FieldsRank: The Network Value of the Firm

Abstract

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 unworkable for both regulators and investors, since organisations proliferate and reach large scales long before there is any meaningful data available to quantify revenues, or assess the quality of their business model. Moreover, due to the network nature of 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; in essence, traffic makes the map of dependencies emerge. 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 flows, the strength and risk profile of the neighbouring entities, and the intensity of reach and momentum of the business itself ­more fundamentally, value is a function of the information content of the firm. Since accurate estimations of traffic are now widely available, 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 non computational experts (e.g. officials in regulatory bodies), in the other, metric representations that are mathematically robust yet intuitive for communication of positions and relationships (for instance, to managerial audiences) are essential. In this paper we present FieldsRank, a valuation indicator for firms operating in the digital economy. Fields Finance draws upon the powerful flow descriptor capabilities of Vector Fields, which are specially suitable to visualise relationships across the multilayer domains of a digital ecosystem (including the web, social networks, email, advertising networks, apps and the internet-of-things), and fit for applying statistical rigour to behavioural dimensions as revealed by brand signals. The authors present a systematic approach to map, estimate and predict relative value of the digital firm using the FieldsRank model and a workflow based on cognitive computing (i.e. augmented/artificial intelligence); the process is validated using real data. Finally, the implications of the emergence of the “Fields Theory of Finance” are discussed.

Keywords: Corporate Finance, Artificial Intelligence, Complex Networks, Fields Finance, Fintech

JEL Classification: G02