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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.
Is it better to provide the raw data, or to instead provide some interesting statistics from aggregating or running a model on the data?
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.
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.
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True. I'd ask: "If this data is truly useful in a financial market context, why are you selling it instead investing with it yourself? What data are you collecting that you're not* selling?"
Because sometimes the data is useful when combined with other data you might not have yourself. Example: you have data on number of Netflix subs. That’s great. It would be even more useful if you combine that with Netflix churn data. You can think of company analysis as a jigsaw puzzle. You may have a few pieces. That by itself isn’t useful unless u have the other pieces.
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That's why start-high and walk away is a fair opening gambit in negotiations of this sort. You can always come back around, but if they pursue you, that tells you something about their appetite.
I mean, I guess that's something that you can do but it's likely to get you laughed out of the room and greatly encourage them to reverse engineer whatever you were trying to sell.
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Is it better to provide the raw data, or to instead provide some interesting statistics from aggregating or running a model on the data?
Usually hedge funds prefer the raw data so their own research teams can do modeling and analysis.
The article mentions having a long history of data, but I want to stress a corollary of this: if you think you might eventually want to sell data, either put your data in an append-only data format now so that history is preserved, or at least take regular snapshots. Otherwise, any time you update the data in place, you make it impossible to reconstruct what the data "looked like at the time" for a backtest.
Can somebody give an idea of what are the most common alternative data sets that are the most widely used?
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.
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.
I recently published a data dashboard( https://public.quantale.io/dashboards/50778f4a-02bb-4d31-b6e... ) using Quantale[1] to show the correlation between Tweet activity of a stock vs the price during the recent stock split of Tesla and Apple. While I believe there are softwares that can collect such data which can definitely be useful to hedge funds but at the same time we should note that the data available to gene…