dsacco (u/dsacco) - Readit News

dsacco commented on Rich Formula: Quant Trading (2015) forbes.com/sites/nathanva... · Posted by u/luu

I guess my only followup would be are these "techniques" more akin (in broadness) to ResNet, or to Dropout? (to use an area that I believe we're both familiar with)

In other words, techniques that are broadly applicable to the field, or techniques that maybe spawn a family of related techniques, but appear to be useful only in a specific subdomain.

dsacco · 7 years ago

That's a good comparison. In general, closer to Dropout.

dsacco commented on Rich Formula: Quant Trading (2015) forbes.com/sites/nathanva... · Posted by u/luu

joshuamorton · 7 years ago

I mean I have little doubt that there are trade secrets that these companies have. Specific algorithms and models. And yeah, industry labs are often ahead here.

But I read your claim as saying that there are broad methods and approaches that they hide. And that's, while possible, more peculiar. Most of the tech industry labs don't keep their theoretical research secret. Practically anything that could be published is.

As for 3, the way you described the "rediscovery" made it sound like those Labs were a number of steps ahead, so I hope you pardon my misunderstanding.

dsacco · 7 years ago

At the highest level there are broad approaches which are kept secret in the financial industry, but the reason that's peculiar is because their efficacy is inherently antagonistic to publicity. Tech firms (mostly) don't lose utility of their trade secrets if they're exposed, they just lose first mover advantages on those techniques. But if everyone is aware of your techniques in finance, your techniques cease to have an edge.

Like I said in the original comment: this isn't (to my knowledge at least) pure mathematics that's being kept secret. But there are absolutely families of techniques and algorithms whose applications to finance are nontrivial, non-incremental and very well guarded.

dsacco commented on Rich Formula: Quant Trading (2015) forbes.com/sites/nathanva... · Posted by u/luu

madengr · 7 years ago

These guys averaged a ~9% return over the last few years. The S&P 500 has exceeded that. So what’s so special? Seems that passive investing works just as well.

dsacco · 7 years ago

> These guys averaged a ~9% return over the last few years.

Who are "these guys"? The funds discussed in the article have average annual returns well above 9%.

dsacco commented on Rich Formula: Quant Trading (2015) forbes.com/sites/nathanva... · Posted by u/luu

downandout · 7 years ago

Could somebody explain why so much effort is being put into quant strategies, when it seems that real-world information gathering would be a much easier way to gain an edge over others? Let’s say you pay to place a camera on a building next to a given company’s factory, and use analysis software to count the number of trucks coming and going from the factory to predict their order flow and earnings. This kind of thing is harder to scale up, but also gives an edge because not everyone else is doing it.

In an age when all hedge funds have the resources to hire the best and brightest engineers and buy the fastest processing hardware, it seems that none of them will have an edge if they are all starting with the same publicly available data.

dsacco · 7 years ago

> Could somebody explain why so much effort is being put into quant strategies, when it seems that real-world information gathering would be a much easier way to gain an edge over others?

I used to be part of a research group that sold the so-called "alternative data" you're describing to 30 or so hedge funds in the NYC area, including several of the largest. The example I like to give is that we knew well ahead of time that Tesla would miss on the Model 3 because we knew every vehicle they were selling by model, year, configuration, date and price with <99% accuracy. I still occasionally sell forecasts like this and the methodology is straightforward enough that even a solo investor can consistently beat the market if they know how to source the data. But I've mostly lost faith in this technique as the sole differentiator of a fund's alpha.

Some funds, like Two Sigma, have large divisions with a very sophisticated pipeline for this kind of analysis. They do exactly what you describe. For the most part it works, but there are several obstacles that keep this from being the holy grail of successful trading:

1. First and foremost, this analysis is fundamentally incomplete. You are not forecasting market movements, you're forecasting singular features of market movements. What I mean by that is that you aren't predicting the future state of a price; if the price of a security is a vector representing many dimensions of inputs, you're predicting one dimension. As a simple example, if I know precisely how many vehicles Tesla has sold, I don't know how the market will react to this information, which means I have some nontrivial amount of error to account for.

2. This analysis doesn't generalize well. If I have a bunch of information about the number of cars in Walmart parking lots, the number of vehicles sold by Tesla (with configurations), the number of online orders sold by Chipotle, etc. how should I design a data ingestion and processing pipeline to deal with all of this in a unified way? In other words, my analysis is dependent upon the kind of data I'm looking at, and I'll be doing a lot of different munging to get what I need. Each new hypothesis will require a lot of manual effort. This is fundamentally antagonistic to classification, automation and risk management.

3. It's slow. Under this paradigm you're coming up with hypotheses and seeking out unique and exclusive data to test those hypotheses. That means you're missing a lot of unknown unknowns and increasing the likelihood of finding things that other funds will also be able to find pretty easily. You are only likely to develop strategies which can have somewhat straightforward and intuitive explanations for their relationship with the data.

This is not to say the system doesn't work - it very clearly works. But it's also easy to hit relatively low capacity constraints, and it's imperfect for the reasons I've outlined. You might think exclusive data gives you an edge, but for the most part it does not (except for relatively short horizons). It's actually extremely difficult to have data which no other market participant has, and information diffusion happens very quickly. Ironically, in one of the very few times my colleagues and I had truly exclusive data (Tesla), the market did not react in a way that could be predicted by our analysis.

The most successful quantitative hedge funds focus on the math, because most data has a relatively short half-life for secrecy. They don't rely on the exclusivity of the data, they rely on superior methods for efficiently classifying and processing truly staggering amounts of it. They hire people who are extraordinarily talented at the fundamentals of mathematics and computer science because they mostly don't need or want people to come up with unique hypotheses for new trading strategies. They look to hire people who can scale up their research infrastructure even more, so that hypothesis testing and generation is automated almost entirely.

