Current value estimation using machine learning

A computer-implemented method for estimating a current value (VCURRENT) using a digital representation of an intangible asset, the method comprising:

• obtaining, by the processor, a source file representing the digital representation of the intangible asset;
• analyzing, by the processor, the source file to identify at least one characteristic of the source file;
• classifying, by the processor, the source file to belong to at least one class (C), from a set (SETC) of predetermined classes, based on the identified at least one characteristic of the source file;
• obtaining, by the processor, one or more evaluation parameters (PEVAL) associated with the creator of the intangible asset from at least one first data source, wherein each of said at least one first data source is a social media source;
• checking, by the processor, if there exists one or more historical value (VHISTORY) associated with the intangible asset in a second data source;
• and, obtaining, by the processor, at least one of said one or more historical values (VHISTORY) associated with the intangible asset from said second data source;
• inputting, to a machine learning model:
• the selected at least one class (C);
• the one or more evaluation parameter (PEVAL) obtained from the first data source;
• and
• the at least one historical value (VHISTORY),
• wherein the machine learning model is configured to predict a future value (VFUTURE) of the intangible asset based on the selected at least one class (C), the one or more evaluation parameters (PEVAL) obtained from the first data source, and the at least one historical value (VHISTORY), wherein the processor or the machine learning model is further configured to estimate the current value (VCURRENT) of the intangible asset based on the predicted future value (VFUTURE) and a pre-set time period (T).

A method for enabling trading of intangible assets on a trading platform (370), the method comprising:

• estimating, by a valuation estimator (600) or a processor (110) executing a machine learning model (115), a current value (VCURRENT) of an intangible asset, wherein inputs to the machine learning model (115) include at least one of a characteristic of the intangible asset, an evaluation parameter, and a historical value (VHISTORY);
• making the intangible asset available to a first-hand market (320) at the estimated current value (VCURRENT), via the trading platform (370);
• determining whether at least a fraction of the intangible asset is sold to one or more investor on the primary market (320) and, thereafter, provide, via the trading platform (370), payout to the intangible asset holder;
• making the intangible asset available to a second hand market (340) at the estimated current value (VCURRENT), via the trading platform (370);
• and
• determining whether at least a fraction of the intangible asset is sold on the second hand market (340), thereafter:
• update the value of the intangible asset to a market value (VMARKET) based on the price at which it was sold;
• update machine learning model (115) based on the market value (VMARKET), and
• provide, via the trading platform (370), payout to the investors.