【Abstract】While stablecoins such as Tether closely track the peg, there is some evidence for recurring spikes in stablecoins' intraday volatilities rendering stablecoin volatilities unstable (Grobys et al., 2021). Using the Barndorff-Nielsen and Shephard (2006a) methodology, the purpose of our study is to examine whether jumps in Tether have an impact on (subsequent) Bitcoin returns. We retrieve hourly data for Bitcoin and Tether from Bitfinex covering the November 2018 to June 2021 period and encode the binary choice (1 - 'jump' and 0 - 'no jump') using bi-power variation based on asymptotic distribution theory at 5% significance level for each trading day. Our results show that the joint effect of positive jumps in Tether in association with an 1% increase in Tether returns on the prior day significantly predict negative prices changes in Bitcoin ranging from-3.65% to-8.49% in daily terms. Our results remain robust even after controlling for various other variables.
【Abstract】The price fluctuation of cryptocurrencies represented by Bitcoin has nonlinear structure characteristics. We select the Bitcoin closing price data from 2013 to 2021, and use GARCH (1,1)-GED to fit the volatility series. We confirm that Bitcoin price Fluctuation has nonlinear dynamics through BDS test, Hurst exponent, correlation dimension test and Lyapunov exponent. We find that the price fluctuation of cryptocurrency does not obey the random walk, and its fluctuation is positively correlated with time. Bullish information and bearish information have basically the same impact on cryptocurrency fluctuations. Cryptocurrency price fluctuations have cyclical trends and inherent long-term unpredictability, as well as certain fractal and chaos characteristics. ARCH effect and long memory characteristics of cryptocurrency return series show that cryptocurrency price fluctuations are Clustering and persistence. These two characteristics constitute the nonlinear dynamic mechanism of Bitcoin price fluctuation. Overall, our study has important implications for investors and regulators within cryptocurrency markets.
【Abstract】Using a hand-collected dataset containing bullish, neutral, and bearish predictions for Bitcoin published by crypto experts, we show that neutral and bearish predictions are followed by negative abnormal returns whereas bullish predictions are not associated with nonzero abnormal returns. Based on all outstanding predictions, we compute prediction revisions relative to (i) the latest issued prediction and (ii) the outstanding consensus prediction. Downward revisions are followed by negative abnormal returns. We conclude that crypto experts are skilled information intermediaries on the Bitcoin market.
【Abstract】This paper aims to compare the safe-haven roles of gold and Bitcoin for energy commodities, including oils and petroleum, during COVID-19. Specifically, we examine the presence of reduction in downside risk after mixing gold/Bitcoin with such energy commodities. To do this, we account for dependence among energy commodities and gold/Bitcoin returns by applying a (vine) copula. The findings show that gold substantially reduces the downside risk of a portfolio containing any allocation to gold and energy commodities, indicating its safe-haven ability. In contrast, Bitcoin's safe-haven functionality is inconsistent since the downside risk reduction is achieved for Bitcoin's small allocation only.
【Abstract】We study the relationship between return and volatility of non-fungible tokens (NFT) segments and media coverage during the outbreak of the COVID-19 pandemic in a connectedness framework. We document media coverage as a net transmitter of spillover for both the return and volatility of NFT segments. We find that NFTs representing the Utilities segment is a major transmitter of spillover. Our findings have important implications for portfolio managers, regulators, and policymakers.
【Abstract】This research examined the impact of the stock market on Bitcoin during COVID-19 and other uncertainty periods. Based on the quantile regression results, during periods of high uncertainty, such as COVID-19, the S&P 500 returns significantly affected Bitcoin returns. Moreover, this research applied the VAR (1)-GARCH (1, 1) model to investigate the spillover effect from the stock market to Bitcoin. According to the findings, the shocks from the stock market also influenced Bitcoin's volatility during COVID-19 and other periods of turmoil.
【Abstract】Cryptocurrency trading is subject to strict government-imposed restrictions in China since September 2017, when trading platforms were shut down. Using a VAR-GARCH-BEKK model, we investigate the effectiveness of this trading ban by examining its consequences on the relationship between Chinese investors' attention and Bitcoin. Our results demonstrate that Chinese investors played a dominant role in the Bitcoin price formation before the ban, and that their influence has not significantly decreased after the shutdown of Chinese trading platforms. Aiming to explain our findings, we provide strong evidence that the crackdown on Bitcoin trading has not been effective as Chinese investors continue to purchase Bitcoin using the stablecoin Tether instead of Chinese yuan.
