Alim Al Ayub Ahmed, Harish Paruchuri, Siddhartha Vadlamudi, Apoorva Ganapathy
Digital finance is assuming a significant part in the arrangement of financial services all over the world. Fast growth with digitalization, data analysis, and computing capacities allows for a whole new scope of financial services and transactions. This financial development empowered by digital financial technology (Fintech) has pulled in a ton of attention, as it could offer some potential for economic growth and development. As a part of the Fintech environment, cryptography has started to grow quickly and digital assets are acquiring in favorability among financial bankers and investors. Human behavior as they engage with financial activities is personally associated with the noticed market elements. However, with many existing theories and studies on the fundamental motivations of the conduct of people in financial frameworks, there is still restricted experimental derivation of the behavioral conduct of the financial agents from a definite market analysis. Cryptocurrency technology has given a map to this analysis with its voluminous data and its transparency of financial transactions. It has empowered us to perform inference on the personal conduct standards of users in the market, which we analyze in the bitcoin and ethereum cryptocurrency markets. In our study, we initially decide different properties of the cryptography users by complex network analysis. Financial cryptography is a difficult subject that necessitates abilities from a variety of seemingly unrelated fields. There is a serious risk that attempts to establish Financial Cryptography frameworks would simplify or omit key disciplines because they are caught between central banking and cryptography. This paper discusses research that attempts to limit the scope of Financial Cryptography. This model should assist the project, administrative, and requirements personnel by classifying each discipline into a seven-layer model of basic nature, where the link between each adjoining layer is evident. While this model is shown as effective, all models have cutoff points. This one does not present a design system or a protocol agenda. Furthermore, given the model's initial adaptation and the field, it should be viewed as a suggestion of complexity rather than a definitive approach.
Chao Liu, Xiaoshuai Zhang, Kok Koeng Chai, Jonathan Loo · 5 authors
Abstract Electric power grid infrastructure has revolutionized our world and changed the way of living. So has blockchain technology. The hierarchical electric power grid has been shifting from a centralized structure to a decentralized structure to achieve higher flexibility and stability, and blockchain technology has been widely adopted in the energy sector to deal with grid management, billing, metering, and so on, because of its nature of decentralization. Here, the aim is to provide a multi‐dimensional review on the technological advances of the blockchain in smart grids. Its corresponding applications based on these advances, including company projects and use cases, are summarized. Furthermore, the security threat issues in smart grids, Ethereum Virtual Machine (i.e. the operating environment of consensus mechanisms), and smart contracts are analysed, with a brief conclusion to manifest the prior tasks in building secure blockchain‐based infrastructures in smart grids. As such, the challenges and features of different protocols and their applicability in each use case are identified to provide an insightful guide for future research studies.
The work presented in this thesis describes the design and development of smart contract system for blockchain based applications. The main objective is to investigate the current state of blockchain technology and its implementations and to reveal how main principles of this disruptive technology can reshape "business as normal" activities. This work examines the Blockchain technology as a whole and addresses its potential for the development of distributed applications. Blockchain based smart contract system for different applications that demonstrate a streamlined access management system using Ethereum blockchain has been designed and implemented. Ethereum reinforces the second gen of blockchain technology by providing an open and global computing/environment platform allowing for the exchange of cryptocurrency (Ether) and the creation of self-verified smart contract applications. Smart contracts provide a framework for the ownership of digital assets and a range of distributed applications in the blockchain region. Ethereum and smart contracts are open, decentralized and unalterable, as such, they are susceptible to vulnerabilities that arise from developers ' simple coding errors. We have designed and implemented smart contract system for blockchain based applications across healthcare management to Facilitate Medical Ecosystems and Energy systems to increase energy efficiency by designing smart contract system for energy-saving certificates. We also identified key themes, developments and emerging areas of healthcare and energy research. In this thesis, blockchain technology has been applied in areas such as healthcare and Energy sector. Healthcare applications are the main contribution of this thesis and the work on the Energy systems reflects as an additional contribution of this thesis. Hopefully the work presented in this thesis will give enough motivation towards developing distributed applications (ĐApps) using blockchain based smart contract system.
