Tokenization promises to convert lumpy, illiquid real-world assets into divisible, transferable claims, yet secondary markets for these instruments remain thin and trading is infrequent. Standard asset pricing models, including the capital asset pricing model and its liquidity-adjusted extensions, were not designed for assets whose holders derive consumption, access, or governance value directly from ownership. This paper develops a conceptual asset pricing framework for utility-backed non-fungible tokens (NFTs) and tokenized real-world assets by augmenting the liquidity-adjusted capital asset pricing model with a utility (convenience) yield. The framework decomposes the required pecuniary return into a risk-free rate, a systematic liquidity-risk premium, an amortized illiquidity level premium that scales with transaction costs and turnover, and a utility-yield offset that lowers the return investors require in cash. Two analytical implications follow. First, utility backing compresses observed pecuniary returns without eliminating the underlying illiquidity premium. Second, where utility flows covary positively with illiquidity, estimates that regress pecuniary returns on liquidity proxies understate the gross illiquidity premium. An illustrative calibration, with parameter ranges drawn from the empirical tokenization literature, quantifies the mechanism rather than estimating it. The framework yields testable predictions and implications for valuation and disclosure.
Francisco Resende, Daniel Costa, Pedro Granate, Armando Teixeira · 8 authors
Non-Fungible Tokens (NFTs) are unique digital assets whose valuation presents a significant challenge due to their non-fungibility, low liquidity, and subjective features. This paper presents a machine learning-based approach to intra-collection NFT valuation using LightGBM, a gradient boosting model. The model was trained on historical sales, metadata, floor prices, and temporal dynamics across six prominent NFT collections. Our approach outperforms traditional valuation baselines, including floor price heuristics, rarity scores, and trait valuation models, achieving significantly lower prediction error (MAPE). The study demonstrates the potential of advanced ML models in enhancing valuation accuracy at a token-level for non-floor assets, with applications in NFT marketplace pricing, portfolio/NAV marking or NFT specialised lending.
The explosive popularity of blockchain-based gaming has brought non-fungible tokens (NFTs) into the fray, as tradable collectibles that connote not only monetary worth but an experience as well. In contrast to traditional in-game items, gaming NFTs exist in two domains of use, on-chain marketplaces and off-chain ecosystems, providing new twists to their valuation. This paper analyzes the approach, issues, and possible developments of the NFT gaming collectibles valuation by consolidating the knowledge in economics, data science, and digital games research. We then present a taxonomy of value drivers, gameplay utility, rarity, provenance, community effects, and tokenomics, to develop a conceptual background. We then compare the relative advantages and (*) disadvantages of popular valuation methods, including hedonic pricing models, repeat-sales indices, machine learning, and liquidity-adjusted approaches. We also identify structural issues of this nascent market, including wash trading, fragmentation of liquidity, evolving product utility, mutability of metadata, and regulatory uncertainty. Critical synthesis helps us see how valuation practices are susceptible to abuse on both sides: how we can abuse the market, but also how we can design the technical aspects of markets to cheat. Lastly, we discuss research directions in the form of standardized benchmarks, dynamic valuation frameworks, interoperability-adjusted models, and integrity checks. The paper will contribute to both the academic and practice worlds as it will provide a reproducible course of action by scholars, game development companies, investors, and marketplaces interested in designing open and sustainable valuation methods for the gaming NFTs.
This study empirically analyzes the determinants of NFT (Non-Fungible Token) value in the collectible NFT market, focusing on investor types. Using structural equation modeling (SEM) and multi-group analysis, we examine the effects of rarity, number of attributes, and trading volume on NFT value, comparing differences between large-scale (whale) and small-scale (ant) investors. Analyzing over 88 thousand transaction data points for 10,000 NFTs from the Bored Ape Yacht Club (BAYC) collection, results show that NFT rarity positively influences value but negatively affects trading volume. Both the number of attributes and trading volume negatively impact on NFT value. Multi-group analysis reveals statistically significant differences in NFT value assessment between whale and ant investors. Whale investors showed a stronger preference for NFTs with higher rarity, particularly valuing the rarity of 'eyes', 'mouth', and 'earring' attributes. Conversely, ant investors showed more interest in NFTs with higher trading frequency and a greater number of attributes. This research contributes to improving the accuracy of NFT value assessment by modeling rarity as a latent variable and clarifying the impact of market dynamics and investor behavior on the structure of the NFT market. These findings provide practical implications for NFT creators, investors, and marketplace operators, and are expected to contribute to strategy formulation for the sustainable development of the NFT market in the future.