【Abstract】During the recent COVID-19 pandemic, many commonalities shared by Bitcoin and gold raise the question of whether Bitcoin can hedge inflation or provide a safe haven as gold often does. By estimating a Vector Autoregression (VAR) model, we provide systematic evidence on the relationship among inflation, uncertainty, and Bitcoin and gold prices. Bitcoin appreciates against inflation (or inflation expectation) shocks, confirming its inflation-hedging property claimed by investors. However, unlike gold, Bitcoin prices decline in response to financial uncertainty shocks, rejecting the safe-haven quality. Interestingly, Bitcoin prices do not decrease after policy uncertainty shocks, partly consistent with the notion of Bitcoin's independence from government authorities. We also find an interesting asymmetry in the drivers of Bitcoin price dynamics between the bullish and bearish market. The main findings hold with or without the COVID-19 pandemic episode.
【Abstract】In this paper, we analyze the connectedness between returns for non-fungible tokens (NFTs) and other financial assets (equities, bonds, currencies, gold, oil, Ethereum) during the period from January 2018 to June 2021. By using the Time-Varying Parameter Vector Autoregressions (TVPVAR) approach, we show that the overall connectedness between the returns for financial assets increased during the COVID-19 period. Our static analysis shows that the behavior of the majority of NFT returns is attributable to endogenous shocks and only a small portion of this variation resulted from the impact of innovation in other assets. The results suggest that NFTs are mainly independent of shocks from common assets classes and even from their close relation, Ethereum. The dynamic analysis across time reveals that during normal times, NFTs act as transmitters of systemic risk to some degree, but during stressful times, their role shifts, and they act as absorbers of risk spillovers. This suggests that NFTs may have diversification benefits during turbulent times, as apparent during the COVID-19 crisis, and especially around the great March 2020 market plunge.
【Abstract】This paper investigates the hedging and safe-haven properties of Bitcoin (BTC) and Ethereum (ETH) for emerging stock market (ESM) indices. The results show all hedging costs are increasing during the pandemic. When the study's hedging effectiveness is generally evaluated, BTC provides better protection against ESM indices than ETH. Moreover, during the pre-pandemic period, investors should create portfolios using an optimal weights-based strategy rather than a hedged portfolio-based strategy. Finally, while BTC and ETH have weak safe-haven properties against most ESM indices, only BTC have strong safe-haven properties against the Malaysia stock index during the pandemic period.
【Abstract】We investigate the pricing efficiency of numerous popular cryptocurrencies using a wide range of non-economic events that include calendar anomalies, natural condition-based anomalies, holidays when US exchanges are closed and secular and ethnic holidays when exchanges are open - all documented in the finance literature regarding equities. We document the existence of very few similar effects in the examined cryptocurrencies. Generally, anomalies found in Bitcoin do not hold for other cryptocurrencies and vice versa. The within-the-month effect is the only effect common to all cryptocurrencies. Our results have implications for efficient asset pricing and diversification benefits for these currencies.
【Abstract】The study aims to examine the hedge and safe-haven properties of three heavyweight cryptocurrencies-Bitcoin, Ripple, and Ethereum-against the stock, commodity, and foreign exchange markets. The study sample covers the period of August 2011 to September 2020 and therefore includes the current coronavirus disease-2019 (COVID-19) crisis. Using a logistic smooth transition regression model (LSTR2), the study findings indicate the ability of monitored cryptocurrencies to act as safe-haven assets, but such behavior differs across markets. Interestingly, during the pandemic period, Ethereum provides the strongest safe haven function for the commodity market. According to our findings, we are mindful of that the COVID-19 outbreak provides an exciting opportunity to advance our knowledge of the prominence of new coins such as Ethereum that are gradually gaining supremacy in the cryptocurrency market to the detriment of traditional cryptocurrencies like Bitcoin.
【Abstract】Since the creation of Bitcoin, there has been an explosion in the number of cryptocurrencies developed and although Ethereum is the second largest cryptocurrency in terms of market capitalization, there is very little research on this cryptocurrency and in this paper, we provide an overview of this cryptocurrency. We examine the addresses, transactions and fees as well as users holding patterns to the Ethereum blockchain. Therefore this paper offers the first detailed overview of the Ethereum blockchain.
【Abstract】We apply the (Phillips et al., 2015a,b) methodology to date-stamp bubbles in the Ethereum blockchain. Our analysis of the drivers of fundamental value suggests that the explosive behavior documented in ether prices does not constitute speculative bubbles but reflects the abrupt rally of demand for the use of the Ethereum Virtual Machine tied to the development of the decentralized application (dApp) ecosystem.