Vehicular crowd sensing is a promising approach to address the problem of traffic data collection by leveraging the power of vehicles. In various applications of vehicular crowd sensing, there exist two burning issues. First, privacy can be easily compromised when a vehicle is performing a crowd sensing task. Second, vehicles have no incentive to submit high-quality data due to the lack of fairness, which means that everyone gets the same paid, regardless of the quality of the submitted data. To address these issues, we propose a smart privacy-preserving incentive mechanism (SPPIM) for vehicular crowd sensing. Specifically, we first propose a new SPPIM model for the scenario of vehicular crowd sensing via smart contract on the blockchain. Then, we design a privacy-preserving incentive mechanism based on budget-limited reverse auction. Anonymous authentication based on zero-knowledge proof is utilized to ensure the privacy preservation of vehicles. To ensure fairness, the reward payments of winning vehicles are determined by not only the bids of vehicles but also their reputation and the data quality. Then, any rewarded vehicle can get the fair payment; on the contrary, malicious vehicles or task initiators will be punished. Finally, SPPIM is implemented by using smart contracts written via Solidity on a local Ethereum blockchain network. Both security analysis and experimental results show that the proposed SPPIM achieves privacy preservation and fair incentives at acceptable execution costs.
The Non-Fungible Token (NFT) market is mushrooming in recent years. The concept of NFT originally comes from a token standard of Ethereum, aiming to distinguish each token with distinguishable signs. This type of token can be bound with virtual/digital properties as their unique identifications. With NFTs, all marked properties can be freely traded with customized values according to their ages, rarity, liquidity, etc. It has greatly stimulated the prosperity of the decentralized application (DApp) market. At the time of writing (May 2021), the total money used on completed NFT sales has reached $34,530,649.86$ USD. The thousandfold return on its increasing market draws huge attention worldwide. However, the development of the NFT ecosystem is still in its early stage, and the technologies of NFTs are pre-mature. Newcomers may get lost in their frenetic evolution due to the lack of systematic summaries. In this technical report, we explore the NFT ecosystems in several aspects. We start with an overview of state-of-the-art NFT solutions, then provide their technical components, protocols, standards, and desired proprieties. Afterward, we give a security evolution, with discussions on the perspectives of their design models, opportunities, and challenges. To the best of our knowledge, this is the first systematic study on the current NFT ecosystems.
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Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
The function of money plays an essential and indisputable role in the development of trade. Typically, banknotes and coins have always been introduced by central authorities. Emerging after the 2008 crisis, however, Bitcoin, considered to be the original cryptocurrency, contributed to money in an unprecedented dimension as it is the first decentralized peer-to-peer payment network. Cryptocurrencies are in constant interaction and the casualty relationship, among other variables, with Brent Oil. This study attempts to investigate the relationship between Bitcoin, Ethereum and Brent Oil price movements using 210 daily data from 10.12.2019 to 01.10.2020, featuring the period of the start and the spread of the COVID-19 pandemic. In this study, the casualty relationship among Brent Oil, Bitcoin and Ethereum was examined with the Granger Causality test. As a result of the study, a bidirectional casualty relationship is determined between Brent Oil and Ethereum. However, a one-way causality relationship is found between Brent Oil and Bitcoin. On the other hand, there is no causality relationship between Ethereum and Bitcoin.
Blockchain technology has a great potential for improving efficiency, security and privacy of Electronic Health Records (EHR) sharing systems. However, existing solutions relying on a centralized database are susceptible to traditional security problems such as Denial of Service (DoS) attacks and a single point of failure similar to traditional database systems. In addition, past solutions exposed users to privacy linking attacks and did not tackle performance and scalability challenges. In this paper, we propose a permissioned Blockchain based healthcare data sharing system that integrates Blockchain technology, decentralized file system and threshold signature to address the aforementioned problems. The proposed system is based on Istanbul Byzantine Fault Tolerant (IBFT) consensus algorithm and Interplanetary File System (IPFS). We implemented the proposed system on an enterprise Ethereum Blockchain known as Hyperledger Besu. We evaluated and compared the performance of the proposed system based on various performance metrics such as transaction latency, throughput and failure rate. Experiments were conducted on a variable network size and number of transactions. The experimental results indicate that the proposed system performs better than existing Blockchain based systems. Moreover, the decentralized file system provides better security than existing traditional centralized database systems while providing the same level of performance.
