Context. Machine learning approaches for smart contract vulnerability detection are typically evaluated on synthetic benchmarks of programmatically generated code snippets. Practitioner reports and recent independent evaluations indicate that automated tools continue to miss critical vulnerabilities in production audits, yet the contribution of benchmark selection to this gap remains under-examined.Objectives. This study investigates whether surface-level Solidity features that correlate with vulnerability severity in synthetic benchmarks retain their predictive validity on professionally audited contracts, and proposes a quantitative metric for assessing benchmark suitability for severity prediction research.Methods. Fifteen features were extracted identically from a 10,448-sample synthetic Solidity benchmark and DAppSCAN, a corpus of 1,646 findings from 1,199 audit reports authored by 29 firms. Feature-severity correlations were compared using Fisher r-to-z, Kolmogorov-Smirnov, and Levene tests. Logistic Regression, Random Forest, and Gradient Boosted classifiers were trained in three conditions: in-distribution synthetic, in-distribution real, and cross-distribution.Results. Mean absolute correlation was 0.228 on synthetic data versus 0.057 on real data, a fourfold gap (all p
In early November 2025 the yield-bearing stablecoin sector experienced its first systemic run: over roughly seventy-two hours, three synthetic dollar tokens lost between 94 and 99 percent of their value, set off by the disclosure of an external-manager loss at Stream Finance. Using hourly on-chain data we reconstruct the cascade and show that survival was not determined by on-chain exposure or scale-the largest instrument, sUSDe, held its peg while absorbing several hundred million dollars of redemptions-but by the quality of the backing and whether redemptions were honored under stress. We further show that the contagion did not travel through observable decentralized-finance composability: public lending exposure to the failed collateral was negligible. Transmission ran instead through off-chain reserve relationships and, in the single material public exposure, through a price oracle that remained frozen at the pre-crash value, implying a roughly 119-fold overvaluation weeks into the collapse, so that no liquidation fired and approximately $7.5 million of bad debt accrued without a single onchain bad-debt event. We read the episode as evidence that opacity in valuation, rather than composability, was the systemic channel, and draw implications for the disclosure, redemption, and oracle requirements that govern tokenized dollars. The paper is a descriptive and structural anatomy of one systemic episode; we make no causal-identification claim.
<b><i>Traditional financial audits</i></b> have long served as the primary instruments for oversight, disclosure assurance, and risk assessment in regulated financial systems. These mechanisms, however, were designed for centralized institutions, periodic reporting cycles, and human-paced transaction environments. In blockchain-based systemsâparticularly those supporting stablecoins, tokenized real-world assets (RWAs), and decentralized finance (DeFi)ârisk materializes continuously and often irreversibly. This paper presents a structural comparison between traditional audit models and the Crystal Validatorâą (CV), a pre-execution enforcement architecture designed for real-time regulatory compliance. We demonstrate that post-fact auditing is structurally incapable of preventing modern on-chain failures, regulatory breaches, and systemic collapses. We argue that effective blockchain regulation requires a shift from retrospective verification to deterministic, pre-transaction authorization enforced at the protocol level.
Double-entry bookkeeping ensures internal balance but offers limited independent evidence that reported state follows from complete, unaltered records under a stated accounting policy. Ian Grigg's operational triple-entry modelâcryptographically linked inter-firm receiptsâand subsequent advances in hash commitments, zero-knowledge proofs, and payment-layer compliance attestations motivate ledger-native assurance: verification artifacts produced during accounting close, not assembled from exports afterward. This working paper presents a design-science framework with four assurance layers (source, posting, record, disclosure); a taxonomy of source-anchoring paths including on-chain settlement, TLS-attested ingestion, and bilateral finalization; an analysis of payment-layer versus ledger-layer zero-knowledge statements; and a close-cadence model distinguishing continuous, partial, and batch close under different capture postures. We state explicit scope limits aligned with audit evidence theory and outline an empirical and regulatory research agenda.
