Modern blockchain, such as Ethereum, supports the deployment and execution of so-called smart contracts, autonomous digital programs with significant value of cryptocurrency. Executing smart contracts requires gas costs paid by users, which define the limits of the contract's execution. Logic vulnerabilities in smart contracts can lead to financial losses, and are often the root cause of high-impact cyberattacks. Our objective is threefold: (i) empirically investigate logic vulnerabilities in real-world smart contracts extracted from code changes on GitHub, (ii) introduce Soley, an automated method for detecting logic vulnerabilities in smart contracts, leveraging Large Language Models (LLMs), and (iii) examine mitigation strategies employed by smart contract developers to address these vulnerabilities in real-world scenarios. We obtained smart contracts and related code changes from GitHub. To address the first and third objectives, we qualitatively investigated available logic vulnerabilities using an open coding method. We identified these vulnerabilities and their mitigation strategies. For the second objective, we extracted various logic vulnerabilities, applied preprocessing techniques, and implemented and trained the proposed Soley model. We evaluated Soley along with the performance of various LLMs and compared the results with the state-of-the-art baseline on the task of logic vulnerability detection. From our analysis, we identified nine novel logic vulnerabilities, extending existing taxonomies with these vulnerabilities. Furthermore, we introduced several mitigation strategies extracted from observed developer modifications in real-world scenarios. Our Soley method outperforms existing methods in automatically identifying logic vulnerabilities. Interestingly, the efficacy of LLMs in this task was evident without requiring extensive feature engineering.
The paper surveys heuristic methods of clusterization in address space of public distributed ledgers. The techniques mentioned rely on collecting behavioral patterns of typical actors and some common sense. Formally heuristics are degenerate clusterization rule-based algorithms, which do not tune their parameters via learning on curated datasets. They can be treated also as persistent motifs in transaction networks. Despite its seeming sim-plicity and inability to assess correctness of the results such approach demonstrates a reasonable effectiveness and often is used as a preliminary step before applying much more sophisticated tools based on machine learning algorithms and AI. Heuristics for Bitcoin, Ethereum, Ripple, Monero and Zcash are discussed. Heuristic clusteri-zation in cross-chain setting is briefly mentioned. Cases when heuristic approach leads to incorrect results are discussed.
Stefanos Chaliasos, Denis Firsov, Benjamin Livshits
Blockchains like Bitcoin and Ethereum have revolutionized digital transactions, yet scalability issues persist. Layer 2 solutions, such as validity proof Rollups (ZK-Rollups), aim to address these challenges by processing transactions off-chain and validating them on the main chain. However, concerns remain about security and censorship resistance, particularly regarding centralized control in Layer 2 and inadequate mechanisms for enforcing these properties through Layer 1 smart contracts. In their current form, L2s are susceptible to multisig attacks that can lead to total user funds loss. This work presents a formal analysis using the Alloy specification language to examine and design key Layer 2 functionalities, including forced transaction queues, safe blacklisting, and upgradeability. Through this analysis, we identify pitfalls in existing designs and introduce an enhanced model that has been model-checked to be correct. Finally, we propose a complete end-to-end methodology to analyze rollups' security and censorship resistance based on manually translating Alloy properties to property-based testing invariants, setting new standards.
