Blockchain Papers

Linlin Zhang, Nianlu Liu, Ke Li, Wenbo Fang · 5 authors

No abstract is available for this record.

Marwan Mohamed Abu Orabi, Abdulrahman Hashem

No abstract is available for this record.

Dongshu Cheng, Chang Wang, Qi Chang

No abstract is available for this record.

MichaelaUrbanová

This work deals with the possibility of using recurrent neural networks of the LSTM and GRU type for predicting daily logarithmic returns of selected cryptocurrencies (BTC, ETH, LTC, BNB). Based on daily OHLCV data from the period 2017–2024, three sets of input variables are constructed: a basic set (transformed price and volume variables and logarithmic returns), a set of technical indicators, and a set of technical indicators together with macroeconomic data. The LSTM and GRU prediction models are calibrated for different memory lengths (10, 20, 50 days), numbers of neurons, and all three sets, and their performance is evaluated using RMSE, MAE, and directional accuracy. The results show that daily returns of cryptocurrencies are difficult to predict from the point of view of a one-time prediction: random walk remains a very strong benchmark in all cases, and the best neural networks only come close to it. Subsequently, simple Long/Short strategies are constructed based on the predictions and compared with the passive Buy&hold strategy. For all four cryptocurrencies, configurations are found whose strategies achieve higher annualized returns and Sharpe ratios than Buy&hold in the test period, especially for more volatile altcoins. However, this outperformance is conditioned by ex post selection of the “best” models and neglect of transaction costs, and therefore it must be interpreted with caution as an illustration of the potential and limits of deep neural networks in short-term prediction of cryptocurrencies.

Santiago Ventura, Steven Murphy

Many poor long-term financial decisions are not “choices” but symptoms of a psychological state called Learned Helplessness. This is the belief, often learned from past setbacks, that one has no control over outcomes, leading to passivity and avoidance. In finance, this manifests as a belief that “it doesn’t matter what I do, I’ll never get ahead.” This is academically defined as an External Locus of Control, the belief that one’s financial future is in the hands of luck or external forces, not personal effort. An External Locus of Control can be directly linked to saving significantly less for retirement and avoiding proactive financial planning. In this research, we attempt to predict cryptocurrency engagement, given it's attractiveness for people who feel that traditional, effort-based financial structures are futile, as a function of beliefs about people's locus of control of their finances, planning horizon, and self-efficacy.

Ylva Baeckström, Akanksha Jalan, Miriam Marra, Roman Matkovskyy · 5 authors

No abstract is available for this record.

Rajendar Dommeti

d IoT security perspective. It makes use of three essential Blockchain features— transparency, immutability, and decentralization— to build environment that are reliable and impenetrable. This application is realized through the utilization of features such as AI-driven fraud detection, Blockchain security, data privacy, the reliability of Smart Contracts, transaction speed, and system scalability. The result is, Blockchain-IoT Security Perspective, the first rank is System Scalability, the lowest rank is AI-based Fraud Detection, Blockchain Security is the fourth rank, Data Privacy is the fifth rank, Smart Contract Reliability is the third rank, and Transaction Speed is the first rank.

Park Ji Hyun

No abstract is available for this record.

K. Meenakshi, Kirti Singh, Deepshikha Bhargava

No abstract is available for this record.

Ines Ghazouani, Zaineb Hlioui, Marwa Zouawi

ABSTRACT This study investigates the impact of environmental attention on cryptocurrency market volatility by introducing the Crypto Environmental Attention Index (CEAI), a new metric inspired by Wang et al. (2022) and constructed using daily web search data. Environmental concerns can significantly impact the popularity and volatility of cryptocurrencies, influencing risk perceptions, and shaping market dynamics. Using vector autoregression (VAR), vector error correction models (VECM), and Granger causality tests on data from 2014 to 2022, the study finds that Ethereum's volatility is strongly influenced by the CEAI in both the short and long‐term, whereas Bitcoin volatility has a short‐term unidirectional effect on environmental attention and a bidirectional relationship in the long term. This study is situated within a broader economic framework of sustainable finance, the transition to greener blockchain technologies, and regulatory responses to environmental issues. It offers actionable insights for risk management, policy formulation, and cryptocurrency valuation using environmental, social, and governance (ESG) criteria.

