ham_sandwich

ah yes, a nice discussion of the central bank toolkit when policy rates are at their effective lower bound

wait, what's this? Bitcoin and ethereum tickers at the top of the page? A section titled 'Helicopter Money on Blockchain'? hold on a minute...

In fairness, the article did avoid a few of the 'not even wrong' pitfalls you might expect to see written about QE and other sovereign currency monetary operations

Quant is a pretty broad term. Some would say it’s often working on nonlinear desks to implement/calibrate volatility surfaces and things like that or working more on the risk management side. There’s also the whole HFT world (Jane St, Virtu, Jump etc) many would call ’quant’ but really is a different game than the HF space.

On the machine learning side, in my experience it’s often simple, linear models that work best in the messy world of financial data. I’m sure there are shops out there breaking out the GPU clusters and training NNs with 6 trillion parameters but in no way will your super deep NN guarantee alpha whatsoever.

Is an HFT arms race necessarily a bad thing? Doesn’t the market in general benefit from these firms viciously competing to grind out spreads+inefficiencies?

I know very little about HFT, but it seems like we’ve gone past “peak HFT margins”. With the Virtu+KCG merger, firms like Jump doing microwave stuff, it seems like return on assets for these firms could have already peaked.

A famous anecdote along these lines are the whatsapp founders.

I haven’t done so personally yet but it’s starting to look like I might consider this route.

Several years ago, I would have leaned toward a SaaS business targeting some super niche vertical, not to make 1000x VC style rocket-ship returns of course, but as a way to bootstrap into a nice business. I get the sense most verticals with a TAM worth going after are now very competitive.

Interested to see if people see opportunities in non-tech routes.

It seems like Tesla is at a pivotal moment.

On one hand there are hyper-bulls who claim Tesla is a $4000 stock and the future of transportation. On the other, hyper-bears claim the equity should trade around $0-$10. There seems to be no middle ground.

It seems like they are almost betting the company on FSD. I don’t think FSD is really even close to a possibility over the next 5-10yrs. I hope I’m wrong, but if I’m right, I don’t see how Tesla keeps going on like this.

Isn’t it just one big misunderstanding between them?

Taleb thinks 538’s probabilities represent a binary option price on the event, in which case, yes, the probabilities should stay very close to 50% because the vol is so high. Whereas Silver’s models are actually saying “Based on current polls, if the election were held tomorrow, then the probability candidate X wins is Y%” and are thus allowed to swing more wildly. Aren’t both just fundamentally different things or am I missing something in Taleb’s argument?

I don’t work at FAANG, but the general sense I get is that while there is no doubt top DL talent that should command mid six to seven figure salaries, it seems that with AI programs stuffed at both the undergrad and grad levels that things should cool off eventually.

More broadly, does success in academia usually translate to delivering business value? Are these companies betting on these researchers to come up with the next great DL architecture?

Looking at that odds chart got me in a betting mood. I think those Yang odds in the article could be steep given the exuberance of some in his online following, however there could be interesting arb plays with an outsider like Yang like going long presidency+short nomination paired with going short presidency+long nomination with someone more establishment like Biden.

If prediction markets had more depth, I’m sure we would see politics hedge funds emerge.

You’re right. Like a lot of other engineers, I once thought “ML+High level team info=$$$$” but quickly learned that you really can’t get an edge unless you’re digging into that more granular data and even then it’s really tough. It can be very hard to improve on simple linear models.

The odds coming out of Vegas are usually priced correctly. Sports markets are very efficient—although perhaps not as ruthlessy efficient as public equity markets. I would imagine there are still syndicates out there that are the “RenTec of sports betting” and just printing alpha.

I just heard about this on Patrick O’Shaughnessy podcast. As a coffee-fiend, I really should look into becoming a customer, and am also interested to see what offerings come next.

Reminds me of a Whitehead quote that I like that can also serve as a heuristic to evaluate business ideas:

“Civilization advances by extending the number of important operations which we can perform without thinking about them”

I love that literally everything is abstracted away from the customer. Stuff just appears like magic.

My votes are for:

-the use of fire

-the number 0. (Although we can debate if this was invented or discovered)

Wouldn’t large banks and other institutions be in favor of more regulations to deepen their moats?

I could see why they’d want to get rid of Volckler though, so the big I-banks can return to the serious pre-crisis trading revenues they were printing.

Just looking quickly at Goldman’s 2006 10K shows they had over $25B in trading revenue, compared to $5.6B from their traditional investment banking sleeve. I had no idea it was at that level, wow.

I’ve been interested in learning about topological data analysis, haven’t dug in too deep yet, but it definitely looks like an interesting direction to zig in while the field at large zags with ever larger deep learning architectures.

UMAP has already demonstrated its efficacy as a tool in any data scientist’s belt. Ayasdi and Gunnar Carlson’s work is certainly interesting, but unsure how much business value it can actually unlock. Seems like there is also opportunity to draw inspiration from the applied category theory crew (Spivak, Fong etc) to use some CT tools to approach data science from a fresh perspective.

Some of the research coming out is interesting, but as a practitioner I’m more interested in seeing how TDA can add differentiated value in a business context. Interested to hear where people see the field moving next.

With the rise of lambda+managed services, I think we’ll start seeing finance and development start to blend and merge.

Besides giving businesses more legibility into what specific parts of their business logic cost to operate vs. the value they generate, you can start building higher-order financial systems based on flows of capital+information within businesses. From there you can implement all sorts of financial engineering like insurance and options+derivatives that could allow businesses to do things like dial up leverage against these flows. Certainly half-baked ideas, but fun to think about the possibilities.

I think it is possible for that culture to exist on teams if it’s instilled by the coach. I have been on teams with more disciplined, formal coaches and people certainly weren’t as open about it. It would probably be frowned upon if brought up.

But I’ve also been on teams with more free-wheeling coaches and that attitude—ahem—trickled down to the rest of the team. And people would openly talk about it and just straight up do it.