With the increasing popularity of blockchain, different blockchain platforms coexist in the ecosystem (e.g., Ethereum, BNB, EOSIO, etc.), which prompts the high demand for cross-chain communication. Cross-chain bridge is a specific type of decentralized application for asset exchange across different blockchain platforms. Securing the smart contracts of cross-chain bridges is in urgent need, as there are a number of recent security incidents with heavy financial losses caused by vulnerabilities in bridge smart contracts, as we call them Cross-Chain Vulnerabilities (CCVs). However, automatically identifying CCVs in smart contracts poses several unique challenges. Particularly, it is non-trivial to (1) identify application-specific access control constraints needed for cross-bridge asset exchange, and (2) identify inconsistent cross-chain semantics between the two sides of the bridge. In this paper, we propose SmartAxe, a new framework to identify vulnerabilities in cross-chain bridge smart contracts. Particularly, to locate vulnerable functions that have access control incompleteness, SmartAxe models the heterogeneous implementations of access control and finds necessary security checks in smart contracts through probabilistic pattern inference. Besides, SmartAxe constructs cross-chain control-flow graph (xCFG) and data-flow graph (xDFG), which help to find semantic inconsistency during cross-chain data communication. To evaluate SmartAxe, we collect and label a dataset of 88 CCVs from real-attacks cross-chain bridge contracts. Evaluation results show that SmartAxe achieves a precision of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mn>84.95</mml:mn> <mml:mo>%</mml:mo> </mml:math> and a recall of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mn>89.77</mml:mn> <mml:mo>%</mml:mo> </mml:math> . In addition, SmartAxe successfully identifies 232 new/unknown CCVs from 129 real-world cross-chain bridge applications (i.e., from 1,703 smart contracts). These identified CCVs affect a total amount of digital assets worth 1,885,250 USD.
R. C. Bose, Jeevan Nagarkar, Sushant Malik, Nisha Bharti
Predictability of the various financial instruments can lead to more trust and investment. The study examines the long-term and causal relationship between various Nifty indices and the Ethereum cryptocurrency. This study considers the data from April 2015 to December 2022 in two phases, pre-covid and post-covid. Johansen’s cointegration test was used to determine if the vectors in the data set are cointegrated, using the Max-Eigen and Trace tests for evaluation. The Granger causality test was also used to explore the short-term causal relationship between Ethereum and the five Nifty indices. The study found that post-pandemic daily returns of stock market indices have developed a significant cointegration with the cryptocurrency over time. The Granger causality test results showed bi-directional relationships of Nifty 50, Nifty 200 and Nifty Next 50 with Ethereum and a unidirectional relationship between Nifty Auto and Ethereum. The non-linear results reveal a one-way relationship pre-covid and a bi-directional relationship post-covid except for Nifty Banks. Johansen’s cointegration test, both in the pre-and post-covid-era, indicated that these indices had a substantial long-term cointegration with cryptocurrencies. This study also offers guidance to investors in making long-term investment decisions and to regulatory authorities. This implied that the investing decisions resulted in developing a causal relationship between the equity market and cryptocurrencies, which seemed very unlikely before 2020. This indicates that a new and young investor also considered cryptocurrencies a viable alternate investment option compared to traditional options such as fixed deposits, gold, and other fixed-income instruments.
This research analyzes the dynamic relationships between the economic and political uncertainty index and the fear index in global markets and cryptocurrencies using the wavelet-based DCC-GARCH method, considering different time scales. Monthly data sets for the periods 2012–April 2024 for GEPU,VIX, and Bitcoin and April 2016–April 2024 for Ethereum are used in the study. Findings are obtained in terms of the volatility interaction between cryptocurrencies (Bitcoin and Ethereum) and GEPU and VIX, as well as four different time scales representing the short, medium, and long term. As a result of the analysis based on raw data, it was found that there is no volatility interaction between cryptocurrencies and GEPU and VIX returns. However, there is a volatility interaction between past volatility shocks and current period volatility shocks in the 4-8 and 16-32 month investment cycle periods of VIX, Bitcoin, GEPU, and Ethereum and time scales. These results, which show that volatility shocks persist in both 4-month and 16-month investment cycles, have significant implications for investors and policymakers. They highlight the need for comprehensive information about changes in the global economy and politics, and they are expected to provide insights for both investors and policymakers.