Akomolehin FO, Famoroti JO

The fast pace of development of cryptocurrency markets challenges classical financial theories, highlighting the importance of investor psychology and sentiment in shaping the dynamics of prices and volatility. In sharp contrast to traditional assets, the cryptoverse is also far more driven by behavioral factors with market action frequently a result of sentiment, cognitive bias and social media than fundamentals. This study examines the intersection of behavioral finance and cryptocurrency investments, and specifically how investor sentiment affects police uncertainty phenomenon, is examined on already established and emerging markets. Using a literature-based integrative review approach, we integrate empirical and theoretical research between 2017 and 2025 from peer-reviewed sources in Scopus, ScienceDirect, JSTOR, SSRN, and Google Scholar. The review also identifies behavioural patterns that are applied again and again, such as overconfidence, herding, anchoring, and loss aversion, and looks at how they manifest in the world of crypto. It is also assessing more sentiment proxies—such as Google Trends, Twitter activity, and Reddit threads—portraying their predictive link to price volatility and trading volume. The results confirm the inefficient property of the Cryptocurrency market and also justify the relevance of behavioral finance in decentralized sentiment-sensitive markets. The paper makes both theoretical contributions by enabling the application of sentiment analysis to blockchain based assets, and practical proposals to investors, regulators, and fintech developers. Highlighting the importance of hybrids, the study argues that behaviorally driven sentiment analysis, as well as artificial intelligence (AI) driven sentiment models should be integrated into market governance frameworks. The results confirm the inefficient property of the Cryptocurrency market and also justify the relevance of behavioral finance in decentralized sentiment-sensitive markets. The paper makes both theoretical contributions by enabling the application of sentiment analysis to blockchain based assets, and practical proposals to investors, regulators, and fintech developers. Highlighting the importance of hybrids, the study argues that behaviorally driven sentiment analysis, as well as artificial intelligence (AI) driven sentiment models should be integrated into market governance frameworks.

Septiana Sihombing, Rindi Ardika Melsalasa Sahputri, Hendrik Ali, Muhamad Galy Njoman · 6 authors

The bitcoin market has exhibited highly volatile return movements, experiencing a sharp surge starting from in November 2022 to 2024. This significant fluctuation underscores the importance of analyzing the factors influencing bitcoin’s return dynamics. This study utilizes daily data with a final sample of 590 observations. All time-series variables must be stationary before being processed in the statistical model. The analysis was conducted using Stata 16 software. To ensure the absence of unit roots, the stationarity of the research variables was tested using the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests. The findings indicate that market capitalization, gold, and litecoin have no significant impact on bitcoin returns. In contrast, miners’ revenue has a significant negative effect, while hashrate, mining difficulty, and the S&P 500 exhibit a significant positive influence on bitcoin returns. This study highlights bitcoin’s role as a store of value and investment asset, emphasizing the impact of hashrate and mining difficulty on its returns and integration into financial markets, particularly the S&P 500. The findings provide insights for investors on portfolio diversification and assets like a gold and equities. Additionally, the study underscores the importance of sustainable mining practices and regulatory policies to balance cryptocurrency’s economic potential with environmental sustainability. Keywords: Market capitalization; Mines’s Revenue; Hashrate; Mining difficulty; Commodity Asset, Cryptocurrency

Qiang Yin

In traditional financial processes, repetitive operations rely on manual intervention, which leads to efficiency bottlenecks and data tampering risks. This study generates standard operation sequences through RPA process mining and builds atomic operation units based on smart contracts. This paper transforms the distributed RPA controller to implement parallel contract calls and combines zero-knowledge proof to ensure cross-organizational data security. The blockchain status channel can be used to monitor anomalies in real time and trigger on-chain evidence storage, and the “execution-evidence-audit” closed loop can be formed through oracle docking supervision. The experiment shows that the processing cycle is shortened from an average of 20.76 hours for manual work to 6.23 hours, and the audit trail completeness rate is improved. Research has confirmed that the deep integration of RPA and blockchain can build an efficient, secure and reliable financial automation system, providing key technical support for digital transformation.