This is why I've said before that the easiest way to be hired by RenTech, DE Shaw, etc. is to be on the verge of re-discovering and publishing one of their trade secrets. People like Simons never really cared about how unique or informative any particular dataset is. They cared about how many diverse sets of data they could get and how efficiently they could find useful correlations between them. The more seemingly disconnected and inexplicable, the better.

Now with all of that said, I would still wholeheartedly recommend this paradigm for anyone with technical ability who wants to beat the market on $10 million or less (as a solo investor). A single creative and competent software engineer can reproduce much of this strategy for equities with only one or two revenue streams. You can pour into earnings positions for which your forecast predicts an outcome significantly at odds with the analyst consensus. You can also use your data to forecast volatility on a per-equity basis and sell options on those which do not indicate much volatility in the near term. Both of these are competitive for holding times ranging from days to months and, with the exception of some very real risk management complexity, do not require a large investment in research infrastructure.

dsacco commented on Rich Formula: Quant Trading (2015) forbes.com/sites/nathanva... · Posted by u/luu

joshuamorton · 7 years ago

Do you have any proof of this, or is this or is it just your opinion that the comparatively smaller groups of researchers at hedge funds are well ahead of academia and the rest of industry?

dsacco · 7 years ago

1. I don't have proof I can share publicly,

2. It's not just my opinion, and

3. I didn't say they're "well ahead" unilaterally.

This isn't unique to finance; industry labs in tech also often have novel results in applied mathematics and computer science that are ahead of academia and other industry labs. You don't have to believe me but it's not exactly a controversial topic. Not everything is published or patented.

dsacco commented on Rich Formula: Quant Trading (2015) forbes.com/sites/nathanva... · Posted by u/luu

twic · 7 years ago

> idea generation is equal parts people, ie brain power, and platform, ie the ability to iterate

To be clear, when you talk about the platform, you mean the research platform used to develop the idea, rather than the execution platform used to apply the idea, right?

What is there to such a platform? What does it actually do? Is it just a question of pumping historical data into a model and measuring its performance?

dsacco · 7 years ago

This is precisely the kind of question for which you won’t find any meaningful, public answer. I’d be thoroughly shocked if you could find someone in the know to give you an answer even anonymously.

dsacco commented on Rich Formula: Quant Trading (2015) forbes.com/sites/nathanva... · Posted by u/luu

cheez · 7 years ago

It seems possible that mathematical breakthroughs are no longer being published, as they are now trade secrets/matters of national security. I wasn't surprised when Tao was beaten by a hedge funder.

dsacco · 7 years ago

This has been the case for a long time in applied mathematics and computer science (not so much pure mathematics). There are hedge funds using work that is not only unpublished, but also unknown to research labs like FAIR and Google Brain. The easiest way to be scouted by one of those funds is to publish research that looks like you’re on the verge of re-discovering their work.

dsacco commented on The Artificial Intelligentsia thebaffler.com/salvos/the... · Posted by u/jamiehall

bamboozled · 7 years ago

Some people enjoy writing and some people enjoy reading. It need not be "to the point" all of the time.

dsacco · 7 years ago

I should clarify: I don't mind "unfocused" writing like this. I can definitely appreciate a creative take on exposition. But I think the introduction of an article is not the most appropriate place to do it. An upfront paragraph - even a few sentences - explaining what is happening would basically resolve this for me.

dsacco commented on The Artificial Intelligentsia thebaffler.com/salvos/the... · Posted by u/jamiehall

dsacco · 7 years ago

Offtopic, but I have a really difficult time reading articles like this. I don’t know if this reflects a problem with the style or my ability to focus, but I find it really annoying:

> “SANDHOGS,” THEY CALLED THE LABORERS who built the tunnels leading into New York’s Penn Station at the beginning of the last century. Work distorted their humanity, sometimes literally. Resurfacing at the end of each day from their burrows beneath the Hudson and East Rivers, caked in the mud of battle against glacial rock and riprap, many sandhogs succumbed to the bends. Passengers arriving at the modern Penn Station—the luminous Beaux-Arts hangar of old long since razed, its passenger halls squashed underground—might sympathize. Vincent Scully once compared the experience to scuttling into the city like a rat. Zoomorphized, we are joined to the earlier generations.

This goes on for about seven paragraphs before I have any idea what the article about. I understand “setting the scene” but I can’t tell whether or not to care about an article if it meanders about with this flowing exposition before indicating what its central thesis is.

It seems like a popular style in thinkpieces and some areas of journalism. The author makes a semi-relevant title, provacative subtitle, and five - ten paragraphs of “introduction” that throw you right into the thick of a story whose purpose doesn’t seem clear unless you know what the article is about. Rather than capturing my attention with engaging exposition, I find it takes me out of it. But it must work if it’s so uniquitous; presumably their analytics have confirmed this style is engaging.

dsacco commented on Y Combinator, Backer of Dropbox, Vaults from Experiment to Kingmaker wsj.com/articles/y-combin... · Posted by u/dopeboy

austenallred · 7 years ago

Of course there’s a lot of nuance there. But certainly we can all agree on two things:

1. YC has not had good returns because it has only had one IPO (apparently selling Twitch and Cruise for $1B each don’t count as a win?)

2. While it’s difficult to know what the true value of YC companies is, the fact that there are nearly 100 companies valued at $100m+ is not just a “vanity metric.” Especially st the later stages of more mature companies there are real dollars trading hands and there’s more liquidity available on secondary markets.

dsacco · 7 years ago

Sure, I agree with those two points.