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When it comes to using technology to be at the cutting edge, the quality of the data you are using is the name of the game. In trading that some times means spending tens or even hundreds of millions of dollars for data that gives you insight into the world that very few have. In this episode we bring on Rich Brown who has lead data and sourcing teams at some of the most successful and well hedge funds in the world to shed to light on this industry.
We talk about the different ways that data is used for discretionary and systematic managers. This can include everything from real time exchange feeds, data from bbg terminals, to the most exotic data you could image. We touch on how some alternative data is used to forecast not just price action but more fundamental performance of KPIs that a separate process may then use to forecast future returns.
We also discuss how to think about licensing data to train LLMs where the licensed data my be embedded in the model weights but not easily traced back to the original source. Rich points out that some of this is new but mostly already solved problems, at least contractually, where the products are conscidered derived work products and are likely covered depending on the licensing model used. There is a wealth of insight and we hope that you enjoy this episode as much as we did in creating it.
Hosted on Acast. See acast.com/privacy for more information.
By Michael Watson5
44 ratings
When it comes to using technology to be at the cutting edge, the quality of the data you are using is the name of the game. In trading that some times means spending tens or even hundreds of millions of dollars for data that gives you insight into the world that very few have. In this episode we bring on Rich Brown who has lead data and sourcing teams at some of the most successful and well hedge funds in the world to shed to light on this industry.
We talk about the different ways that data is used for discretionary and systematic managers. This can include everything from real time exchange feeds, data from bbg terminals, to the most exotic data you could image. We touch on how some alternative data is used to forecast not just price action but more fundamental performance of KPIs that a separate process may then use to forecast future returns.
We also discuss how to think about licensing data to train LLMs where the licensed data my be embedded in the model weights but not easily traced back to the original source. Rich points out that some of this is new but mostly already solved problems, at least contractually, where the products are conscidered derived work products and are likely covered depending on the licensing model used. There is a wealth of insight and we hope that you enjoy this episode as much as we did in creating it.
Hosted on Acast. See acast.com/privacy for more information.

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