Ethereum Smart Contracts based on Blockchain Technology (BT)enables monetary transactions among peers on a blockchain network independent of a central authorizing agency. Ethereum smart contracts are programs that are deployed as decentralized applications, having the building blocks of the blockchain consensus protocol. This enables consumers to make agreements in a transparent and conflict-free environment. However, there exist some security vulnerabilities within these smart contracts that are a potential threat to the applications and their consumers and have shown in the past to cause huge financial losses. In this study, we review the existing literature and broadly classify the BT applications. As Ethereum smart contracts find their application mostly in e-commerce applications, we believe these are more commonly vulnerable to attacks. In these smart contracts, we mainly focus on identifying vulnerabilities that programmers and users of smart contracts must avoid. This paper aims at explaining eight vulnerabilities that are specific to the application level of BT by analyzing the past exploitation case scenarios of these security vulnerabilities. We also review some of the available tools and applications that detect these vulnerabilities in terms of their approach and effectiveness. We also investigated the availability of detection tools for identifying these security vulnerabilities and lack thereof to identify some of them
For applications of Byzantine fault tolerant (BFT) consensus protocols where the participants are economic agents, recent works highlighted the importance of accountability: the ability to identify participants who provably violate the protocol. At the same time, being able to reach consensus under dynamic levels of participation is desirable for censorship resistance. We identify an availability-accountability dilemma: in an environment with dynamic participation, no protocol can simultaneously be accountably-safe and live. We provide a resolution to this dilemma by constructing a provably secure optimally-resilient accountability gadget to checkpoint a longest chain protocol, such that the full ledger is live under dynamic participation and the checkpointed prefix ledger is accountable. Our accountability gadget construction is black-box and can use any BFT protocol which is accountable under static participation. Using HotStuff as the black box, we implemented our construction as a protocol for the Ethereum 2.0 beacon chain, and our Internet-scale experiments with more than 4000 nodes show that the protocol achieves the required scalability and has better latency than the current solution Gasper, which was shown insecure by recent attacks.
Within cryptoassets we can find cryptocurrencies (e.g. Bitcoin, Ethereum) and digital tokens which are specific right or value representatives. One of areas that there are a lot of doubts regarding these new technological solutions is accounting. It is not clear how we can classify particular groups of cryptoassets and how to value them in financial statement. The aim of the chapter is to present the essence, use and valuation issues of cryptoassets and also to review the definitions of selected asset groups in the currently applicable accounting regulations to identify those asset groups to which cryptocurrencies can be classified. The chapter also discusses available accounting models under IFRS, with a major focus being put on the recognition and presentation in balance sheet. The assessment of models is based on distinguishing different types of tokens (payment, security, utility) as well as on differentiating between holders’ and issuers’ perspective.
This paper sets out to explore the nexus between economic policy uncertainty (EPU) and digital currencies. An integrated survey takes place based on eleven primary studies. Furthermore, an econometric analysis is conducted by the threshold ARCH, simple asymmetric ARCH and non-linear ARCH specifications covering the bull and the bear markets as well as the highly volatile period up to the present. Threshold ARCH is found to provide the best fit for estimations. Outcomes reveal that Bitcoin is strongly connected with EPU while Ethereum and Litecoin are not but are strongly linked with Bitcoin performance. Moreover, weak negative effects of the VIX on both cryptocurrencies are detected while oil exerts weak positive impacts on Ethereum. Overall, Ethereum and Litecoin could serve for diversifiers against Bitcoin or hedgers against traditional assets during highly stressed periods with the advantage of not being affected by economic policy uncertainty news.
Performance contracts used for servitized business models enable consideration of overall life-cycle costs rather than just production costs. However, practical implementation of performance contracts has been limited due to challenges with performance evaluation, accountability, and financial concepts. As a solution, this paper proposes the connection of the digital building twin with blockchain-based smart contracts to execute performance-based digital payments. First, we conceptualize a technical architecture to connect blockchain to digital building twins. The digital building twin stores and evaluates performance data in real-time while the blockchain ensures transparency and trusted execution of automatic performance evaluation and rewards through smart contracts. Next, we demonstrate the feasibility of both the concept and technical architecture by integrating the Ethereum blockchain with digital building models and sensors via the Siemens building twin platform. The resulting prototype is the first full-stack implementation of a performance-based smart contract in the built environment.
