5 ms·
"Share-price time series going back decades still contain far less information than, say, the image data used to train Facebook’s facial-recognition algorithms.
by matchagaucho 9y ago
"Share-price time series going back decades still contain far less information than, say, the image data used to train Facebook’s facial-recognition algorithms."
What?
Clearly these ML models aren't trying hard enough. Correlating stock movements with the weather, geo populations, ... there are potentially infinite patterns buried in the data that a hedge fund could uncover.
- deepnotderp 9y agoThis is a fluff article and they know it, plenty of major quant firms analyze satellite images with neural nets to make trades.
- nsebban 9y agoDo they do that ? And why exactly do they do that ?
- mathperson 9y agoI can pay for high resolution images of every walMart in the US- Use math- estimate foot traffic- estimate average sales per person- I can estimate profitability of Walmart. Let's say it's a good quarter. I buy a bunch of Walmart stock - they then disclose quarterly earnings- investors see the good numbers- the share price goes up. My position is long-I sell. Profit. edit: obviously many steps of this process can use machine learning in general and deep learning in particular
- junkscience2017 9y agono you can't. take the absolute highest resolution images you can buy, and take 100 of the best mathematicians and using only maps you won't even be able to guess within 20%...analysts can already estimate within 1% of expected earnings
- forapurpose 9y ago> take the absolute highest resolution images you can buy, and take 100 of the best mathematicians and using only maps you won't even be able to guess within 20%... Why is that?
- mathperson 9y agothat example was more hypothetical of possible uses of satellite images. idk whether or not its too coarse to be a profitable signal https://www.cnbc.com/id/38722872 https://www.cnbc.com/id/38722872
- cocoablazing 9y agoWalmart’s last quarterly EPS beat analyst consensus by >3%, according to an aggregation by the WSJ.
- QML 9y agoWouldn’t it be easier to create an incentive for customers to directly give you data about where they shop? Like make an app that pulls up coupons specifically for Walmart..
- hu3 9y agoFor starters satelitte images can help infer crop farm size and health and use it as one of the many signals to predict corelated stocks.
- aaavl2821 9y agoi think some firms buy analyses of imagery to predict oil market movements by looking at whether oil storage containers are empty or full. could probably imagine doing similar things in other commodities markets (corn etc)
- tigerBL00D 9y agoYes, but not necessarily to trade stocks. There is a more direct use of this data - to predict demand.
- aaavl2821 9y agonot to trade stocks necessarily but to trade commodities
- bfrink 9y agoThere are about 30,000 institutionally-investable stocks in the world. They have ~252 closing prices per year. This is the thing you're trying to predict, so it forms the outputs of your training set (be sure to hold some back for out-of-sample testing and there are probably distinct regimes you need to switch between). Yes, there may be lots of features (weather, geo populations, etc., as you point out), but you're often hard up against the curse of dimensionality here. Sure, intraday data is more voluminous, but there are fewer economically reasonable influences on that.
- mathperson 9y agoAlso commodities, forex...
- lordnacho 9y agoThat only adds a few dozen items. There's only so many currencies and commods, bonds. Even if you include really illiquid stuff maybe you get 150 things.
- JumpCrisscross 9y ago> There's only so many currencies and commods, bonds Currencies are traded in pairs. Bonds are numerous, if illiquid off the run. Not to mention the bursting menagerie of derivatives our species tends to.
- lordnacho 9y agoHighly correlated. I mean you can add option surfaces, but to count each option as separate is a stretch.
- snowAbstraction 9y agoSome people are already trying and succeeding. And since a lot of these signals already incorporated into the market prices of various financial instruments, you can apply deep learning techniques to these available prices (and time series) to get similar results, to make buy and sell decisions/algorithms.
- IAmGraydon 9y agoYes, but the quote is talking about “share-price time series.”
- Cacti 9y agoJust 32x32 image space is, like, larger the the atoms in the universe. Even per-tick financial data is quite small in comparison. Or, from another angle, say you've got 10 ticks of a stock per second for 12 hours = 432,000 data points. This is the same as a single (relatively small) 380x380x3 image. If you assume there are roughly 30,000 instruments like this in the world of this volume, that's probably, I dunno, the number of images facebook sees in a second. Those are all guesses but you can see the scale of differences involved here.
- erikpukinskis 9y agoHello Frame Problem.
- sgt101 9y agoSome good answers in the thread below, but... 1. This is all observational data - even if you have all the features in the domain it's pretty clear that you won't have seen all of the generative theory. 2. You don't have all the features because the features change, renewable power suddenly makes average wind conditions relevant, and so on. 3. This domain is clearly chaotic; you can induce the various attractors and theories, but you cannot measure the conditions well enough to predict the shift between attractors. There are many people who are using ML to predict closed parts of the financial world where they have an deep and well founded understanding and excellent data. But there are lots and lots of people who are feeding data into models and getting good results and then going on good runs who are going to get a shock.