Jesús Rosa-Bilbao, Juan Boubeta-Puig, Jesús Lagares-Galán, Mark Vella
Blockchain is a relatively recent technology that provides immutability, traceability and transparency of information, thus building trust in the digital society. Blockchain networks generate a large amount of logs which capture and describe data flowing through the network in the form of transactions, blocks and events. Monitoring these blockchain data from the off-chain world is needed to detect anomalies with the aim of mitigating the risks that may arise as a result of using blockchain technology. However, the real-time monitoring of these logs by off-chain systems has become a challenge from the beginning of 2018 when the blockchain networks reached a high number of daily transactions. In this paper, we propose a portable, maintainable and easily configurable architecture integrating blockchain and complex event processing technologies that allows for both the real-time monitoring of logs generated in Ethereum Virtual Machine (EVM)-compatible blockchain networks and the automatic detection of anomalies in these networks by matching event patterns. This architecture was tested by using vast amounts of blockchain data already publicly registered in Ethereum and Polygon networks. The results demonstrate that the proposed architecture is able to automatically detect anomalies which occur in different blockchain networks, making analytics of blockchain data possible by off-chain systems.
Elvira Albert, María García de la Banda, Alejandro Hernández-Cerezo, Alexey Ignatiev · 6 authors
Given a loop-free sequence of instructions, superoptimization techniques use a constraint solver to search for an equivalent sequence that is optimal for a desired objective. The complexity of the search grows exponentially with the length of the solution being constructed and the problem becomes intractable for large sequences of instructions. This paper presents a new approach to superoptimizing stack-bytecode via three novel components: (1) a greedy algorithm to refine the bound on the length of the optimal solution; (2) a new representation of the optimization problem as a set of weighted soft clauses in MaxSAT; (3) a series of domain-specific dominance and redundant constraints to reduce the search space for optimal solutions. We have developed a tool, named S uper S tack , which can be used to find optimal code translations of modern stack-based bytecode, namely WebAssembly or Ethereum bytecode. Experimental evaluation on more than 500,000 sequences shows the proposed greedy, constraint-based and SAT combination is able to greatly increase optimization gains achieved by existing superoptimizers and reduce to at least a fourth the optimization time.
Ethereum is the first and largest blockchain that supports smart contracts. To enhance scalability and security, one major planned change of Ethereum 2.0 (Eth2) is to upgrade the smart contract interpreter from Ethereum Virtual Machine (EVM) to WebAssembly (WASM). In the meanwhile, many other popular blockchains have adopted WASM. Since Ethereum hosts millions of smart contracts, it is highly desirable to automatically migrate EVM smart contracts to WASM code to foster the prosperity of the blockchain ecosystem, while inheriting the historical transactions from Ethereum. Unfortunately, it is non-trivial to achieve this purpose due to the challenges in converting the EVM bytecode of smart contracts to WASM bytecode and adapting the generated WASM bytecode to the underlying blockchain environment. In particular, none of the existing tools are adequate for this task because they fail to achieve accurate translation and compatibility with the blockchain environment. In this paper, we propose a novel solution and use Eth2 as the target blockchain to demonstrate its feasibility and performance because Eth2 is highly attractive to both industry and academia. Specifically, we develop EVMBT, a novel EVM2WASM bytecode translation framework that not only ensures the fidelity of translation but also supports plugins to improve smart contracts. Extensive experiments demonstrate that EVMBT can successfully translate real-world smart contracts with high fidelity and low gas overhead.
Background: Research has been done on the vulnerabilities of Ethereum smart contract detection since the emergence of blockchain technologies. Ethereum is one of the most popular platforms for DApps (decentralized applications) and smart contracts but turns more undoubtedly when their number and popularity grow. Methods: The study evaluates different detection methods including static analysis, dynamic code analysis, symbolic execution, and machine learning. Findings: The performance metrics on key areas, e.g. detection time, true positive rate, false positive rate, and scalability are emphasized in this evaluation analysis. These inferences imply that although Static Analysis can provide fast detection and high accuracy, Machine Learning is better at High scalability. The study also identifies trending flaws often encountered such as re-entrancy attacks and lack of input validation and stresses further the necessity of strong security methods. Besides, you may consider the sensitivity analysis in different network load scenarios as it shows the efficiency of detection technique in changing operational settings. Novelty and applications: Overall, the research brings a reliable development to smart contracts in Ethereum's security industries through analyzing and profiling vulnerability types and performance metrics that inform the development of more stable and efficient security activities for distributed applications.