We construct a protocol-native valuation signal for Ethereum based on demand-side fees expressed as a share of token supply. The signal measures the log deviation of current fee intensity from its trailing median, a dimensionless ratio denominated entirely in ETH. It predicts subsequent token returns at 10 to 60 day horizons with in-sample R-squared up to 22.8% and expanding-window out-of-sample R-squared of 14.4% at 45 days. The signal retains predictive power after macroeconomic controls, standard crypto risk factors, and momentum controls, and predicts ETH-specific relative returns. Predictability emerges only after the Dencun hard fork (March 2024), which separated execution fees from data availability fees, making the demand signal empirically detectable. Our findings demonstrate that demand-side economic flows are capitalized into token prices in the absence of firms, contracts, or residual cash flow rights, extending valuation logic to rule-based economic systems.
The rapid growth of entities that hold, trade and earn revenue from crypto-assets has outpaced the development of auditing guidance tailored to this environment. Digital assets have moved decisively from the margins of finance into the balance sheets of regulated funds, market makers, fintechs and conventional corporates, with the global crypto-asset market now measured in the trillions of dollars. Auditors are now routinely asked to express opinions on financial statements that include digital assets, yet the established toolkit of external confirmations, period-end cutoff procedures and observable market prices maps poorly onto blockchain-based holdings and continuous, automated trading. This paper sets out, from a practitioner's standpoint, the principal challenges encountered when auditing crypto-holding and trading entities: establishing the existence and ownership of on-chain assets; obtaining assurance over the completeness of an entity's wallet population; valuing illiquid tokens, liquidity-pool positions, non-fungible tokens and stablecoins; auditing emerging instruments such as token loan agreements, warrants and forfeitures; addressing cutoff in markets that never close; and testing revenue arising from market-making, arbitrage and staking. For each area, the paper describes how the profession currently adapts existing standards-notably ISA 500, ISA 501, ISA 540 (Revised), ISA 240, IFRS 13 and the recently effective FASB ASC 350-60-and where meaningful gaps remain. The paper argues that strengthening audit practice in this domain is not a narrow technical concern but a matter of investor protection and financial-system integrity, given the scale of value now held in digital form and the heightened fraud and money-laundering risks that accompany it. It concludes with practical considerations for auditors and a call for more specific standard-setting and practitioner guidance.
We document a sizeable disposition effect in the market for non-fungible tokens (NFTs). Using a comprehensive transaction dataset from OpenSea, we show that NFT holders systematically realize gains prematurely while holding onto losses, mirroring behavior documented in traditional equity markets. Consistent with a high participation rate of retail investors and the lack of clear fundamental values, the effect is significantly more severe than in equity markets. We further find that the magnitude of the disposition effect attenuates in December, consistent with end-of-year tax-loss harvesting incentives, suggesting that on-chain transactions can be monitored by tax authorities. Finally, to address the NFT market's episodic illiquidity, we introduce a novel measure of the disposition effect based on the time-to-sale of listed assets. Our findings extend behavioral finance theory to digital-asset markets and provide new tools for studying the disposition effect in illiquid trading environments.
The accounting for cryptoassets under current IFRS remains fragmented. Following the IFRS Interpretations Committee's 2019 agenda decision on holdings of cryptocurrencies, most holders default to IAS 38 Intangible Assets unless IAS 2 Inventories applies. That outcome produces incomplete comparability, weak performance reporting, and a recurring tension between the economic liquidity of many cryptoassets and the accounting model applied to them. This discussion paper proposes a narrow holder-side framework for fungible cryptoassets that do not provide the holder with an enforceable claim on an issuer, measured subsequently at fair value through profit or loss, with business model affecting presentation and disclosures rather than measurement. The analysis also addresses matters commonly omitted in early crypto accounting proposals: counterpart entries for acquisition, use, rewards and disposal; liability-side consequences arising from taxes, slashing, safeguarding, financing and legal obligations; interaction with IFRS 13, IAS 12, IAS 37 and IFRS 7; and practical illustrations across treasury entities, funds, exchanges, validators, Web3 operators and payment platforms. Public-company reporting examples are incorporated as illustrative evidence of how existing accounting frameworks affect balance-sheet presentation, profit or loss, deferred taxes and scope boundaries in practice.