Damla Nurcan Özkılınç

The digital transformation of finance and accounting is accelerating with AI, blockchain, and automation, reshaping financial operations, auditing, and compliance. This study conducts a thematic analysis of academic literature (2018–2025) and industry reports from PwC, Deloitte, EY, HSBC, and central banks to examine key trends. Six themes emerged: automation and efficiency, security and fraud prevention, decentralization, financial inclusion, regulatory challenges, and adoption barriers. Findings show that AI and RPA enhance financial reporting and fraud detection, while blockchain improves transparency and security but poses scalability and regulatory challenges. Decentralized finance (DeFi) and digital currencies like JPM Coin and the Digital Yuan are transforming transactions but raise concerns over compliance and illicit activity risks. Mobile banking and blockchain-based solutions improve financial inclusion, yet digital literacy and security risks remain barriers. Using NVivo-based thematic analysis, the study identifies key trends shaping the future of financial digitalization. While AI and blockchain drive efficiency, regulatory complexities and adoption barriers must be addressed for sustainable transformation. Future research should explore scalability, AI-enhanced compliance, and blockchain’s role in financial security.

Bhimanagouda L. Rayanagoudra

The digital economy is rapidly transforming the global landscape by integrating technology, entrepreneurship, and innovation across every sector. Startups have become the key drivers of this transformation, enabling new models of production, finance, and governance. By 2047, the digital economy is expected to evolve into a deeply interconnected system powered by artificial intelligence, blockchain, decentralized finance, and sustainable technologies. These advancements will reshape industries, empower small enterprises, and foster inclusive growth. This paper explores how startups will act as engines of innovation, leveraging digital tools to solve complex social and economic challenges. It highlights emerging trends such as AI-driven decision-making, edge computing, green technologies, and decentralized governance models that will redefine the global business environment. At the same time, the paper acknowledges the challenges of data privacy, cybersecurity, skill development, and environmental sustainability. Through policy analysis and strategic recommendations, the study emphasizes the importance of strong digital infrastructure, ethical data practices, and inclusive innovation ecosystems to ensure balanced growth. By 2047, success in the digital economy will depend not only on technological advancement but also on human creativity, collaboration, and sustainable practices.

Ruchi Priya Khilar, Shikta Singh

Purpose : Investors sometimes have difficulties in making appropriate decisions during unpredictable times such as the COVID-19 pandemic. Based on the extended theory of planned behavior (TPB), the objective of this study was to validate the psychological antecedents of willingness to invest in cryptocurrency during the outbreak of the COVID - 19 pandemic. Design/Methodology/Approach : This study was conducted by collecting primary data from 204 respondents using a structured online questionnaire, which was further analyzed using SPSS Amos 23.0. The current research explored the association between variables named “subjective norms,” “perceived self-efficacy,” and “attitude to invest.” Findings : The findings suggested that “subjective norms” and “perceived self-efficacy” are the major factors influencing investors’ attitudes toward cryptocurrency investment, directly impacting their intentions for the same. The pandemic underscored the dual nature of cryptocurrencies, demonstrating advantages such as enabling remote transactions and providing a hedge against economic instability, while simultaneously displaying difficulties, including environmental implications and market volatility. Practical Implications : The results have important reference significance for future investors who intend to invest in the crypto-assets market. The findings of the study provided important insights to investors and financial planners on which psychological factors affect whether to make a cryptocurrency investment during volatile periods, such as the COVID-19 era. Learning these key aspects will provide scientists with tools to navigate choppy waters, which will ultimately make them better decision-makers. Originality : This research enhanced the existing body of knowledge by uniquely incorporating the extended theory of planned behavior into the context of cryptocurrency investments during a global crisis. It offered an extensive comprehension of investor psychology during periods of uncertainty, essential for both scholarly study and pragmatic investment strategies.

Andi Prayitno, Miftahul Jannah, Darmawati Darmawati, Syarifuddin Rasyid · 5 authors

This study examines the relationship between market efficiency and digital financial innovation in the context of global financial transformation over the past decade, when fintech, cryptocurrency, and Decentralized Finance (DeFi) have significantly altered price formation and information dissemination mechanisms. The main issue raised is whether the Efficient Market Hypothesis (EMH) theory remains relevant in the face of digital market dynamics characterized by high volatility, speculative behavior, and regulatory uncertainty. The objective of this study is to assess the impact of digital innovation on information efficiency, price transparency, and the stability of modern financial markets. The study used the Systematic Literature Review (SLR) method, examining 15 scientific articles published between 2015 and 2025 from various academic databases. The findings indicate that digital technology increases access and speed of information distribution, but does not always result in consistently efficient markets. Crypto and DeFi markets have been shown to exhibit fluctuating efficiency due to price anomalies, information asymmetry, and weak regulation. Overall, the literature synthesis confirms that market efficiency in the digital era is dynamic and influenced by the interaction between technology, investor behavior, and governance quality. This study concludes that the EMH remains relevant as a basic framework, but needs reinterpretation to suit the complex and rapidly changing characteristics of digital markets.

