To this day, the value of the firm as an economic measure has been assessed mainly in function of direct revenues, or as a sum of claims from creditors and equity holders. However, in the context of the digital economy this approach is becoming unworkable for both regulators and investors, since rapid growth businesses reach large scales long before there is any information available to quantify revenues or assess the quality of its business model. Moreover, due to the network nature of such digitally native companies (or the digital branches of brick and mortar firms) it is clear that they do not exist in isolation, but are actually influenced and exercising influence in other firms and networks. The connecting stream across entities is traffic: traffic inflows encode the value they receive from the network, and traffic outflows the value they contribute to the network. The own intrinsic value of the business can still be assessed using more traditional financial measures, but now it also depends on the level of visibility and liquidity associated with those inflows and outflows. Surprisingly, accurate estimations of traffic are now widely available, so the main limitation to develop a new valuation framework is not data related, but methodological: in one side, cognitive computation processes are needed to make discovery accessible to noncomputational experts (e.g. officials in regulatory bodies), in the other, metric representations that are mathematically robust yet intuitive for communication of positions and relationships are essential. In this paper we present FieldsRank, a valuation metric for firms operating in digital economies.
FieldsRank draws upon the powerful flow descripting capabilities of Vector Fields, which are specially suitable to map relationships across the multilayer domains of a digital ecosystem (including the web, social networks, email communication, advertising networks, apps and the internet-of-things), and also to apply statistical rigor to behavioral dimensions as revealed by brand signals. The authors present a systematic approach to map, estimate and predict value of the digital firm using the FieldsRank model and a workflow based on cognitive computing (i.e. augmented/artificial intelligence); the model and process are validated using real data. Finally, the implications of the emergence of a “Fields Theory of Finance” are discussed.