Blockchain Papers

Bonsu Adjei-Arthur, Qi Xia, Jianbin Gao, Goodlet Akwasi Kusi · 6 authors

No abstract is available for this record.

Sachin Yadav, Khushi Johari, Ashish Kumar Jha, Aarzu · 5 authors

LokNirikshan: A Blockchain-Inspired Election Transparency and Management System LokNirikshan is a comprehensive, blockchain-inspired digital platform designed to enhance transparency, integrity, and efficiency in modern election systems. Traditional voting mechanisms—both paper-based and electronic—often suffer from limitations such as lack of transparency, centralized control, slow processing, and susceptibility to data manipulation. These challenges reduce public trust in electoral outcomes and highlight the need for more secure and verifiable solutions. This work proposes a hybrid approach that integrates key blockchain principles—such as cryptographic hashing, Merkle tree-based verification, and audit trails—into a practical, scalable, and user-friendly web-based system. Instead of implementing a fully decentralized blockchain, which introduces complexity and performance constraints, LokNirikshan selectively adopts core concepts to achieve transparency and data integrity without compromising usability. The system supports the complete election lifecycle, including voter registration, constituency and booth assignment, political party onboarding, candidate nomination, election configuration, voting, result computation, and post-election verification. It incorporates role-based access control (RBAC) to manage different stakeholders such as voters, party representatives, party heads, and administrators, ensuring secure and structured interactions across the platform. A key innovation of the system lies in its verification layer, which utilizes Merkle trees to ensure data integrity. Election results are converted into cryptographic hashes and organized into a hierarchical structure, generating a root hash that acts as a tamper-evident reference. This allows independent verification of results without requiring access to the complete dataset, thereby promoting trust through transparency. Additionally, an open public verification portal enables users and observers to validate election outcomes in a decentralized manner. The platform is implemented using modern web technologies, with React and Vite for the frontend, Node.js and Express for backend services, and MongoDB for flexible data storage. Authentication and session management are handled using JSON Web Tokens (JWT), ensuring secure access control. The system also includes anomaly detection mechanisms to identify irregularities such as duplicate entries, missing records, and inconsistent data. Experimental evaluation was conducted using a simulated dataset of 500 voters across multiple constituencies. The system demonstrated high functional reliability, successfully executing all stages of the election process. Verification tests using Merkle proofs achieved 100% accuracy for valid records, while anomaly detection reached approximately 98% effectiveness. Performance analysis indicated efficient response times, with most operations completing within milliseconds. Despite its strengths, the system has certain limitations, including scalability constraints for large-scale elections, partial centralization, and basic identity verification mechanisms. Future enhancements may include full blockchain integration (e.g., Ethereum or Hyperledger), advanced cryptographic techniques such as zero-knowledge proofs, improved voter authentication, machine learning-based anomaly detection, and mobile accessibility. In conclusion, LokNirikshan demonstrates that a balanced integration of blockchain-inspired concepts with conventional web technologies can significantly improve the transparency and reliability of election systems. It provides a practical foundation for developing secure, verifiable, and scalable digital governance platforms, contributing to increased public trust in democratic processes.

Grigoriy Korolev

ABSTRACT We explore the risk‐return tradeoff of Decentralized Finance (DeFi). We construct three novel indices for different asset classes: Lending, Decentralized Exchanges, and Derivatives. Motivated by the cryptocurrency pricing framework of Liu and Tsyvinski, we investigate how DeFi assets comove with financial primitives. We document limited correlation with traditional equities, currencies, interest rates, and commodities. We further examine several DeFi‐specific factors. Bitcoin and Ethereum returns show no significant association with subsequent DeFi returns, highlighting a decoupling between base‐layer assets and application‐layer protocols. Meanwhile, we find some in‐sample associations with DeFi‐specific factors such as momentum, investor attention, and performance of centralized platforms. A novel book‐to‐market ratio constructed using Total Value Locked and market capitalization does not display a systematic relationship with returns. Finally, we find only limited and sector‐specific associations with traditional equity industries.