Ruidi Liu

This paper reviews the innovative applications of AI and Web3 in metaverse social platforms. It first analyzes the foundational roles of AI (e.g., virtual avatar generation, intelligent interaction, personalized recommendation) and Web3 (e.g., blockchain, NFTs, decentralized identity) in enabling immersive, secure, and user-centric social interactions. It then examines their synergies, with case studies of Decentraland and The Sandbox illustrating practical integrations. The research identifies key challenges, including technical bottlenecks (e.g., AI realism, blockchain scalability), user-related issues (e.g., awareness, privacy concerns), and industry-level hurdles (e.g., regulatory ambiguities, homogenization). Finally, it proposes future directions: advancing AI/Web3 technologies, expanding application scenarios across education and entertainment, and implementing strategic recommendations to foster inclusive and sustainable metaverse social ecosystems.

George Rooney, Cäzilia Loibl

No abstract is available for this record.

Jinghao Yang

Behavioral finance explores the psychological influences and cognitive biases that affect investor behavior and financial decision-making, including herding, the disposition effect, overconfidence, and others. Algorithmic trading is a method that uses computer programs to automatically execute buy and sell orders based on predefined mathematical models and trading strategies. With the continuous development of modern technology, the advent of the Web3 era, and the gradual evolution of artificial intelligence, algorithmic trading is becoming increasingly prevalent and garnering significant attention. While algorithmic trading is automated and may seem immune to human cognitive biases, the opposite is often true. This study aims to review the main findings of existing research from the perspective of the stock market, exploring the interactive relationship between behavioral finance and algorithmic trading and how cognitive biases such as herding and the disposition effect can influence algorithm performance. The results emphasize the importance of behavioral finance in both the research and practice of algorithmic trading, while also proposing the potential for using machine learning techniques to advance the field of behavioral finance. By integrating existing theories, this study contributes to a deeper understanding of the relationship between behavioral finance and algorithmic trading and offers new perspectives for its future development.

Parisa Bouzari, Maria Fekete-Farkas, Zsigmond Gábor Szalay

This research investigates the efficacy of transformer architectures in classifying sustainability claims made by cryptocurrency projects, addressing a critical gap in automated environmental impact assessment of digital assets. Employing design science research (DSR) methodology, we develop and empirically evaluate a novel framework comparing five state-of-the-art transformer models across multiple performance dimensions. Through rigorous analysis of 300 synthetic cryptocurrency sustainability news articles, we demonstrate that RoBERTa-large-MNLI achieves optimal performance (F1: 1.00) with exceptional prediction stability (0.98)—meaning highly consistent predictions across varied inputs—and minimal entropy (0.05)—indicating strong confidence in classification decisions—albeit at higher computational costs. Our findings challenge conventional assumptions about the inverse relationship between model complexity and prediction reliability in specialized financial domains. The results advance theoretical understanding of transfer learning in sustainable finance while establishing quantitative benchmarks for automated environmental claim verification. This research contributes to both academic literature and regulatory frameworks by providing empirically validated methodologies for distinguishing between substantive and symbolic environmental initiatives in cryptocurrency markets. The findings provide valuable guidelines for cryptocurrency projects, financial institutions, and regulatory bodies seeking to implement automated sustainability assessment systems, while establishing a foundation for future research in the intersection of artificial intelligence and sustainable finance.