The accounting for cryptoassets under current IFRS remains fragmented. Following the IFRS Interpretations Committee's 2019 agenda decision on holdings of cryptocurrencies, most holders default to IAS 38 Intangible Assets unless IAS 2 Inventories applies. That outcome produces incomplete comparability, weak performance reporting, and a recurring tension between the economic liquidity of many cryptoassets and the accounting model applied to them. This discussion paper proposes a narrow holder-side framework for fungible cryptoassets that do not provide the holder with an enforceable claim on an issuer, measured subsequently at fair value through profit or loss, with business model affecting presentation and disclosures rather than measurement. The analysis also addresses matters commonly omitted in early crypto accounting proposals: counterpart entries for acquisition, use, rewards and disposal; liability-side consequences arising from taxes, slashing, safeguarding, financing and legal obligations; interaction with IFRS 13, IAS 12, IAS 37 and IFRS 7; and practical illustrations across treasury entities, funds, exchanges, validators, Web3 operators and payment platforms. Public-company reporting examples are incorporated as illustrative evidence of how existing accounting frameworks affect balance-sheet presentation, profit or loss, deferred taxes and scope boundaries in practice.
A recurring narrative in digital-asset markets holds that tokens of protocols with "real revenue" are fundamentally cheaper and should outperform. I test this directly using the full cross-section of fee- and revenue-reporting protocols tracked by DefiLlama (2,259 protocols; 345 with a traded market capitalization) and one year of daily price and market-capitalization data. Three findings emerge. First, valuation is economically disconnected from revenue at the level of the market: a single asset (Bitcoin) accounts for 90.5% of sample market capitalization, tokens with essentially no measurable protocol revenue represent roughly 92% of market capitalization, and even among application protocols revenue multiples are extraordinarily dispersed (median price-to-revenue of 9.0Ă spanning well below 1Ă to effectively unbounded). Second, in the cross-section of forward returns, the formation-date revenue multiple has no power to discriminate winners from losers: over a window in which the median token fell 78.7% and only 5.3% of tokens posted a positive return, the rank correlation between price-to-revenue and the subsequent twelve-month return is statistically zero (Spearman Ï = 0.04), and is unchanged after controlling for size and asset class (slope on log price-to-revenue = -0.0004, p = 0.98). Third, in a monthly Fama-MacBeth panel the relationship is, if anything, weakly anti-value (mean Ï = +0.063, t = 2.18): cheaper-on-revenue tokens did marginally worse, not better. The evidence does not support a revenue-based value premium in this market and period; the dominant force in returns was a near-uniform sector-wide repricing. Results are specific to a single, predominantly bearish regime and to a universe conditioned on revenue generation, limitations I discuss in detail. AI-use disclosure: The author used a large language model (Anthropic's Claude) to assist with data-collection scripting, routine statistical computation, and manuscript drafting and editing; all research-design choices, the analysis, and the conclusions are the author's own.
Smart contracts implement financial logic on public blockchains, and external code audits are a primary means of providing assurance about their security. This paper discusses Landsman, Lyandres, Maydew, Rabetti, and Zhang (Journal of Accounting and Economics, forthcoming), who describe and analyze the market for smart contract audits. I provide a brief primer on blockchains and smart contracts, explain what distinguishes blockchainbased finance from traditional finance, outline why no audit can guarantee security in an open, permissionless environment, and discuss how Landsman et al.s findings relate to financial auditing. A central result in their paper is that pre-launch audits do not predict fewer breaches, which is noteworthy and consistent with the view that a smart contract audit, like a financial audit, is a snapshot, not a shield. Beyond the conceptual and methodological parallels between smart contract and financial audits, two distinctive features merit the attention of accounting scholars: open code forking, which creates networks of common-code exposure and correlated systemic risk, and bug bounty programs, which provide a crowdsourced and potentially continuous form of assurance.