Yulin Liu

Abstract This study presents a structured dataset of blockchain-registered artificial intelligence agents under the ERC-8004 standard on Ethereum. The dataset integrates on-chain identity records, minting transactions, transfer events, reputation summaries, and individual feedback records, together with resolved off-chain metadata where available. Data were collected from Ethereum mainnet using Web3 RPC queries and processed into tabular form to enable reproducible analysis. The dataset covers 10,000 agents within a defined block range and includes both event-level records and aggregated summaries. It enables empirical research on agent identity formation, reputation systems, service exposure, and early-stage decentralized AI ecosystems. This resource supports studies in blockchain analytics, decentralized trust infrastructure, and the emerging agentic economy.

Shubham Verma

Currently blockchain platforms are not capable of managing sufficient transactions per second. And the gas fees? They make most real world scenarios essentially infeasible. We built Ledgerlink Both these bottlenecks can be linked together, using Ethereum. smart contracts with Arbitrum’s Layer-2 rollup mechanism. Hashing coupled with cryptography and consensus engine (supports both). PoW and POS) eliminate changes in the data. L2 part provides throughput of the order of 10x that of mainnet. you, gas prices are less than 90% lower. Tech stack wise – Solidity. TypeScript, Express, and Next.js TypeScript, optimally backend with express, next as a whole. Frontend tailwind. Simulated load tests were carried out. Enterprise-grade volumes, which promote volumes, are. and can be accomplished without the latency and cost nightmares that you will normally. see on Layer-1. In the present paper we are going to walk through our architecture, the decisions that we made on the way (some good, some we’d) re- consider, and the benchmarking deliverables.

Wei Xiong, Yangcheng Hu, Danping Wan

A novel blockchain-based smart contract framework is proposed and designed to mitigate financial product fraud by enhancing transparency, auditability, and trust in advisory processes. The framework introduces a unique dual-contract architecture that combines product and authorization smart contracts, along with a challenge-response verification protocol that ensures both product authenticity and advisor legitimacy. The system is implemented and tested on the Ethereum blockchain, demonstrating operational feasibility through real-world transaction simulations and a corresponding gas cost analysis. By leveraging immutability, the framework preserves data integrity, while cryptographic signatures enable privacy protection without exposing sensitive data on-chain. It is designed to support multi-institutional environments, allowing various financial institutions to issue products and authorize advisors under a unified, fraud-resistant platform. Experimental results confirm the system’s effectiveness in preventing common fraud scenarios, while maintaining low transaction costs and high security.

K. Vijaya Bhaskar Reddy, Kethavath Vinod, Sheri Shiva Prasad, Machini Manikanta

The rapid growth of cloud computing has significantly transformed the way digital data is stored, managed, and accessed, enabling efficient sharing of information across distributed environments; however, this advancement introduces serious concerns related to data security, privacy, and trust, particularly when sensitive information is involved. Many cloud-based systems rely on centralized architectures, which increase the risk of single points of failure, unauthorized access, data tampering, and limited transparency in tracking data activities. These systems often depend on basic encryption techniques without strong auditing or verification mechanisms, making them vulnerable to insider misuse and external cyber threats, while the absence of immutable records reduces accountability and weakens user confidence. Such limitations highlight the need for a secure and transparent data-sharing framework that ensures confidentiality, integrity, and traceability of data transactions. To address these challenges, the proposed system introduces a secure cloud data sharing model developed using the Django framework, integrating Elliptic Curve Cryptography (ECC), blockchain technology, and the InterPlanetary File System (IPFS) to enhance data protection and decentralization. In this approach, ECC is used to generate cryptographic keys and encrypt user files before storage, ensuring that only authorized users can decrypt the data using the corresponding private key. The encrypted files are stored using IPFS for distributed and content-addressable storage, while file metadata such as username, filename, and timestamp is recorded on the blockchain through smart contracts using Web3, providing a decentralized and tamper-resistant record of all transactions. The system also supports user registration, authentication, secure file upload, and controlled file download functionalities, ensuring secure access and traceability. During file access, encrypted data is retrieved from IPFS and decrypted using ECC to maintain end-to-end security. By combining cryptographic encryption, decentralized storage, and blockchain-based verification, the proposed system enhances data confidentiality, prevents unauthorized modifications, and establishes a reliable and transparent framework for secure cloud data sharing.

Sufyan Ansari

No abstract is available for this record.