Smart contract is one of the core features of Ethereum and has inspired many blockchain descendants. Since its advent, the verification paradigm of smart contract has been improving toward high scalability. It shifts from the expensive on-chain verification to the orchestration of off-chain VM (virtual machine) execution and on-chain arbitration with the pinpoint protocol. The representative projects are TrueBit, Arbitrum, YODA, ACE, and Optimism. Inspired by visionaries in academia and industry, we consider the DNN computation to be promising but on the next level of complexity for the verification paradigm of smart contract. Unfortunately, even for the state-of-the-art verification paradigm, off-chain VM execution of DNN computation has an orders-of-magnitude slowdown compared to the native off-chain execution. To enable the native off-chain execution of verifiable DNN computation, we present Agatha system, which solves the significant challenges of misalignment and inconsistency: (1) Native DNN computation has a graph-based computation paradigm misaligned with previous VM-based execution and arbitration; (2) Native DNN computation may be inconsistent cross platforms which invalidates the verification paradigm. In response, we propose the graph-based pinpoint protocol (GPP) which enables the pinpoint protocol on computational graphs, and bridges the native off-chain execution and the contract arbitration. We also develop a technique named Cross-evaluator Consistent Execution (XCE), which guarantees cross-platform consistency and forms the correctness foundation of GPP. We showcase Agatha for the DNN computation of popular models (MobileNet, ResNet50 and VGG16) on Ethereum. Agatha achieves a negligible on-chain overhead, and an off-chain execution overhead of 3.0%, which represents an off-chain latency reduction of at least 602x compared to the state-of-the-art verification paradigm.
As the importance of vehicle data increases, it has become very important to safely store them. However, because onboard diagnostics scanners generally used to store vehicle data are IoT devices, security and capacity issues exist to store data safely and efficiently. To address this, we propose a system that stores vehicle data safely and efficiently using blockchain and IPFS. Users can access the system through DApp, an Ethereum-distributed application, and manage their vehicle data. Various experiments have been conducted to demonstrate the superior performance of this system, and the experimental results show its advantages in terms of data-processing speed and cost.
Tyler Kell, Haaroon Yousaf, Sarah Levin Allen, Sarah Meiklejohn · 5 authors
Pyramid schemes are investment scams in which top-level participants in a hierarchical network recruit and profit from an expanding base of defrauded newer participants. Pyramid schemes have existed for over a century, but there have been no in-depth studies of their dynamics and communities because of the opacity of participants' transactions. In this paper, we present an empirical study of Forsage, a pyramid scheme implemented as a smart contract and at its peak one of the largest consumers of resources in Ethereum. As a smart contract, Forsage makes its (byte)code and all of its transactions visible on the blockchain. We take advantage of this unprecedented transparency to gain insight into the mechanics, impact on participants, and evolution of Forsage. We quantify the (multi-million-dollar) gains of top-level participants as well as the losses of the vast majority (around 88%) of users. We analyze Forsage code both manually and using a purpose-built transaction simulator to uncover the complex mechanics of the scheme. Through complementary study of promotional videos and social media, we show how Forsage promoters have leveraged the unique features of smart contracts to lure users with false claims of trustworthiness and profitability, and how Forsage activity is concentrated within a small number of national communities.
Yuzhou Chen, Ignacio Segovia-Domínguez, Yulia R. Gel
There recently has been a surge of interest in developing a new class of deep learning (DL) architectures that integrate an explicit time dimension as a fundamental building block of learning and representation mechanisms. In turn, many recent results show that topological descriptors of the observed data, encoding information on the shape of the dataset in a topological space at different scales, that is, persistent homology of the data, may contain important complementary information, improving both performance and robustness of DL. As convergence of these two emerging ideas, we propose to enhance DL architectures with the most salient time-conditioned topological information of the data and introduce the concept of zigzag persistence into time-aware graph convolutional networks (GCNs). Zigzag persistence provides a systematic and mathematically rigorous framework to track the most important topological features of the observed data that tend to manifest themselves over time. To integrate the extracted time-conditioned topological descriptors into DL, we develop a new topological summary, zigzag persistence image, and derive its theoretical stability guarantees. We validate the new GCNs with a time-aware zigzag topological layer (Z-GCNETs), in application to traffic forecasting and Ethereum blockchain price prediction. Our results indicate that Z-GCNET outperforms 13 state-of-the-art methods on 4 time series datasets.
