41–50 of 59 posts
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Trying to get my credit union to allow users to opt in to sell bulk annon checking account transactions to a hedge fund. Having that much point of sale data would be huge.
uh no, please don't do that. Individual decisions that seem harmless can be incredibly destructive in the aggregate, and this is one of them.
I run a business in this space. Realistically your chances of having data that is useful to a HF (of any kind) is pretty low so I wouldn't bank on it as a revenue source unless you have a strong reason to believe (1) your data is predictive of something an investor cares about and (2) isn't already covered by other data.
Out of curiosity what resolution and time scale is useful. Is it fair to assume the most hedge funds are relatively good at tracking recent information and the value is in older archives that's hard to collect? Also are event streams and large connect event graphs like Forge.AI what sells actually useful? Curious as my phd research potentially has applications for information extraction and event linking. But not ent…
No, like he said the most valuable data has a signal that is independent from other data sets, which generally means something proprietary, and almost always without a long history. Having a cleaned archive of standard data with a long history is valuable but already pretty well served. It would be hard to compete with CRSP.
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Usually hedge funds prefer the raw data so their own research teams can do modeling and analysis.
Once they see the raw data they'll have a good idea how to get it. Why don't they just set up their own scraper at that point, instead of paying me?
The most valuable data is data that is difficult to gather. Think things like proprietary (i.e. unpublished) industry data. The canonical "sexy" alternative data set sold to hedge funds is counts of cars in retail parking lots from satellite photos.
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Could I send you a sample? I’m curious about certain data I’m scraping for a project.
If you shoot me an email I can tell you very quickly if it's viable, and direct you to specific people at firms who would buy it. I probably don't need a sample if you give an honest description of it.
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Usually hedge funds prefer the raw data so their own research teams can do modeling and analysis.
Once they see the raw data they'll have a good idea how to get it. Why don't they just set up their own scraper at that point, instead of paying me?
Time spent on maintaining a scraping operation is time taken away from optimizing your ETL process and producing actionable research for your trading team. You know how people pay to have their pipes unclogged even when they know how it's done? Same idea.
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Once they see the raw data they'll have a good idea how to get it. Why don't they just set up their own scraper at that point, instead of paying me?
If all it is some data scraped off a few web sites that they could get an intern to do in a week or three, then it's unlikely to be valuable enough for them to pay you a substantial sum of money. The most valuable data is data that is difficult to gather. Think things like proprietary (i.e. unpublished) industry data. The canonical "sexy" alternative data set sold to hedge funds is counts of cars in retail parking lo…
If the data is compelling and clearly correlates to earnings KPIs, I can tell you from experience that "some data scraped off a few websites" can be salable to the tune of $50,000/quarter. Hedge funds will frequently choose to pay that instead of setting up their own scraping operation, because scraping sucks.
Not all hedge funds, of course. Some do actively try to reverse engineer your dataset. But you probably don't want to work with those anyway.
> The canonical "sexy" alternative data set sold to hedge funds is counts of cars in retail parking lots from satellite photos.
I've personally developed forecasts from (ostensibly) public, scraped sources which beat drone and satellite footage of manufacturing facilities. That one Bloomberg article is not representative; satellite footage sounds sexy but it's not what most alternative data looks like.
Can somebody give an idea of what are the most common alternative data sets that are the most widely used?
1. Social Media Data - Twitter mentions etc 2. Website scraping (trolling linkedin, alexa, github) for evidence of activity 3. Credit Card Data - aggregate and POS data 4. Satellite Data - How many cars in the Walmart parking lot. Those are a couple of the more shiny alternative data set in terms of interest. A lot of quants also pay big bucks for more micro or hft data.
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1. Social Media Data - Twitter mentions etc 2. Website scraping (trolling linkedin, alexa, github) for evidence of activity 3. Credit Card Data - aggregate and POS data 4. Satellite Data - How many cars in the Walmart parking lot. Those are a couple of the more shiny alternative data set in terms of interest. A lot of quants also pay big bucks for more micro or hft data.
Credit card data would be the keys to the castle if terms of predicting revenue for a company.
Can somebody give an idea of what are the most common alternative data sets that are the most widely used?