E. Sravanthi, Pabbathi Laxmiprasanna, Mulukutla Jahnavi, Kancharla Kritika Reddy

The increasing reliance on digital systems in law enforcement has emphasized the need for secure, transparent, and reliable mechanisms to manage crime evidence. In existing systems, evidence management is typically handled through centralized databases and manual record-keeping, where crime reports, officer details, and evidentiary materials are stored in a single controlled environment. This approach introduces critical challenges such as data tampering, unauthorized access, loss of sensitive information, and lack of transparency, which can weaken trust and complicate legal proceedings. Furthermore, storing evidence in physical formats or unsecured digital systems makes it difficult to ensure authenticity and maintain a proper Chain of Custody (CoC). These limitations highlight the necessity for a system that ensures data integrity, traceability, and secure verification. To overcome these issues, the proposed framework adopts a decentralized architecture using Blockchain technology and Smart Contracts to provide immutability, transparency, and enhanced security of evidence records. The system leverages Ethereum for decentralized data storage, Web3 for enabling interaction between the application and the blockchain network, and Django as the web framework for managing the user interface, file handling, and administrative functionalities. Authorized officers can securely upload, access, and manage evidence, while administrators can monitor and verify transactions in real time. Each evidence record is assigned a unique identifier and permanently stored on the blockchain, preventing unauthorized modification and ensuring a verifiable audit trail. Although the system does not utilize Machine Learning (ML) or Deep Learning (DL), it effectively employs smart contracts-based automation for secure evidence tracking, thereby improving accountability, legal reliability, and operational efficiency.

Collins Izuchukwu Okafor, Love Allen Chijioke Ahakonye, Dong‐Seong Kim, Jae Min Lee

Vehicular-to-Everything (V2X) communications promise unprecedented safety and efficiency gains but remain vulnerable to confidentiality breaches such as eavesdropping, traffic analysis, and man-in-the-middle attacks. We propose ConfidSPEC-V2X, a focused hybrid framework that integrates continuous-variable quantum key distribution (CV-QKD), a multi-agent deep reinforcement learning (DRL), and an Ethereum-based permissioned blockchainPureChainpublic-key infrastructure (PKI) to deliver information-theoretic secrecy, dynamic traffic obfuscation, and tamper-proof key management. In the quantum module, CV-QKD transceivers embedded in On-Board Units (OBUs) and Roadside Units (RSUs) establish symmetric keys resilient to passive interception and capable of immediate eavesdropping detection. The Artificial Intelligence (AI) module employs multi-agent DRL agents at RSUs to learn optimal dummy-traffic injection policies that obfuscate real V2X message patterns against statistical inference. The blockchain module leverages PureChain smart contracts to register, rotate, and timestamp vehicle public keys, ensuring that any man-in-the-middle attempt to forge or replay keys is invalidated. We implement and evaluate ConfidSPEC-V2X within an OMNeT++/Veins simulation under realistic urban mobility scenarios, measuring the quantum bit error rate, key generation throughput, obfuscation entropy, and key management latency. Results demonstrate that our framework achieves robust confidentiality protection with minimal performance overhead.

Dushyant Manghani, Devidas S, Usha Chouhan

Most blockchain anomaly detection research is model-centric, focusing on either proposing new models or comparing benchmarks. Very little work addresses how such methods behave when actually deployed. Data ingestion, feature updates, and inference stability are among the practical concerns that usually get ignored. This work targets this: design, implementation, and evaluation of an AutoML-based platform for anomaly detection targeting Ethereum wallets. The emphasis is on operational behavior, rather than algorithmic novelty. The system is an AutoGluon ensemble that is trained offline from historical, labeled data. Each wallet behavior is represented as a fixed-length feature vector. During inference, live blockchain data is fetched through the blockchain API. This data is transformed into features required by the trained model. The predictor then outputs probabilistic risk scores along with feature-level explanations. Feature computation and model inference are treated as separate processes. This separation allows repeated inference without online learning or continuous retraining. The experimental evaluation discusses several deployment-relevant factors, including class imbalance during training and the contribution of different feature groups. It also examines the stability of the risk score under repeated feature recomputation. Results show that imbalance-aware training improves the reliability of detection. They also indicate that anomaly detection depends on the combined effect of multiple behavioral feature categories.

Rashmi Mandal Vijayvergiya, Aditya Sarkar, Sankhadip Paul, Shuvendu Dhal · 7 authors

No abstract is available for this record.

Chen Ziwen

No abstract is available for this record.