The accounting architecture for digital assets has developed unevenly. Under both IFRS and U.S. GAAP, recent technical activity has concentrated mainly on the holder-side of crypto-assets, while issuer-side token transactions remain fragmented across analogies to financial instruments, revenue contracts, loyalty programmes, provisions, and, in practice, non-recognition. That fragmentation is no longer tenable. Web3 issuers, centralised platforms, fintechs and traditional enterprises are now using tokens not only as fundraising devices, but also as access rights, governance mechanisms, customer-retention instruments, and promotional distribution tools. This paper develops a principles-based issuer-side model that separates token arrangements according to their economic substance rather than their technological form. The proposed framework proceeds through five decision gates: âą (i) whether the token creates a contractual claim or residual interest within IAS 32 / IFRS 9; âą (ii) whether it embodies an enforceable promise to transfer goods, services or network access within IFRS 15; âą (iii) whether it grants a material right in a loyalty or rewards structure; âą (iv) whether a promotional airdrop creates a substantive stand-ready obligation; and âą (v) whether the token is, in substance, a governance-only digital right with no continuing issuer obligation. The paper argues that minting alone is ordinarily not a recognition event, that internally generated treasury tokens are not issuer assets, that governance tokens are not equity absent the IAS 32 residual-interest test, and that utility and loyalty tokens ordinarily create contract liabilities rather than immediate revenue. It also proposes a more disciplined treatment for promotional airdrops, together with journal-entry mechanics, disclosure requirements, market illustrations and a bridge to CPC and U.S. GAAP practice. The objective is to provide an auditable, globally usable foundation for the accounting of token issuance by issuers rather than holders.
This paper develops a principles-based issuer-side accounting framework for token issuance, promotional airdrops, governance tokens, utility tokens and Web3 loyalty programmes. It argues that tokens should be classified according to the economic substance of the rights and obligations created by the issuer, rather than by their technological form. The framework is anchored in IAS 32, IFRS 9, IFRS 15, IAS 37, the IFRS Conceptual Framework, U.S. GAAP analogues and Brazilian CPC literature. It is intended to support technical analysis by preparers, auditors, academics, regulators and accounting standard-setters.
This study asks whether Ethereumâs proof-of-stake (PoS) incentives not only make economic sense on paper but also feel attractive to real validators who may be loss-averse and sensitive to risk. We take a canonical Eth2 slot-level model of rewards, penalties, costs, and proposer-conditional maximal extractable value (MEV) and overlay a prospect-theoretic valuation that captures reference dependence, loss aversion, diminishing sensitivity, and probability weighting. This Prospect-Theoretic Incentive Mechanism (PT-IM) separates the âmoney edgeâ (expected accounting return) from the âfelt edgeâ (behavioral value) by mapping monetary outcomes through a prospect value function and comparing the two across parameter ranges. The mechanism is parametric and modular, allowing different MEV, cost, and penalty profiles to plug in without altering the base PoS model. Using stylized numerical examples, we identify regions where cooperation that pays in expectation can remain unattractive under plausible loss-averse preferences, especially when penalties are salient or MEV is volatile. We discuss how these distortions may affect validator participation, economic security, and the tuning of rewards and penalties in Ethereumâs PoS. Integrating behavioral valuation into crypto-economic design thus provides a practical diagnostic for adjusting protocol parameters when economics and perception diverge.
This comprehensive review examines the evolutionary trajectory of financial information systems from the 1670s to the present day, analyzing how technological innovations have fundamentally transformed financial reporting, auditing practices, and information accessibility. Through a bibliometric and conceptual analysis of seminal literature, this study identifies key technological inflection points including the emergence of structured bookkeeping systems, the institutionalization of financial publicity through the 1867 law, the development of sophisticated financial communication tools, and the recent integration of blockchain technology and data analysis capabilities. The review demonstrates that each technological wave has progressively enhanced data accuracy, real-time reporting capabilities, and audit efficiency while simultaneously introducing new challenges related to data security, regulatory compliance, and technological adoption barriers. Contemporary developments in distributed ledger technology and advanced analytics represent a paradigm shift toward autonomous financial reporting systems with unprecedented transparency and verification capabilities. The findings suggest that future financial information systems will be characterized by increased automation, enhanced predictive analytics, and seamless integration of blockchain-based audit trails. This evolution has profound implications for accounting professionals, regulatory frameworks, and corporate governance structures, necessitating adaptive strategies for stakeholder education and regulatory modernization.