The rapid advancement of blockchain technology has fueled the prosperity of the cryptocurrency market. Unfortunately, it has also facilitated certain criminal activities, particularly the increasing issue of phishing scams on blockchain platforms such as Ethereum. Consequently, developing an efficient phishing detection system is critical for ensuring the security and reliability of cryptocurrency transactions. However, existing methods have shortcomings in dealing with sample imbalance and effective feature extraction. To address these issues, this study proposes an Ethereum phishing scam detection method based on DA-HGNN (Data Augmentation Method and Hybrid Graph Neural Network Model), validated by real Ethereum datasets to prove its effectiveness. Initially, basic node features consisting of 11 attributes were designed. This study applied a sliding window sampling method based on node transactions for data augmentation. Since phishing nodes often initiate numerous transactions, the augmented samples tended to balance. Subsequently, the Temporal Features Extraction Module employed Conv1D (One-Dimensional Convolutional neural network) and GRU-MHA (GRU-Multi-Head Attention) models to uncover intrinsic relationships between features from the time sequences and to mine adequate local features, culminating in the extraction of temporal features. The GAE (Graph Autoencoder) concept was then leveraged, with SAGEConv (Graph SAGE Convolution) as the encoder. In the SAGEConv reconstruction module, by reconstructing the relationships between transaction graph nodes, the structural features of the nodes were learned, obtaining reconstructed node embedding representations. Ultimately, phishing fraud nodes were further identified by integrating temporal features, basic features, and embedding representations. A real Ethereum dataset was collected for evaluation, and the DA-HGNN model achieved an AUC-ROC (Area Under the Receiver Operating Characteristic Curve) of 0.994, a Recall of 0.995, and an F1-score of 0.994, outperforming existing methods and baseline models.
Solana gained considerable attention as one of the most popular blockchain platforms for deploying decentralized applications. Compared to Ethereum, however, we observe a lack of research on how Solana smart contract developers handle security, what challenges they encounter, and how this affects the overall security of the ecosystem. To address this, we conducted the first comprehensive study on the Solana platform consisting of a 90-minute Solana smart contract code review task with 35 participants followed by interviews with a subset of seven participants. Our study shows, quite alarmingly, that none of the participants could detect all important security vulnerabilities in a code review task and that 83% of the participants are likely to release vulnerable smart contracts. Our study also sheds light on the root causes of developers' challenges with Solana smart contract development, suggesting the need for better security guidance and resources. In spite of these challenges, our automated analysis on currently deployed Solana smart contracts surprisingly suggests that the prevalence of vulnerabilities - especially those pointed out as the most challenging in our developer study - is below 0.3%. We explore the causes of this counter-intuitive resilience and show that frameworks, such as Anchor, are aiding Solana developers in deploying secure contracts.
Chuyi Yan, Xueying Han, Yan Zhu, Dan Du · 6 authors
Abstract Despite the growing attention on blockchain, phishing activities have surged, particularly on newly established chains. Acknowledging the challenge of limited intelligence in the early stages of new chains, we propose ADA-Spear-an automatic phishing detection model utilizing a dversarial d omain a daptive learning which symbolizes the method’s ability to penetrate various heterogeneous blockchains for phishing detection. The model effectively identifies phishing behavior in new chains with limited reliable labels, addressing challenges such as significant distribution drift, low attribute overlap, and limited inter-chain connections. Our approach includes a subgraph construction strategy to align heterogeneous chains, a layered deep learning encoder capturing both temporal and spatial information, and integrated adversarial domain adaptive learning in end-to-end model training. Validation in Ethereum, Bitcoin, and EOSIO environments demonstrates ADA-Spear’s effectiveness, achieving an average F1 score of 77.41 on new chains after knowledge transfer, surpassing existing detection methods.