Munaganti Anjali, Arra Nanda Kishore, Sirnapally Mithesh, Mr Lodangi Prabhat

Abstract—The crowdfunding sites that are in use today make use of a centralized system in which the funding and validation of crowdfunding campaigns are done by an authority figure. This has resulted in higher transaction fees and a lack of transparency in the way the funding is being done by donors. This paper aims to propose a Decentralized Crowdfunding Platform using blockchain technology to overcome the current challenges. A new crowdfunding system based on smart contracts using Ethereum is proposed. This new system will ensure a reliable environment for executing smart contracts. A new crowdfunding system based on smart contracts using Ethereum is proposed. This system will have a Startup Verifi-cation process and an Iteration-Based Fund Release process. In the current crowdfunding system, crowdfunding sites release the funds in bulk to the startup. In the new system that is being proposed, the funds will be released in stages. A new system of voting will also be implemented in the new system. This will enable donors to vote based on the cryptographic proofs and reports that are presented by the startup. The software development framework of the platform is based on an effective decentralized technology stack such as Solidity for smart contract development and execution, Hardhat for software development and testing, and React.js combined with Web3.js for front-end interface development. The incorporation of the blockchain technology stack guarantees that all financial transactions are transparent, secure, and tamper-proof. The efficacy of the proposed decentralized approach is measured by how effectively it avoids the costs of intermediaries, is auditable in real-time, and is democratic in nature for all donors. Index Terms—Blockchain Technology, Ethereum, Smart Con-tracts, Solidity, Decentralized Finance (DeFi), Crowdfunding, Decentralized Autonomous Organization (DAO), Iteration-Based Funding, Milestone Verification, Web3.js, Hardhat, Metamask, Trustless Execution, Cryptographic Transparency, Digital Wallet Authentication.

Saran J, S Harish, Mr. K. Arunkumar

This research presents a blockchain-enabled freelancing platform that integrates smart contract-based escrow, decentralized identity, and intelligent freelancer matching to promote trust, transparency, and automation in digital labor markets. The system uses an Ethereum-compatible smart contract called Freelance Escrow, which manages the funding of projects securely, restricts interactions between employers and freelancers to a few specific roles, and automates the release of payments based on the verifiable completion of work. A Python-based blockchain interface developed using Web3.py is used to deploy contracts, sign transactions, and retrieve the current states, while a Streamlit front end provides authentication for user, project, and wallet operations. The platform includes a TF-IDF similarity model that matches freelancers to projects based on relevant skills and semantic similarity, as well as a structured database using SQLite, in which all users, profiles, and project metadata are stored. Comprehensive analysis reveals that the application has strengths in automation, transparency, and enforcement of workflow, while addressing privacy concerns around private key handling, file path inconsistencies, and Web3 library compatibility. The research demonstrates a working end-to-end architecture for decentralized freelance contracting and establishes a foundation for building further secure, scalable, and trust-preserving digital marketplaces.

Shaoyu Li, Chaoyu Zhang, Hexuan Yu, Y. Thomas Hou · 5 authors

Autonomous AI agents live or die by the API tokens they consume: without paid inference capacity they cannot reason, act, or delegate. Compute-token cost has become the binding resource of the emerging agent economy, yet it is non-transferable: it is account-bound, vendor-specific, and absent from on-chain ledgers. Existing payment rails such as x402 move fiat-backed value between agents, but they do not represent the quantity agents actually burn. As a result, agents can transport purchasing power but cannot quote, escrow, or settle workflows in a unit aligned with compute cost. We present ClawCoin, a tokenized, compute-cost-indexed unit of account and settlement asset for decentralized agent economies. ClawCoin combines four layers: a robust basket index over standardized prices; an oracle publishing signed fresh attestations; a NAV-based mint/redeem vault with coverage thresholds and rate limits; and an on-chain settlement layer for multi-hop delegations. We implement a prototype on an Ethereum-compatible L2 and evaluate it using a multi-agent simulator and the OpenClaw testbed. Across single-agent, multi-agent, workflow, and procurement experiments, ClawCoin stabilizes execution capacity under cost shocks, reduces cross-agent quote dispersion, eliminates partial settlements, and sustains cooperative market dynamics that fiat-denominated baselines cannot. These results suggest that compute-indexed units of account can improve decentralized agent coordination.