Algan Tezel, Pedro Febrero, Eleni Papadonikolaki, İbrahim Yitmen
The interest in the implementation of distributed ledger technologies (DLTs) is on the rise in the construction sector. One specific type of DLT that has recently attracted much attention is blockchain. Blockchain has been mostly discussed conceptually for construction to date. This study presents some empirical discussions on supply chain management (SCM) applications of blockchain for construction by collecting feedback for three blockchain-based models: project bank accounts (PBAs) for payments, reverse auction–based tendering for bidding, and asset tokenization for project financing. The feedback was collected from three focus groups and a workshop. The working prototypes for the models were developed on Ethereum. The implementation of blockchain in payment arrangements was found to be simpler than in tendering and project tokenization workflows. However, the blockchain integration of those workflows may have large-scale impacts on the sector in the future. A broad set of general and model-specific benefits/opportunities and requirements/challenges was also identified for blockchain in construction. Some of these include streamlined, transparent transactions and rational trust building, and the need for challenging the sector culture, upscaling the legacy information technology (IT) systems, and compliance with the regulatory structures.
Millions of smart contracts have been deployed onto Ethereum for providing various services, whose functions can be invoked. For this purpose, the caller needs to know the function signature of a callee, which includes its function id and parameter types. Such signatures are critical to many applications focusing on smart contracts, e.g., reverse engineering, fuzzing, attack detection, and profiling. Unfortunately, it is challenging to recover the function signatures from contract bytecode, since neither debug information nor type information is present in the bytecode. To address this issue, prior approaches rely on source code, or a collection of known signatures from incomplete databases or incomplete heuristic rules, which, however, are far from adequate and cannot cope with the rapid growth of new contracts. In this paper, we propose a novel solution that leverages how functions are handled by Ethereum virtual machine (EVM) to automatically recover function signatures. In particular, we exploit how smart contracts determine the functions to be invoked to locate and extract function ids, and propose a new approach named type-aware symbolic execution (TASE) that utilizes the semantics of EVM operations on parameters to identify the number and the types of parameters. Moreover, we develop SigRec, a new tool for recovering function signatures from contract bytecode without the need of source code and function signature databases. The extensive experimental results show that SigRec outperforms all existing tools, achieving an unprecedented 98.7 percent accuracy within 0.074 seconds. We further demonstrate that the recovered function signatures are useful in attack detection, fuzzing and reverse engineering of EVM bytecode.
Youssef Faqir-Rhazoui, Miller-Janny Ariza-Garzón, Javier Arroyo, Samer Hassan
Blockchain technology has enabled a thriving emergent ecosystem of tools and communities actively using decentralized systems. However, most blockchain infrastructure (e.g. Ethereum) requires users to pay some fees to execute their desired actions in these novel online services. To which extent an increase in the price of such fees negatively affects user activity? Would significant price surges deter users from using blockchain-enabled online services? In this work, we study the 2020 surge of transaction fee price in the Ethereum network, and analyze how that affected user activities. Our use cases are the blockchain-enabled Decentralized Autonomous Organizations (DAOs) from the platforms DAOstack and DAOhaus. Thus, we analyzed 5,580 transactions from 7,825 users grouped in 191 DAO communities, using a VAR model with a daily time series of the average fee value and the DAO operations. Our results show just a minor influence of the fee (gas) price and the activity of DAO users. The insensitivity of the activity to the fee price is an anomaly in a supposedly self-regulated market, and we consider this should be tackled in future implementations.
EIP-1559 is a new proposed pricing mechanism for the Ethereum protocol developed to bring stability to fluctuating gas prices. To properly understand this as a stochastic process, it is necessary to develop the mathematical foundations to understand under what conditions the base fee gas price outcomes behave as a stationary process, and when it does not. Understanding these mathematical fundamentals is critical to properly engineering a stable system.