Smart contracts led to the emergence of the decentralized finance (DeFi) marketplace within blockchain ecosystems, where diverse participants engage in financial activities. In traditional finance, there are possibilities to create values, e.g., arbitrage offers to create value from market inefficiencies or front-running offers to extract value for the participants having privileged roles. Such opportunities are readily available -- searching programmatically in DeFi. It is commonly known as Maximal Extractable Value (MEV) in the literature. In this survey, first, we show how lucrative such opportunities can be. Next, we discuss how protocol-following participants trying to capture such opportunities threaten to sabotage blockchain's performance and the core tenets of decentralization, transparency, and trustlessness that blockchains are based on. Then, we explain different attempts by the community in the past to address these issues and the problems introduced by these solutions. Finally, we review the current state of research trying to restore trustlessness and decentralization to provide all DeFi participants with a fair marketplace.
Rug pulls in Solana have caused significant damage to users interacting with Decentralized Finance (DeFi). A rug pull occurs when developers exploit users' trust and drain liquidity from token pools on Decentralized Exchanges (DEXs), leaving users with worthless tokens. Although rug pulls in Ethereum and Binance Smart Chain (BSC) have gained attention recently, analysis of rug pulls in Solana remains largely under-explored. In this paper, we introduce SolRPDS (Solana Rug Pull Dataset), the first public rug pull dataset derived from Solana's transactions. We examine approximately four years of DeFi data (2021-2024) that covers suspected and confirmed tokens exhibiting rug pull patterns. The dataset, derived from 3.69 billion transactions, consists of 62,895 suspicious liquidity pools. The data is annotated for inactivity states, which is a key indicator, and includes several detailed liquidity activities such as additions, removals, and last interaction as well as other attributes such as inactivity periods and withdrawn token amounts, to help identify suspicious behavior. Our preliminary analysis reveals clear distinctions between legitimate and fraudulent liquidity pools and we found that 22,195 tokens in the dataset exhibit rug pull patterns during the examined period. SolRPDS can support a wide range of future research on rug pulls including the development of data-driven and heuristic-based solutions for real-time rug pull detection and mitigation.
This thesis investigates the impact of design solutions on the execution costs of Ethereum smart contracts, focusing on gas consumption at function level. The work analyzes real-world smart contracts and combines transaction data from Etherscan, function similarity analysis through SmartEmbed and ANTLR, and statistical testing to identify design patterns associated with higher execution costs. Starting from a dataset of Solidity smart contract functions, the study identifies highly similar function pairs with divergent gas costs and manually examines the surrounding contract context to detect recurring design differences. These patterns are then grouped into broader categories and evaluated through statistical analysis to assess their relationship with gas consumption. The results provide practical insights for developers and researchers interested in designing more efficient, sustainable, and cost-aware smart contracts on Ethereum.
Xiaozhi Ma, Wenbo Du, Lingyue Li, Jing Liu · 5 authors
Abstract The integration of Blockchain Technology (BT) with Digital Twins (DTs) is becoming increasingly recognized as an effective strategy to enhance trust, interoperability, and data privacy in virtual spaces such as the metaverse. Although there is a significant body of research at the intersection of BT and DTs, a thorough review of the field has not yet been conducted. This study performs a systematic literature review on BT and DTs, using the CiteSpace analytic tool to evaluate the content and bibliometric information. The review covers 976 publications, identifying the significant effects of BT on DTs and the integration challenges. Key themes emerging from keyword analysis include augmented reality, smart cities, smart manufacturing, cybersecurity, lifecycle management, Ethereum, smart grids, additive manufacturing, blockchain technology, and digitalization. Based on this analysis, the study proposes a development framework for BT-enhanced DTs that includes supporting technologies and applications, main applications, advantages and functionalities, primary contexts of application, and overarching goals and principles. Additionally, an examination of bibliometric data reveals three developmental phases in cross-sectional research on BT and DTs: technology development, technology use, and technology deployment. These phases highlight the research field’s evolution and provide valuable direction for future studies on BT-enhanced DTs.