Ms. Sumangala Pujari

In this paper, they speak of the evidence protection system (EPS) that is a new approach to problem resolution involving contemporary legal and investigative procedures. The EPS uses the blockchain technology called Ethereum to ensure that under all the stages of the evidences life-cycle they are secured, authentic and comprehensive. Using timestamps, smart contracts, and cryptography sequencing, the system creates an evidence management platform, which is easy to read, decentralized, and cannot be hacked. The EPS stores evidence as a record that is not mutable through the use of distributed ledger technologies and digital timestamps. This is what makes it be safer than the centralized systems. smart contracts even the playing field of security and transparency by providing automation of functions such as chain of custody and access control. The integrity of data can be checked in two ways, encryption, and hashing, and keep the actual data safe. overall: the EPS provides the full solution to the issues of processing the evidence in legal environment of the current times, which is why confidence in the efficiency and credibility of evidence that is stored grows.

Wanshui Song, Jingwen Tan, Huanran Wang, Shuai Han · 6 authors

Ethereum’s active financial ecosystem makes itself become a hotbed of phishing scams. Existing studies construct transaction subgraphs and employ GNNs to identify potential phishing accounts. However, existing detection methods rely on complete historical transaction data, making it difficult to detect scams at an early stage. To address this issue, we propose Ethereum Phishing Scams Early Detection (called EPED) method. First, we combine GCN and GRU to capture accounts’ local structural relations and the temporal evolution of their transactions. Second, we introduce deep reinforcement learning for adaptive optimization of the detection time. The two strategies jointly enable early detection with limited data. Experimental results demonstrate that by using only 4.6 days of transaction data, our method achieves a Recall 4.09% higher than existing methods that rely on the full dataset. This result demonstrates the method’s timeliness and effectiveness.

Ning Wang, Fei Ouyang, Haolin Gan, Xiangke Zhang · 5 authors

The rapid growth of decentralized finance on Ethereum has facilitated the rise of fraudulent Ponzi schemes, which exploit blockchain immutability and pseudonymity to deceive investors. Existing detection methods, often fail to generalize to evolving attack strategies, while current multimodal approaches suffer from high computational overhead. To address these challenges, we propose LightPonzi, a lightweight multimodal framework that integrates transaction graphs, abstract syntax trees, and textual semantics of smart contracts. By leveraging GraphSAGE and DistilBERT, LightPonzi efficiently encodes structural, behavioral, and semantic features, which are fused for accurate classification. Extensive experiments on a curated dataset of Ethereum contracts demonstrate that LightPonzi achieves a balanced F1 score of 0.911 while processing each contract in 80.11 ms on average, outperforming state-of-the-art baselines in both effectiveness and efficiency. Our framework provides a practical solution for real-time Ponzi scheme detection.

Irene Aldridge, Gavhar Annaeva, Leyla Beriker, Zhiheng Cai · 24 authors

Ethereum's EIP-1559 fee mechanism was designed under the assumption of homogeneous, myopic agents responding to a single congestion signal. We examine how this assumption interacts with the heterogeneous demand structure of real-world Ethereum users. Analyzing 62,142 confirmed transactions from seven operational firms across seven industries (January--March 2026), we document significant intraday gas-fee variation: fees peak at hour~12 UTC (7\,AM ET, $\hatβ_{12}=\$0.054$ above the U.S.\ evening baseline, $p<0.001$) and are associated with periods of elevated speculative-arbitrage activity. Operational firms exhibit heterogeneous scheduling responses moderated by transaction deferrability and gas intensity. Residual cost floors, i.e. the gap between observed expenditure and the counterfactual under perfect off-peak scheduling, range from 40.7\% to 92.5\% of actual expenditure, and persist even during the lowest-cost hours ($h\in\{20,21,22,23\}$ UTC, 3--6\,PM ET). We introduce an On-Chain Scheduling Matrix that maps firms to four scheduling regimes as a practical framework for managing gas-fee exposure under the current mechanism.

Yulin Liu

This study presents a structured dataset of blockchain-registered artificial intelligence agents under the ERC-8004 standard on Ethereum. The dataset integrates on-chain identity records, minting transactions, transfer events, reputation summaries, and individual feedback records, together with resolved off-chain metadata where available. Data were collected from Ethereum mainnet using Web3 RPC queries and processed into tabular form to enable reproducible analysis. The dataset covers 10,000 agents within a defined block range and includes both event-level records and aggregated summaries. It enables empirical research on agent identity formation, reputation systems, service exposure, and early-stage decentralized AI ecosystems. This resource supports studies in blockchain analytics, decentralized trust infrastructure, and the emerging agentic economy.