Smart contracts upgradeability is a critical requirement for modern decentralized applications based on blockchain technology, but its implementation remains a technical challenge due to the immutable nature of smart contracts. The Proxy Pattern has become the most widely used solution for implementing upgradability into smart contracts, but it comes with some limitations such as implementation complexity and strict memory layout inheritance. This paper introduces an alternative approach based on dynamic routing with Router Contract, which enables modular upgradeability through centralized address management, offering greater flexibility at the cost of requiring external state persistence. Furthermore, a hybrid architecture is proposed, combining both Proxy Pattern and Router Contract approaches to achieve dual-layer upgradeability – supporting both state-preserving updates and full module replacements. The paper offers a comprehensive evaluation of upgradeability strategies and proposes a versatile solution for evolving smart contract systems.
Bitcoin treasury companies have taken stock markets by storm amassing billions of dollars worth of tokens in hundreds of entities. The paper discusses, how leverage - whether created through corporate debt or investors using stock as loan collateral - fuels this trend. The extension of the binary-choice Kelly criterion to incorporate uncertainty in the form of the Kullback-Leibler divergence or more generally Bregman divergence is also briefly discussed.
This paper introduces a novel multi-objective optimization framework for sustainable portfolio rebalancing under uncertainty. The model simultaneously targets return maximization, downside risk control, and liquidity preservation, addressing the complex trade-offs faced by investors in volatile markets. Unlike traditional static approaches, the framework allows for dynamic asset reallocation and explicitly incorporates nonlinear transaction costs, offering a more realistic representation of trading frictions. Key financial parameters—including expected returns, volatility, and liquidity—are modeled using interval arithmetic, enabling a flexible, distribution-free depiction of uncertainty. Risk is measured through semi-absolute deviation, providing a more intuitive and robust assessment of downside exposure compared to classical variance. A core innovation lies in the behavioral modeling of investor preferences, operationalized through three strategic configurations, pessimistic, optimistic, and mixed, implemented via convex combinations of interval bounds. The framework is empirically validated using a diversified cryptocurrency portfolio consisting of Bitcoin, Ethereum, Solana, and Binance Coin, observed over a six-month period. The simulation results confirm the model’s adaptability to shifting market conditions and investor sentiment, consistently generating stable and diversified allocations. Beyond its technical rigor, the proposed framework aligns with sustainability principles by enhancing portfolio resilience, minimizing systemic concentration risks, and supporting long-term decision-making in uncertain financial environments. Its integrated design makes it particularly suitable for modern asset management contexts that require flexibility, robustness, and alignment with responsible investment practices.
Romario Desouza Daniel Mangiwa, Revina Siregar, Sri Anum Sari, Neli Agustina
Ethereum is one of the leading cryptocurrencies utilizing blockchain technology for peer-to-peer financial transactions. This study aims to forecast Ethereum's price using the Autoregressive Integrated Moving Average (ARIMA)model. Historical price data from January 1, 2023, to January 15, 2025, covering 534 periods, was analyzed. The ARIMA (0,1,9) model was selected based on AIC, SC, and Adjusted R-squared criteria, with forecast evaluation showing a Mean Absolute PercentageError (MAPE) of 15.01% and a Root Mean Squared Error (RMSE) of 649.702. Forecast results indicate an upward trend in Ethereum's price over the next 30 periods, with fluctuations being less pronounced compared to historical data. The study concludes that ARIMA provides reasonably accurate short-term predictions, although forecasting errors increase with longer prediction periods. These findings can serve as a reference for investors in developing short-term investment strategies for Ethereum.
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.
Anas Ghazalat, Ala’a Zuhair Mansour, Shadi Maher Al-Khasawneh, Mohammad Abedalrahman Alhmood
Background SMEs failure is common within the first 5 years. For efficient resource management and improve corporate performance, SMEs need management accounting systems. Structure, strategy, and staff qualifications affect MAS adoption in organizations. Objective : This study aims to examine the effect of contingent factors in the adoption of MAS, as well as the effect of MAS on performance, and also the role of MAS as a mediator between performance and contingent factors in SMEs in Jordan. Methods PLS-SEM was used to evaluate a questionnaire of accounting department heads and finance managers from Jordanian SMEs ( N = 415). Results Decentralization, accounting staff qualification, differentiation strategy, and low-cost strategy directly increase MAS adoption. This study shows that MAS improve performance significantly. This study also shows that MAS mediate the relationship between decentralization, accounting staff qualification, differentiation strategy, and low-cost strategy and SME performance in Jordan. Conclusions The study found that contingent factors can help us understand how managers can use MAS information to improve performance. The results only somewhat expand the corpus of research on MAS’s usefulness, but they help us understand the aspects that may affect MAS design and performance in firms.
