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One of the most vague issues in the art market is price formation, to date there has been no attempt to create a clear and regulated structure related to this matter, since price fixing is a major tool of manipulating margins in the private art deals.Transparency as a top priority for Z-gen collectors and a new generation therefore the overall trend of the last years was to try to use Big Data for valuations, estimations and price predictions. Normally it is transactional data from publicly available auction sales. However only 5% of overall transactions are going through publicly recorded auctions, the rest is done in closed ecosystems or private deals.


We are looking at a solution to use non-transactional data for price formation and updates, such as giving subjective estimates to non-monetary metrics that are directly correlated to an artist's career performance. While it would make perfect sense to try and use the AI and machine learning algorithms to perform such valuations, in the Beta version of Artsted we will start off with a simple calculation formula that will be based on submitted CV entries.


Artsted provides a visual representation of a user's career over time, as reported by the user himself. The visual representation of the form of a  "Performance Chart"  that will be reflected in the Artist's public profile. The updates of "CV", will be connected to the raise in Personal Coefficient by x points. This means that the prices on already sold items will be updated according to that raised by x% , this change can be viewed in Investors profile in "My portfolio".


In terms of Due Diligence, we are leaving the users freedom to upload the relevant data as self-audit. The real Due Diligence check will only be performed once an artist will upload the Item that will be priced by them over 10'000 EUR as for the latest AML directive.





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