Ankit Vishnoi, Varun Sapra, Luxmi Sapra, Preeti Narooka · 5 authors

Introduction The proliferation of dApps is increasing the attack surface for exploitable vulnerabilities in smart contracts, and thus there is a need for verifiable detection methodologies. Methods In this work, we propose a machine learning framework with blockchain integration for explainable and note that “explainable” implies “verifiable” smart contract vulnerability detection. The SmartBugs-curated data was systematically pre-processed with metadata filtering, feature correlation analysis and encoding for model evaluation. Four ensemble learning methods, Random Forest, XGBoost, LightGBM and CatBoost were tested under identical experimental settings for comparison. Results The Random Forest classifier initially achieved the best balance in terms of stability and performance with an accuracy of 87.67%, successfully detecting important vulnerability classes such as re-entrancy, unchecked low-level calls, etc. To enhance the applicability of our blockchain-based machine learning framework for vulnerable smart contract analysis we extend it from the initial 143-contract dataset SmartBugs-Curated to evaluate it on on large-scale set, namely, SmartBugs-Wild which contains 47,398 real-world Ethereum contracts. Based on 29 static contract-level features, unsupervised clustering (k = 4, silhouette score = 0.3735) identifies discrete structural archetypes present in the dataset. Ensemble classifiers (such as XGBoost, CatBoost, Random Forest and LightGBM) can get excellent discriminative performance on these cluster labels: LightGBM achieves 99% accuracy and 0.98918 macro-F1. Discussion The additional results show that the approach scales, is robust and leads to stable models, even if interpretable. After injecting SHAP-based explainability, the interpretability and predictive power of CatBoost became similar to those of Random Forest. In order to guarantee end-to-end trust and traceability of our optimised classifier, this was linked to a blockchain oracle that independently store the outcomes as well as confidence scores for predictions directly onto an Ethereum-compatible ledger through a Vulnerability Registry smart contract. This integration provides the data is immutable, auditable and transparent in reporting.

Kamil Kaczyński

Miksi is a decentralized blockchain protocol that uses zero knowledge proofs (ZKP) and cryptographic commitments to enable anonymous transactions within a secure decentralized network. The system consists of three components: the main application (miksi.ts), the ZKP circuits (deposit and withdraw.circom), and the smart contracts (Miksi.sol, deposit-verifier.sol, and withdraw-verification.sol). Each component plays a pivotal role in the overall security of the system, and we evaluate them for common vulnerabilities such as improper input validation, reentry attacks, and weaknesses in proof verification. Through this analysis, we highlight both the strengths and potential areas for improvement in ensuring secure, anonymous cryptocurrency transactions.Keywords: Ethereum, ZKP, blockchain, DeFi, anonymity, mixer

Wisam Bukaita, Xinrui Li

This study examines dynamic interdependencies and risk transmission among major cryptocurrencies and traditional financial assets, including Bitcoin, Ethereum, U.S. equities, and gold, over the period 2017–2024. Particular attention is given to the structural shift associated with the 2024 U.S. spot Bitcoin exchange-traded fund (ETF) approval, which marked a significant milestone in the institutionalization of cryptocurrency markets. Using daily data, the analysis distinguishes volatility-driven co-movement from structural spillover effects across markets. Dependence structures are modeled using tail-sensitive Student-t copulas applied to GARCH-filtered returns to capture nonlinear and extreme co-movements, while a vector autoregressive framework combined with generalized impulse response functions and Diebold–Yilmaz connectedness measures is employed to evaluate order-invariant shock transmission dynamics across pre- and post-ETF regimes. The results reveal three main findings. First, cryptocurrencies display strong internal dependence and short-horizon contagion, with Bitcoin consistently acting as the dominant transmitter of shocks to Ethereum over an approximately three-day transmission window. Second, linkages between cryptocurrencies and equity markets remain moderate and largely regime-dependent rather than indicative of persistent structural spillovers. Third, gold remains weakly connected throughout the sample, maintaining its role as a diversification asset. Portfolio analysis further indicates that including Bitcoin can reduce portfolio variance by 4–7% and Value-at-Risk by up to 5%, although economic gains are sensitive to transaction costs. Overall, the findings suggest that cryptocurrencies function as a partially segmented asset class, offering conditional diversification benefits despite increasing institutional adoption.