【Abstract】The fundamental value of Bitcoin is difficult to evaluate because there is no underlying company or cashflow. Extant literature documented various macro-economic factors and technical factors to price Bitcoin. However, for a highly speculative instrument, investors' behavior plays an important role in asset pricing. This paper incorporates Bitcoin Ethereum synchronicity conditional on upside volatility of Bitcoin as a proxy for the fear of high Bitcoin prices. Empirical results reveal that when upside volatility is high, Ethereum synchronicity exerts a significant positive influence on Bitcoin crash risk.
【Abstract】In this paper, we study the long memory behavior of Bitcoin, Litecoin, Ethereum, Ripple, Monero, and Dash with a focus on the COVID-19 period. Initially, we apply a time-varying Lifting method to estimate the Hurst exponent for each cryptocurrency. Then we test for a change in persistence over time. To model the multivariate con-nectivity, the wavelet-based multivariate long memory approach proposed by Achard and Gannaz (2016) is implemented. Our results indicate a change in the long-range dependence for the majority of cryptocurrencies, with a noticeable downward trend in persistence after the 2017 bubble and then a dramatic drop after the outbreak of COVID-19. The drop in persistence after COVID-19 is further illustrated by the Fractal connectivity matrix obtained from the Wavelet long-memory model. Our findings provide important implications regarding the evolution of market efficiency in the cryptocurrency market and the associated fractal structure and dy-namics of the crypto prices over time
【Abstract】This paper studies the tail dependence among carbon prices, green and non-green cryptocurrencies. Using daily closing prices of carbon, green and non-green cryptocurrencies from 2017 to 2021 and a quantile connectedness framework, we find evidence of asymmetric tail dependence among these markets, with stronger dependence during highly volatile periods. Moreover, carbon prices are largely disconnected from cryptocurrencies during periods of low volatilities, while Bitcoin and Ethereum exhibit time-varying spillovers to other markets. Our results also show that green cryptocurrencies are weakly connected to Bitcoin and Ethereum, and their net connectedness are close to 0, except during the COVID-19 pandemic. Finally, we find a significant influence of macroeconomic and financial factors on the tail dependence among carbon, green and non-green cryptocurrency markets. Our results highlight the time-varying diversification benefits across carbon, green and non-green cryptocurrencies and have important implications for investors and policymakers
【Abstract】Blockchain is a decentralized ledger system that enables transactional consensus among untrusted nodes. Due to the independence between blockchains, it is tough to complete asset exchange tasks between diverse chains. Facing this problem, multiple cross-chain exchange schemes were proposed, but they have not been widely used due to various defects in terms of compatibility, flexibility, security, and practicability. In this paper, we construct a comprehensive cross-chain exchange system called Practical AgentChain. Various coins can be mapped to the corresponding tokens on Practical AgentChain for trading. One or multiple trading operators can spontaneously form a service deposit pool on existing blockchains and register a trading group for profit using the well-designed smart contract called Agent Contract. All the trading groups competitively provide cross-chain exchange services. Clients with exchange currency requirements can freely select an appropriate trading group to request services, such as mapping assets to tokens or withdraw tokens. The choice of the trading group is made according to the group's deposit and the group members' reputation recorded on the Agent Contract. This system also incorporates Town crier as the reliable Data Oracle system for obtaining the status of cross-chain transactions and combines a matching exchange protocol to ensure the fairness of on-chain token exchange. In addition, we design an impartial service arbitration mechanism and a deposit allocation scheme to ensure the reliability of the system. Through security analysis, our system can resist re-use attacks, denial-of-service attacks and resolve service availability issues. We modify the Town crier adaptively and combine it with Ethereum to implement the simulation. The experimental results show that Practical AgentChain is efficient and low-cost. Besides, our system can be deployed on any existing blockchain that supports smart contracts since the system's architecture is highly portable. (C) 2021 Elsevier B.V. All rights reserved.
【Abstract】This paper analysis the dynamic of the volatility connectedness between three main categories of the cryptocurrency market (mineable coins, non-mineable coins and tokens).1 Furthermore, it investigates factors (internal and external) that can help to predict the net directional and total volatility connectedness indices. We find that the CC market is highly connected where mining coins dominate the CC market and are typically net transmitter of shocks. For the non-mining coins, a low connectedness with all mining coins and tokens versus a high connectedness with Stellar are highlighted. The results show also that, unlike previous studies, Ethereum is the dominant contributor to volatility spillover and the only CC net pairwise transmitter in our sample. Interestingly, we find that most of the tokens are highly connected with their platform (Ethereum) rather than bitcoin. Finally, we find that both financial and macroeconomic indicators play an important role in explaining the total and net directional connectedness, a result that can significantly help cryptocurrency market participants and investors improve their risk management and the benefits from diversification.
