litigated on HN a dozen times already, I'm not going to rehash it. Do you dispute my primary point here? If so, why?

(Also, even if I agree it was purely intended for tax avoidance, I don't understand why you think that would obviate having to understand how the options work intimately well).
fractionalhare
·6 年前·discuss
I can see why someone would characterize RenTech that way but it's not really fair to do so. There is a lot of mythos about how Simons hired computer scientists, mathematicians, signal processing and NLP experts, etc. When Mercer came over from IBM, he definitely contributed a significant amount of analytical expertise that was probably nonexistent in financial trading at the time (with the possible exception of the Ed Thorp diaspora). The astrophysicists RenTech hires every year bring new insights in ways to model and understand vast amounts of data with absurd dimensionality.

But all of this has to be utilized in the context of the data. The reality is that you're not going to develop a sophisticated options trading strategy without a strong understanding of what an option (and more generally, a derivative) is. You can't develop a viable statistical arbitrage strategy just by treating market microstructure as a blackbox signal to be solved with e.g. Fourier analysis. You can certainly find an edge in using fundamentally superior methods of analysis, but you still need to know what that data represents in the context of the market.

Don't be fooled: people working at firms like RenTech have a strong understanding of the underlying finance. It's just that they learned it on the job, because the ethos at these firms is that learning fundamental theory in math and statistics is harder than learning fundamental theory in finance. You don't have to take my word for it though. Read about one of the few strategies of RenTech's which has been publicized: https://www.bloomberg.com/opinion/articles/2014-07-22/senate.... Deutsche and RenTech didn't team up on this strategy (to fantastic success) by treating basket options as some kind of blackbox abstraction devoid of delta, gamma, theta and vega.
fractionalhare
·6 年前·discuss
Yes, it's overwhelmingly unlikely that the winning model will actually be a competitive trading strategy.

Kaggle encourages a domain agnostic approach to modeling, in the sense that participants use sophisticated machine learning and statistical methods but typically have no domain expertise in the underlying data. This kind of approach to finance has historically performed poorly. [1]

Good quantitative trading is usually backed by a strong fundamental thesis and an interpretable model, which is obtained by cross-pollinating sophisticated math and statistics with domain expertise in some part of finance. That domain expertise might be in different kinds of assets, liquidity or market microstructure, but it's there.

$100k is cheap for Jane Street. If nothing else they have a new recruiting pipeline of people with demonstrable machine learning skills.

______________

1. I would also say this is a poor way to approach statistical analysis in most domains, and usually leads to spurious or overfit results. But the idea that you can just run a model and find patterns in pricing data is especially attractive and insidious.