Majid Mirzaee Ghazani, Ali Akbar Momeni Malekshah, Reza Khosravi
Abstract We used daily return series for three pairs of datasets from the crude oil markets (WTI and Brent), stock indices (the Dow Jones Industrial Average and S&P 500), and benchmark cryptocurrencies (Bitcoin and Ethereum) to examine the connections between various data during the COVID-19 pandemic. We consider two characteristics: time and frequency. Based on Diebold and Yilmaz’s (Int J Forecast 28:57–66, 2012) technique, our findings indicate that comparable data have a substantially stronger correlation (regarding return) than volatility. Per Baruník and Křehlík’ (J Financ Econ 16:271–296, 2018) approach, interconnectedness among returns (volatilities) reduces (increases) as one moves from the short to the long term. A moving window analysis reveals a sudden increase in correlation, both in volatility and return, during the COVID-19 pandemic. In the context of wavelet coherence analysis, we observe a strong interconnection between data corresponding to the COVID-19 outbreak. The only exceptions are the behavior of Bitcoin and Ethereum. Specifically, Bitcoin combinations with other data exhibit a distinct behavior. The period precisely coincides with the COVID-19 pandemic. Evidently, volatility spillover has a long-lasting impact; policymakers should thus employ the appropriate tools to mitigate the severity of the relevant shocks (e.g., the COVID-19 pandemic) and simultaneously reduce its side effects.
The concept of the Internet of Medical Robotics Things (IoMRT) is where intelligent robots assess surrounding events, combine information from their sensors, use both local and dispersed intelligence to determine the best course of action, and move or command objects. Telesurgery is one application of IoMRT (TS). With 5G-enabled Tactile Internet (TI) enabling telesurgery (TS), there is ample opportunity to provide exceptional, accurate, ultra-responsive, and real-time virtual surgical procedures. The potential for accurate surgical diagnosis involving the exchange of patient electronic medical records (EMR) with several doctors using an assistant robot (AR) could be greatly useful in the medical field. As a part of this, permission delegation has emerged as a novel approach for data sharing in TI. Robust control of access guidelines combined with a configurable permission scheme promise secure EMR exchange. The present research proposes a multi-hop permission delegation strategy for EMR exchange based on blockchain technology and with configurable delegation depth. Furthermore, the original EMRs are stored on the interplanetary file system (IPFS). Permission delegation uses smart contracts and proxy re-encryption technology. Attribute-based encryption, which offers fine-grained management of access, is used to guarantee data security. Blockchain is also utilized to accomplish immutability and traceability. Delegators may regulate the depth of delegation by using smart contracts. The suggested approach satisfies the intended aims, according to analysis of the protocol. Lastly, the Ethereum test chain is used to assess and put the suggested method into practice. The outcomes of the conducted experiments demonstrate that the suggested protocol operates better than the competitors.
D Uday Kumar, Gunda Sravya, Akula Leelavathi, K Rama Naga Sai Sri Swathi · 5 authors
The concept of crowdfunding, a method for online fundraising, has evolved to enable public contributions in support of creative projects. Leveraging blockchain technology, cro wdfunding platforms now integrate smart contracts, ensuring secure, transparent, and reliable transactions. This study focuses on the development of interactive interfaces for campaign creation and financial contributions, facilitating engagement for both creators and donors. Campaign creators can propose initiatives and submit them for approval, while donors can browse and support projects through financial contributions. Transparency is ensured through blockchain recording of all transactions, offering immutable and transparent records. The incorporation of smart contracts is pivotal in removing the need for intermediary trust in blockchain-based agreements. This work emphasizes the importance of developing executable code for blockchain execution, ensuring transaction integrity and security. Initially associated with cryptocurrencies, blockchain technology has expanded its applications across industries, offering a sustainable solution for internet transactions. Crowdfunding platforms stand to benefit significantly from blockchain integration. Challenges in the current crowdfunding landscape include inadequate oversight and fraudulent investment schemes. By leveraging Ethereum smart contracts, this study seeks to address these challenges, enforcing time limits and automating contract execution to enhance trust and transparency in the crowdfunding process.