【Abstract】A Central Bank Digital Currency (CBDC) launched by the Bank of England could enable businesses to directly make electronic payments. It can be argued that digital payment is helpful in supply chain management applications. However, the adoption of CBDC in the supply chain could bring new turbulence since the CBDC value may fluctuate. Therefore, this paper intends to optimize the production plan of manufacturing supply chain based on a volatility clustering model by reducing CBDC value uncertainty. We apply both GARCH model and machine learning model to depict the CBDC volatility clustering. Empirically, we employed Baltic Dry Index, Bitcoin and exchange rate as main variables with sample period from 2015 to 2021 to evaluate the performance of the two models. On this basis, we reveal that our machine learning model overwhelmingly outperforms the GARCH model. Consequently, our result implies that manufacturing companies' performance can be strengthened through CBDC uncertainty reduction.
【Abstract】Because of the popularity of mobile devices, crowdsensing has emerged as a data sensing paradigm for collecting large-scale data. Blockchain technology is promising to address problems of privacy and trust existing in centralized crowdsensing systems. However, it is challenging for crowdsensing applications to use a public blockchain to collect real-time or large-scale data since current blockchain systems lack of scalability. We propose a crowdsensing scheme that combines Trusted Execution Environments (TEE) with a public blockchain, achieving high efficiency with guarantees of privacy and trust. The scheme is a layer-two blockchain solution that supports off-chain multi-round sensing-data evaluations inside TEE enclaves, so that the sensing data needs not be propagated over the blockchain network. Besides, the scheme prevents false-reporting and free-riding for workers and requesters without reliance on a trusted third party. Moreover, the scheme secures the sensing data, letting only the worker and the corresponding requester know the data. Evaluations of our experimental prototype demonstrate the efficiency of our designs, as well as the reasonable on-chain monetary cost of running a task's smart contract and performing token payments using Ethereum.
【Abstract】As the first blockchain platform supporting smart contracts, Ethereum has become increasingly popular in recent years and generates a massive number of transaction records. Nowadays, millions of accounts in Ethereum have been reported to participate in a variety of businesses, and some of them have been found to be involved in illegal behaviors or even cyber-crimes by exploiting the pseudonymous nature of blockchain. Therefore, there is an urgent need for an effective method to conduct account classification and audit transaction behaviors of each account. In this paper, we model the Ethereum transaction records as a transaction network, and the account classification problem is converted to a node classification problem. Based on the Ethereum transaction network, we propose a novel framework named Filter and Augment Graph Neural Network (FA-GNN), which can retain the information of important neighbors and augment node features with high-order information. Experimental results demonstrate that our proposed FA-GNN outperforms state-of-the-art methods in Ethereum account classification.
【Abstract】Bitcoin mining is not only the fundamental process to maintain Bitcoin network, but also the key linkage between the virtual cryptocurrency and the physical world. A variety of issues associated with it have been raised, such as network security, cryptoasset management and sustainability impacts. Investigating Bitcoin mining from a spatial perspective will provide new angles and empirical evidence with respect to extant literature. Here we explore the spatial distribution of Bitcoin mining through bottom-up tracking and geospatial statistics. We find that mining activity has been detected at more than 6000 geographical units across 139 countries and regions, which is in line with the distributed design of Bitcoin network. However, in terms of computing power, it has demonstrated a strong tendency of spatial concentration and association with energy production locations. We also discover that the spatial distribution of Bitcoin mining is dynamic, which fluctuates with diverse patterns, according to economic and regulatory changes.
【Abstract】With countless devices connected to the Internet of Things, trust mechanisms are especially important. IoT devices are more deeply embedded in the privacy of people's lives, and their security issues cannot be ignored. Smart contracts backed by blockchain technology have the potential to solve these problems. Therefore, the security of smart contracts cannot be ignored. We propose a flexible and systematic hybrid model, which we call the Serial-Parallel Convolutional Bidirectional Gated Recurrent Network Model incorporating Ensemble Classifiers (SPCBIG-EC). The model showed excellent performance benefits in smart contract vulnerability detection. In addition, we propose a serial-parallel convolution (SPCNN) suitable for our hybrid model. It can extract features from the input sequence for multivariate combinations while retaining temporal structure and location information. The Ensemble Classifier is used in the classification phase of the model to enhance its robustness. In addition, we focused on six typical smart contract vulnerabilities and constructed two datasets, CESC and UCESC, for multi-task vulnerability detection in our experiments. Numerous experiments showed that SPCBIG-EC is better than most existing methods. It is worth mentioning that SPCBIG-EC can achieve F1-scores of 96.74%, 91.62%, and 95.00% for reentrancy, timestamp dependency, and infinite loop vulnerability detection.