In the ever evolving landscape of decentralized finance automated market makers (AMMs) play a key role: they provide a market place for trading assets in a decentralized manner. For so-called bluechip pairs, arbitrage activity provides a major part of the revenue generation of AMMs but also a major source of loss due to the so-called 'informed orderflow'. Finding ways to minimize those losses while still keeping uninformed trading activity alive is a major problem in the field. In this paper we will investigate the mechanics of said arbitrage and try to understand how AMMs can maximize the revenue creation or in other words minimize the losses. To that end, we model the dynamics of arbitrage activity for a concrete implementation of a pool and study its sensitivity to the choice of fee aiming to maximize the revenue for the AMM. We identify dynamical fees that mimic the directionality of the price due to asymmetric fee choices as a promising avenue to mitigate losses to toxic flow. This work is based on and extends a recent article by some of the authors.
Decentralized autonomous organizations (DAOs) are emerging innovative organizational structures, enabling collective coordination, and reshaping digital collaboration. Despite the promising and transformative characteristics of DAOs, the potential technological advancements and the understanding of the business value that organizations derive from implementing DAO characteristics are limited. This research applies a systematic review of DAOs' business applicability from an open systems perspective following a best-fit framework methodology. Within our approach, combining both framework and thematic analysis, we discuss how the open business principles apply to DAOs and present a new DAO business framework comprising of four core business elements: i) token, ii) transactions, iii) value system and iv) strategy with their corresponding sub-characteristics. This paper offers a preliminary DAO business framework that enhances the understanding of DAOs' transformative potential and guides organizations in innovating more inclusive business models (BMs), while also providing a theoretical foundation for researchers to build upon.
This article introduces the LP forward contract, a derivative replicating the payoff of a liquidity position (LP) at a future date without intermediate yield. It provides tools for mitigating directional risks and managing impermanent loss. The framework applies the Black-Scholes model to the crypto space, offering closed-form solutions for LP forwards and dynamic hedging strategies. It breaks down HODL portfolios into impermanent loss-hedging portfolios and LP forward contracts, quantifying yield farming costs and enhancing risk management for liquidity providers. This approach aligns the interests of Web3 projects and investors, promoting sustainable growth in DeFi.
C. Vinoth Kumar, Poongundran Selvaprabhu, Nivetha Baska, Vivek Menon U · 7 authors
The Know Your Customer (KYC) process is a fundamental prerequisite for any financial institution’s compliance with the regulatory framework. Blockchain technology has emerged as a revolutionary solution to enhance the effectiveness of the KYC procedure. It ensures that the KYC process is transparent, secure, and immutable, thereby offering a robust solution to combat fraudulent activities. The potential of blockchain technology in revolutionizing the KYC process has been acknowledged globally. Blockchain technology provides a decentralized platform for storing customer data, enabling financial institutions to access the information seamlessly. Using ethereum blockchain technology in KYC procedures can enhance the efficiency of financial institutions, significantly reducing the time and cost associated with the process. This work aims to provide a viable and sustainable solution to the challenges that banks experience in implementing KYC procedures and onboarding new customers. The proposed solution involves the central bank maintaining a comprehensive register of all registered banks while closely monitoring their adherence to the existing regulations governing KYC and customer acquisition.
Non-fungible tokens (NFTs), one of the most talked about technological innovations within Web 3.0 discourse, has stirred awe and confusion in the recent months. While interest in NFTs has grown rapidly in the marketing and the broader business literatures, the question of their value remains a theoretical puzzle. The mystery created by the millions of dollars paid for some NFTs while others are virtually worthless hampers our understanding of what constitutes the value of an NFT. Thus, an appreciation of NFT valuation can advance our theory of value, with implications for businesses. In an attempt to shed light on what drives the value of an NFT we develop a theoretical framework involving a typology of value construction and value perception for NFTs based on two main dimensions––legitimacy (extrinsic vs performative) and groundedness (connection to people, places, or times). We specify four types of NFTs based on subjective and objective dimensions that extend and enrich our theory of the value. We then detail further elements shaping NFT valuation. Finally, we offer advice on how to make sense of value construction and perceptions of NFTs. While our paper is mostly about object valuation, we briefly consider person, culture, and situation factors that also affect valuation. But since the market is agnostic with regard to such factors, we emphasize the NFT object. We end the paper by outlining a research agenda that can push research on NFT forward as crypto and metaverse markets develop.