Daniela Popescu, Cătălin Mihail Barbu, Sorina Gîrboveanu, Mariana Jugănaru · 5 authors

In an era of fast-pace technological change, the internet is evolving from Web 1.0 (static, one-way communication) and Web 2.0 (interactive, collaborative platforms) to Web 3.0, characterized by decentralization, artificial intelligence, blockchain, and a focus on authentic values and meaningful connections. Web 3.0 empowers consumers and transforms the internet into a decentralized platform where users control their personal data, intermediaries are replaced by smart contracts and blockchain, but it also introduces challenges such as technological complexity, security risks, regulatory difficulties, and interoperability with Web 2.0. Web 3.0 marketing emphasizes an approach that includes emotional, cultural, and spiritual dimensions, enabling brands to gain a profound and lasting relevance. In this paper we analyse the multifacets of Web 3.0 marketing in the fashion industry, a sector intensely transformed by social, cultural, and technological dynamics. We investigate how marketing principles and Web 3.0 technologies, such as non-fungible tokens (NFTs), the metaverse, and digital identity, are being incorporated into fashion brand strategies, highlighting the benefits and challenges of building authentic relationships with consumers. Fashion brands are embracing emerging technologies to create immersive experiences and loyalty through NFTs, augmented reality, and virtual spaces in the metaverse.

Oksana Nakonechna, Stanislav Gorodnichenko

Distributed ledger technology (DLT) provides a fundamental methodological basis for strengthening control and enhancing the reliability of accounting. It shifts trust to the cryptographic level and enables triple-entry accounting and continuous auditing, ensuring the immutability and integrity of accounting information. This article offers a comprehensive examination of the potential of Distributed Ledger Technology (DLT) as a foundation for the methodological transformation of accounting systems and enterprise financial control – specifically, the transition toward the paradigms of triple-entry accounting and continuous auditing – and develops recommendations for mitigating systemic challenges essential for ensuring the reliability of accounting information in the digital economy. The study employs conceptual, synthetic, and classification-systematizing analytical methods to assess the architecture of DLT systems and their suitability for accounting tasks. To provide a multifaceted evaluation of integration potential and related challenges, comparative, synergetic, SWOT, and risk analyses are applied. The research substantiates that adopting DLT technologies enables a fundamental methodological shift in accounting, facilitating the transition from traditional double-entry bookkeeping to Triple-Entry Accounting. It is demonstrated that the immutability, transparency, and cryptographic security of distributed ledgers enable the integration of accounting and analytical functions with emerging digital technologies (AI, IoT, and Big Data). This synergy forms the basis for Continuous Auditing, in which the control function is performed automatically and in real time. The study identifies the main systemic challenges of DLT implementation, including the acute shortage of specialists with dual expertise (accounting, auditing, and DLT), insufficient digital competencies of existing staff, and significant integration complications with legacy enterprise IT infrastructures (ERP systems). Additional risks arise from regulatory uncertainty regarding the legal status of crypto-assets and smart contracts, which hinders the standardization of accounting practices. The article proposes practical recommendations emphasizing that the successful implementation of DLT projects requires a comprehensive approach. This includes not only technological transformation (gradual integration and transition toward consortium-based DLT models) but also active development of human capital. Specifically, investment is required in retraining accounting and analytical personnel, creating new interdisciplinary educational programs focused on smart-contract deployment and DLT analytics, and adapting national accounting standards. Keywords: distributed ledger technology, accounting and analytical support, triple-entry accounting, continuous auditing, digital economy, smart contracts, professional competencies.

Manabhanjan Sahu, Furquan Uddin, Md Billal Hossain

Cryptocurrency investment in India has quickly become a mainstream financial activity, but it is still highly prone to psychological factors that impact the decision-making of retail investors. This study examines the effect of personality traits on cryptocurrency investment behavior using the mediating variable of behavioral biases. Based on the Big Five Personality Model and the theory of Behavioral Finance, data were gathered from 716 Indian retail investors using a structured questionnaire. Partial Least Squares Structural Equation Modeling (PLS-SEM) was conducted to analyze the relationships among the variables. Results show that Openness to experience and Agreeableness significantly predict Availability Bias, whereas Extraversion and Agreeableness affect the Disposition Effect. The theoretical framework shows how bias-driven investment behavior in volatile markets such as cryptocurrency is triggered by personality-based predispositions. The study adds to the behavioral finance literature by taking psychological profiling outside the realms of traditional investment contexts into digital asset investing and provides practical insights for regulators, fintech platforms, and investment advisors to design interventions to mitigate bias and enhance investor education.