Xiang, Yuexin, Yang Lei, Yuanzhe Zhang, Qin Wang · 7 authors
Stablecoins such as USDT and USDC aspire to peg stability by coupling issuance controls with reserve attestations. In practice, however, transparency remains fragmented across heterogeneous data sources, with key evidence about circulation, reserves, and disclosure dispersed across records that are difficult to connect and interpret jointly. We introduce a large language model (LLM)-based automated framework for bridging cross-domain transparency in stablecoins by aligning issuer disclosures with observable circulation evidence. First, we propose an integrative framework using LLMs to parse documents, extract salient financial indicators, and semantically align reported statements with corresponding market and issuance metrics. Second, we integrate multi-chain issuance records and disclosure documents within a model context protocol (MCP) framework that standardizes LLM access to both quantitative market data and qualitative disclosure narratives. This framework enables unified retrieval and contextual alignment across heterogeneous stablecoin information sources and facilitates consistent analysis. Third, we demonstrate the capability of LLMs to operate across heterogeneous data domains in blockchain analytics, quantifying discrepancies between reported and observed circulation and examining their implications for transparency and price dynamics. Our findings reveal systematic gaps between disclosed and verifiable data, showing that LLM-assisted analysis enhances cross-domain transparency and supports automated, data-driven auditing in decentralized finance (DeFi).
This paper examines the Financial Accounting Standards Boardâs (FASB) recent changes to define crypto assets, focusing on why non-fungible tokens (NFTs), utility tokens and asset-backed tokens (ABTs) were not included. By examining the core features of these excluded assets, the research unpacks the reasoning behind their omission. The absence of these assets from the standard definition creates challenges for financial institutions. Without clear accounting guidance, companies face uncertainty in valuation, liquidity risk and difficulty meeting compliance requirements. Risk managers are left guessing how to assess and report these holdings. This has an impact on everything from disclosures to capital planning. The findings highlight a critical need for accounting standards that keep pace with the complexity and growth of digital assets. Institutions may misprice assets, misjudge exposure and fall short of regulatory expectations without up-to-date guidance. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
This study examines the emerging convergence of triple-entry accounting, blockchain technology, and machine learning as a transformative framework for enhancing financial transparency. Using a bibliometric analysis of Scopus-indexed publications from 2000 to 2025, the research identifies key intellectual structures, thematic clusters, and temporal trends that shape this field. The results show that blockchain serves as the foundational infrastructure enabling immutable, verifiable accounting records, while machine learning functions as an analytical layer that strengthens anomaly detection, continuous auditing, and fraud prevention. Triple-entry accounting is found to be evolving from a conceptual innovation into a practical accounting architecture supported by cryptographic verification and distributed ledger systems. The study highlights significant implications for auditors, regulators, and organizations seeking to modernize financial reporting through automation and secure digital ecosystems. Although promising, the research also notes limitations related to data scope, conceptual depth, and the need for empirical validation. Overall, the findings underscore the potential of technologically integrated accounting systems to redefine trust, accountability, and transparency in modern financial environments.
ABSTRACT This study examines how higher employer social security costs affect the allocate decisionâmaking authority using data from Chinese listed companies from 2007 to 2022, employing both fixedâeffects and differenceâinâdifferences (DID) models. Based on an extensive sample of firms, we find that higher social security costs are associated with a major delegation of authority from parent companies to their affiliates. Evidence suggests this adjustment occurs because the extra cost changes each firms' competitive environment and operating results. The impact is most pronounced in nonâstateâowned firms and in firms that face tight financing constraints, indicating that ownership and financing flexibility shape the response. Our findings contribute to the organizational design literature by demonstrating that social security costs can act as an external driver of firm decentralization.
This study examines the adoption of Digital Ledger Technology (DLT) and its impact on the âaccuracy of financial reporting and the efficiency of auditing processes within Jordanian âorganizations. Using a quantitative research design, the study assesses how DLT enhances âfinancial data integrity and supports real-time auditing capabilities. Data were collected from ââ210 accounting and auditing professionals representing five major Jordanian institutions: the âCentral Bank of Jordan, Jordan Customs Department, Arab Bank, Deloitte Jordan, and Ernst â& Young Jordan. A structured questionnaire served as the primary data collection instrument, âemploying a five-point Likert scale to measure perceptions across key constructs related to âDLT adoption. To improve response rates, the questionnaire was distributed through both âphysical and digital channels. Descriptive statistics were used to analyze demographic data, âwhile multiple regression and independent samples t-tests were applied to test the studyâs âhypotheses. The results revealed a statistically significant and positive relationship between âDLT adoption and both the accuracy and reliability of financial reporting, as well as between âDLT utilization and enhanced auditing speed and efficiency. Regression analysis indicated that âDLT adoption accounted for 52% of the variance in financial reporting accuracy, while t-test âresults confirmed significant differences between DLT-based and traditional auditing methods. âThe study complied with ethical standards, ensuring confidentiality and voluntary âparticipation. Overall, the findings demonstrate that DLT plays a transformative role in âimproving accounting and auditing practices within a developing economy contextâ.