Penelitian ini menyoroti tantangan yang dihadapi oleh penulis ilmiah, terutama terkait kurangnya penghargaan, dukungan finansial, dan apresiasi. Solusi inovatif diajukan melalui penerapan teknologi blockchain, khususnya Ethereum, untuk meningkatkan transparansi dan keamanan dalam alur donasi. Dengan menggunakan pendekatan tersebut, penelitian ini bertujuan untuk memberikan solusi atas masalah tersebut serta membuka peluang kolaborasi yang lebih luas dalam komunitas penelitian. Penelitian ini melibatkan langkah-langkah seperti studi literatur, analisis sistem, pengembangan sistem, dan pembuatan laporan. Dalam pengembangan sistem, digunakan metodologi SDLC (Software Development Life Cycle) dengan model waterfall yang terstruktur dan sesuai untuk merancang sistem donasi dengan teknologi blockchain. Penelitian ini berhasil mengembangkan sebuah sistem donasi yang terintegrasi dengan blockchain ethereum berbasis ekstensi browser. Hasil pengujian unit testing menunjukkan bahwa fungsi-fungsi yang digunakan berjalan dengan baik. Diharapkan penelitian ini dapat memberikan kontribusi signifikan dalam mendukung penulis ilmiah, memperkuat transparansi dalam alur donasi, serta menjadi landasan bagi penelitian-penelitian selanjutnya di bidang ini.
This study aims to investigate the information spillover among four traditional financial assets (i.e., crude oil, gold, stock, and U.S. dollar) and nine main cryptocurrencies (i.e., Bitcoin, Cardano, Dai, Ripple, Dogecoin, Ethereum, Ethereum Classic, Monero, and Tether), by constructing entropy-based information spillover network and information integration network from both static and dynamic perspectives. The empirical results show that the information spillover among these assets is time-varying, experiencing an obvious increase trend after the COVID-19. As a whole, traditional financial assets mainly play the role of net information transmitter while cryptocurrencies mainly play the role of net information recipient. Tether and Dai are the two main visual coins that can transmit net information flow to traditional assets, while gold and stock are the two main traditional assets that transmit net information flow to cryptocurrencies. Tether and U.S. dollar are the central nodes that link traditional financial assets and cryptocurrencies together.
This article presents a novel approach to cryptocurrency price forecasting, leveraging advanced machine-learning techniques.By comparing traditional autoregressive models with recurrent neural network approaches, the study aims to evaluate the forecasting accuracy of Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models across various cryptocurrencies, including Bitcoin, Ethereum, Dogecoin, Polygon, and Toncoin.The data for this empirical study was sourced from historical prices of these specific cryptocurrencies, as recorded on the CoinMarketCap platform, covering January 2022 to April 2024.The methodology employed involves rigorous statistical and neural network modelling where each model's parameters were meticulously optimized for the specific characteristics of each cryptocurrency's price data.Performance metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) were used to assess the precision of each model.The main results indicate that LSTM and GRU models, leveraging deep learning techniques, generally outperformed the traditional ARIMA and SARIMA models regarding error metrics.This demonstrates a higher efficacy of neural networks in handling the non-linear complexities and volatile nature of cryptocurrency price movements.This study contributes to the ongoing discourse in financial technology by elucidating the practical implications of using advanced machine-learning techniques for economic forecasting.Importantly, it provides valuable insights that can directly inform and enhance the decision-making processes of investors and traders in digital assets.