Integrating digital assets into financial valuation theory and practice

Abstract

The article deals with the theoretical basics of digital asset valuation and substantiates the need for their integration into modern financial analysis and corporate finance. It concludes that traditional methods—discounted cash flow (DCF), the capital asset pricing model (CAPM), and comparative multiple analysis—have proven effective in valuing stocks, bonds, and other traditional instruments, but are limited in the digital economy. Cryptocurrencies, utility tokens, digital rights, and non-fungible tokens (NFTs) possess unique features: intangible nature, lack of guaranteed cash flows, high price volatility, dependence on network effects, and decentralization. The article looks into the latest adaptation of valuation methods, including network metrics (market capitalization to transaction volume ratio (NVT), Metcalfe's law), modified fee discounting models, and scenario-based venture approaches. It also explores the using the MV=PQ equation for tokenomics analysis and the determining of a "price floor" by means of mining or staking cost. Particular attention is paid to the role of Big Data and on-chain analytics, which enable applying open blockchain data on transactions and users' activity, as well as using artificial intelligence and machine learning algorithms for price forecasting, identifying fundamental value, and separating it from speculative factors. It emphasizes the need to expand the conceptual framework, to consider legal definitions, and develop specialized models for various token types (utility, security, stablecoins, NFTs) that take into account the technological characteristics of networks, incentive economics, and behavioral factors. It is concluded that integrating digital assets into financial valuation theory requires an interdisciplinary approach that compraises finance, network economics, legal regulation, data-driven analytics, and tokenomics engineering design.