Nara Raquel D. Andrade, Oscar William N. de Carvalho, Carlos H. G. Ferreira, Glauber Dias Gonçalves
The market for Non-Fungible Tokens (NFTs) continues to evolve, yet it still lacks robust methodologies to estimate the future value of its assets. Unlike traditional financial markets, NFT pricing is challenged by intangible factors such as the artistic nature of the items and the influence of social and transactional networks among buyers and sellers. This study investigates whether the structural position of participants in the transaction network can serve as a relevant predictor of the future value of NFTs. To this end, we reconstructed the NFT trading network for the period 2020â2021, extracted both structural and transactional metrics of the participants, and applied supervised machine learning models, including deep neural networks. The results demonstrate the feasibility of the proposed approach, achieving 74% accuracy and a global F1-Score of 72%. Interpretability analysis using SHAP values revealed that, in addition to historical price averages, network metrics such as degree and neighborhood significantly contribute to prediction. These findings highlight the role of network dynamics in NFT valuation and point toward promising directions for more transparent and evidence-based pricing methodologies.
Purpose âthis paper aims to examine the impact of the integration between Extensible Business Reporting Language (XBRL) and Smart Contracts, which represent the second generation of the decentralized ledger Blockchain, on the transition from continuous auditing to Real-time Auditing. Design/methodology/approach â Using Exploratory Study , this study examines the impact of the integration between XBRLand Smart Contracts on the transformation from continuous Auditing to Real-time Auditing. Findings â This paper finds that the XBRL- Smart Contracts is a good way to activate real-time Auditing because of the characteristics of XBRL and Smart contracts based on blockchain that can support real-time Auditing, including transparency, privacy, decentralization, and pre-validation of operations at the same time as they occur with no possibility of modification or fraud. Therefore, Smart Contracts is not a substitute for XBRL, as the Blockchain is a ledger through which transactions can be conducted, and XBRL is the standard that can standardize the terms and standards for the items that are exchanged in accounting in those transactions, which means that XBRL supports Smart contracts based on blockchain in transparency and trust in transactions. There for, this paper finds that the XBRL- Smart contracts based on blockchain integration affects significatively to transfer from continuous auditing to real time auditing. Originality/value â This paper contributes to the literature on Provide a proposed A Prospective framework for the integration between XBRL and Smart Contract to transfer from continuous Auditing to Real time Auditing.
Purpose This study aims to investigate the potential benefits of blockchain technology in enhancing corporate accountability and reporting transparency to foster a more optimistic view toward its implementation in business practices. Design/methodology/approach Data were gathered from 304 managers, accountants, auditors and board members of small and medium-sized enterprises in Razavi Khorasan Province 2024. A combination of standardized instruments and a custom-developed questionnaire validated by experts was used to measure the variables. The data were analyzed using SmartPLS. In the proposed model, blockchain adoption and reporting transparency are independent variables, while corporate accountability and reporting quality are dependent variables. Findings The analysis reveals that implementing blockchain technology has a strong positive impact on corporate accountability and financial reporting quality. In addition, higher levels of transparency in reporting are associated with improved organizational responsiveness, suggesting that greater openness in information disclosure strengthens companiesâ ability to respond to stakeholder demands and regulatory expectations. The results indicate that adopting blockchain can significantly contribute to more reliable, timely and transparent corporate reporting practices. Originality/value This research offers a novel empirical perspective on how blockchain can transform corporate accountability frameworks. It emphasizes the importance of technological trust, transparency and continuous blockchain-based auditing in advancing corporate governance. The study provides practical recommendations for managers, regulators and policymakers to support blockchain implementation through regulatory measures, security protocols and inter-organizational collaboration. Ultimately, these findings reinforce stakeholder trust, regulatory compliance and long-term organizational credibility.