Making more of data using AI

Commodity traders now have access to a wide and increasing number of data sources and significantly larger volumes of data of all types. New regulations such as REMIT, for example, that are designed to increase market transparency and reduce possible market manipulation, ensure that many more new types and sources of important data are now generally available. A recent proprietary survey conducted by ComTech Advisory on behalf of DataGenic concluded that for around half of those surveyed, the volume of data they have to deal with has almost doubled over the last two-years. Commodity traders will need to cope with and manage, an ever-increasing amount of data in the future.

Social media has also become a very popular medium over the last several years and arguably, it is increasingly a relatively untapped source of potentially useful intelligence for commodity and other traders. Social media outlets such as Twitter and the blogosphere are increasingly being utilized in other asset classes to provide traders with trading indicators and sentiment analysis. However, can social media really have any value for commodity traders and how are useful signals to be extracted from among all of the daily noise from such sources?

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