Blockchain technology (BT) Ethereum Smart Contracts allows programmable\ntransactions that involve the transfer of monetary assets among peers on a BT\nnetwork independent of a central authorizing agency. Ethereum Smart Contracts\nare programs that are deployed as decentralized applications, having the\nbuilding blocks of the blockchain consensus protocol. This technology enables\nconsumers to make agreements in a transparent and conflict-free environment.\nHowever, the security vulnerabilities within these smart contracts are a\npotential threat to the applications and their consumers and have shown in the\npast to cause huge financial losses. In this paper, we propose a framework that\ncombines static and dynamic analysis to detect Denial of Service (DoS)\nvulnerability due to an unexpected revert in Ethereum Smart Contracts. Our\nframework, SmartScan, statically scans smart contracts under test (SCUTs) to\nidentify patterns that are potentially vulnerable in these SCUTs and then uses\ndynamic analysis to precisely confirm their exploitability of the\nDoS-Unexpected Revert vulnerability, thus achieving increased performance and\nmore precise results. We evaluated SmartScan on a set of 500 smart contracts\ncollected from the Etherscan. Our approach shows an improvement in precision\nand recall when compared to available state-of-the-art techniques.\n
Ethereum is an open-source, decentralized applications to support a cryptocurrency-based trading platform. The recent Ethereum hard fork, called ‘Istanbul’ took place in October 2019 due to threats from multitude of competitors has been completed. This hard fork brings six Ethereum Improvement Proposals (EIPs) which includes code execution, blockchain’s mining algorithm, increase data storage process capacity and also reduce Gas costs. Although these changes intended to improve the performance of Ethereum blockchain, many people have raise concern that forking is controversial. Cryptocurrency forks usually brings big changes ahead and may have serious implication on the price of Ethereum. Many resource appropriators (coin holders) are anxious about when will be the next network update for Ethereum. Most of the rules and decisions are made before these participants join the system or leave in the hands of developers of the project. There is lack of effective means for most resource appropriators or stakeholders to participate in system of decision-making for the next network update. This article provides a comprehensive overview of how decision-making for network update to be made in Ethereum and to propose the implementation of the management rights concerning the design, implementation of future changes to the Ethereum platform in order to achieve a clear collective-choice arrangement that allow most resource appropriators and stakeholders to participate in the decision-making process within the Ethereum community.
Bitcoin, one of the major cryptocurrencies, presents great opportunities and challenges with its tremendous potential returns accompanying high risks. The high volatility of Bitcoin and the complex factors affecting them make the study of effective price forecasting methods of great practical importance to financial investors and researchers worldwide. In this paper, we propose a novel approach called MRC-LSTM, which combines a Multi-scale Residual Convolutional neural network (MRC) and a Long Short-Term Memory (LSTM) to implement Bitcoin closing price prediction. Specifically, the Multi-scale residual module is based on one-dimensional convolution, which is not only capable of adaptive detecting features of different time scales in multivariate time series, but also enables the fusion of these features. LSTM has the ability to learn long-term dependencies in series, which is widely used in financial time series forecasting. By mixing these two methods, the model is able to obtain highly expressive features and efficiently learn trends and interactions of multivariate time series. In the study, the impact of external factors such as macroeconomic variables and investor attention on the Bitcoin price is considered in addition to the trading information of the Bitcoin market. We performed experiments to predict the daily closing price of Bitcoin (USD), and the experimental results show that MRC-LSTM significantly outperforms a variety of other network structures. Furthermore, we conduct additional experiments on two other cryptocurrencies, Ethereum and Litecoin, to further confirm the effectiveness of the MRC-LSTM in short-term forecasting for multivariate time series of cryptocurrencies.
Bitcoin, one of the major cryptocurrencies, presents great opportunities and\nchallenges with its tremendous potential returns accompanying high risks. The\nhigh volatility of Bitcoin and the complex factors affecting them make the\nstudy of effective price forecasting methods of great practical importance to\nfinancial investors and researchers worldwide. In this paper, we propose a\nnovel approach called MRC-LSTM, which combines a Multi-scale Residual\nConvolutional neural network (MRC) and a Long Short-Term Memory (LSTM) to\nimplement Bitcoin closing price prediction. Specifically, the Multi-scale\nresidual module is based on one-dimensional convolution, which is not only\ncapable of adaptive detecting features of different time scales in multivariate\ntime series, but also enables the fusion of these features. LSTM has the\nability to learn long-term dependencies in series, which is widely used in\nfinancial time series forecasting. By mixing these two methods, the model is\nable to obtain highly expressive features and efficiently learn trends and\ninteractions of multivariate time series. In the study, the impact of external\nfactors such as macroeconomic variables and investor attention on the Bitcoin\nprice is considered in addition to the trading information of the Bitcoin\nmarket. We performed experiments to predict the daily closing price of Bitcoin\n(USD), and the experimental results show that MRC-LSTM significantly\noutperforms a variety of other network structures. Furthermore, we conduct\nadditional experiments on two other cryptocurrencies, Ethereum and Litecoin, to\nfurther confirm the effectiveness of the MRC-LSTM in short-term forecasting for\nmultivariate time series of cryptocurrencies.\n