The accelerating shift toward a digital economy, driven by advancements in technology, has fundamentally transformed the nature of assets, markets, and business models. In this changing environment, Fair Value Accounting (FVA) — which aims to measure assets and liabilities based on current market conditions — faces unprecedented challenges and opportunities. The emergence of digital assets such as cryptocurrencies, non-fungible tokens (NFTs), and decentralized finance (DeFi) products has exposed limitations in traditional valuation frameworks, highlighting issues related to market volatility, illiquidity, and the absence of standardized valuation practices. At the same time, technological innovations like artificial intelligence (AI), big data analytics, and blockchain technology offer new tools that can enhance the accuracy, transparency, and timeliness of fair value measurements. This paper critically examines the future trajectory of fair value accounting in a digitalized world, identifies the major obstacles in valuing digital assets, and explores the role of emerging technologies in addressing these gaps. By analyzing global regulatory responses and proposing strategic reforms, this study provides insights into how accounting standards must evolve to maintain relevance, reliability, and comparability in financial reporting. Ultimately, the paper argues for a proactive transformation of FVA practices, integrating dynamic digital valuation methods while preserving the core principles of accountability, transparency, and investor protection in an increasingly complex economic landscape.
The secondary market for Ethereum non-fungible tokens (NFTs) has resulted in over $1.8bn being paid to creators in the form of a sales tax commonly called creator royalties. This was despite royalty payments being enforced by no more than social contract alone. Predictably, such an incentive structure led to zero-royalty alternatives becoming abundant and payments dwindled. A purely programmatic solution to royalty enforcement is hampered by the prevailing NFT standard, ERC-721, which is ignorant of sale values and royalty enforcement therefore relies on (potentially dishonest) third parties. We thus introduce an incentive-compatible mechanism for which there is a single rationalisable solution, in which royalties are paid in full, while maintaining full ERC-721 compatibility. The mechanism constitutes the core of ERC-7526.
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Cel – Celem artykułu jest prezentacja koncepcji systemu informatycznego umożliwiającego prognozowanie kursu kryptowaluty bitcoin (BTC) w odniesieniu do waluty euro. Metodologia badania – Na potrzeby realizacji tak sformułowanego celu opracowano model sztucznej sieci neuronowej – perceptronu wielowarstwowego. W ramach badań dobrano zmienne wejściowe, od których uzależniono kurs BTC. Pozyskano także odpowiednie dane, pochodzące z dziennych notowań kursów wybranych walut i metali. Dane poddano stosownej obróbce matematycznej w celu ich dostosowania do wykorzystania podczas uczenia, walidacji i testowania sztucznej sieci neuronowej. Oryginalność/wartość – Oryginalny był dobór wektora zmiennych wejściowych, umożliwiających prognozowanie kursu BTC. Wyniki przeprowadzonych eksperymentów potwierdziły wysoką skuteczność prognozowania w perspektywie jedno- i dwudniowej. Wysokie wartości współczynnika regresji (R) i mały błąd średniokwadratowy (MSE) świadczą o tym, że opracowany system predykcyjny prawidłowo przewiduje kursy analizowanej kryptowaluty nie tylko w odniesieniu do danych historycznych, lecz także dla wartości bieżących i przyszłych.
Abstract The accounting fraternity has employed regression analysis rather infrequently. This article presents an application of multiple regression analysis to cost control. The context of the application is the consumer finance industry where extensive decentralization makes effective cost control extremely important. The consumer finance industry is made up of companies whose principal activity is making personal installment cash loans under state small loan laws. The cost behavior model employed in this article is developed from the results of multiple regression analysis of cost and other operating data of branch offices of a major consumer finance chain. While the consumer finance industry is used as the basis, it should be emphasized that the procedure outlined would be applicable to other types of businesses as well. The article shows that an important requirement for the applicability of the procedure is the existence of a relatively large number of homogeneous operating units. Consumer finance companies meet this requirement particularly well. However, other types of business also operate with large numbers of homogeneous units-food including service chains and lodging chains. The procedure outlined would, therefore, be applicable to them as well.