【Abstract】The anonymity and de-anonymity of blockchain and Bitcoin have always been a hot topic in blockchain related research. Since Bitcoin was created by Nakamoto in 2009, it has, to some extent, deviated from its currency attribute as a trading medium but instead turned into an object for financial investment and operations. In this paper, the power-law distribution that the Bitcoin network obeys is given with mathematical proof, while traditional de-anonymous methods such as clustering fail to satisfy it. Therefore, considering the profit-oriented characteristics of Bitcoin traders in such occasion, we put forward a de-anonymous heuristic approach that recognizes and analyzes the behavioral patterns of financial High-Frequency Transactions(HFT), with realtime exchange rate of Bitcoin involved. With heuristic approach used for de-anonymity, algorithm that deals with the adjacency matrix and transition probability matrix are also put forward, which then makes it possible to apply clustering to the IP matching method. Basing on the heuristic approach and additional algorithm for clustering, finally we established the de-anonymous method that matches the activity information of the IP with the transaction records in blockchain. Experiments on IP matching method are applied to the actual data. It turns out that similar behavioral pattern between IP and transaction records are shown, which indicates the superiority of IP matching method.
【Abstract】There is a growing stream of empirical research that endeavors to identify the influential variables contributing to the price formation of cryptocurrencies and, in particular, Bitcoin. However, results of those studies generally remain inconsistent in terms of not only the true combination of factors that affect Bitcoin prices, but also the nature of effects (positive vs. negative) that each individual factor has on the price behavior. The present study investigates the robustness of a wide variety of candidate determinants that have been the focus of attention in relevant literature. Our inquiry relies on the extreme bounds analysis (EBA), which is a type of large-scale sensitivity analysis capable of addressing model uncertainty issues. The findings suggest that crypto market forces of supply and demand, public interest, and economic policy uncertainty are the only variables robust to all possible variations in the conditioning information set. Our evidence argues in favor of the predominance of cryptocurrency-related determinants over global macroeconomic and financial ones in explaining Bitcoin price movements.
【Abstract】This paper reports evidence of intraday return predictability, consisting of both intraday momentum and reversal, in the cryptocurrency market. Using high-frequency price data on Bitcoin from March 3, 2013, to May 31, 2020, it shows that the patterns of intraday return predictability change in the presence of large intraday price jumps, FOMC announcement release, liquidity levels, and the outbreak of the COVID-19. Intraday return predictability is also found in other actively traded cryptocurrencies such as Ethereum, Litecoin, and Ripple. Further analysis shows that the timing strategy based on the intraday predictors produces higher economic value than the benchmark strategy such as the always-long or the buy-and-hold. Evidence of intraday momentum can be explained in light of the theory of late-informed investors, whereas evidence of intraday reversal, which is unique to the cryptocurrency market, can be related to investors' overreaction to non-fundamental information and overconfidence bias.
【Abstract】Virtual reality (VR) has been brought closer to the general public over the past decade as it has become increasingly available for desktop and mobile platforms. As a result, consumer-grade VR may redefine how people learn by creating an engaging "hands-on" training experience. Today, VR applications leverage rich interactivity in a virtual environment without real-world consequences to optimize training programs in companies and educational institutions. Therefore, the main objective of this article was to improve the collaboration and communication practices in 3D virtual worlds with VR and metaverse focused on the educational and productive sector in smart factory. A key premise of our work is that the characteristics of the real environment can be replicated in a virtual world through digital twins, wherein new, configurable, innovative, and valuable ways of working and learning collaboratively can be created using avatar models. To do so, we present a proposal for the development of an experimental framework that constitutes a crucial first step in the process of formalizing collaboration in virtual environments through VR-powered metaverses. The VR system includes functional components, object-oriented configurations, advanced core, interfaces, and an online multi-user system. We present the study of the first application case of the framework with VR in a metaverse, focused on the smart factory, that shows the most relevant technologies of Industry 4.0. Functionality tests were carried out and evaluated with users through usability metrics that showed the satisfactory results of its potential educational and commercial use. Finally, the experimental results show that a commercial software framework for VR games can accelerate the development of experiments in the metaverse to connect users from different parts of the world in real time.
【Abstract】The advent of the metaverse age has gradually transformed digital survival from a fantasy in science fiction to a reality. Especially in recent years, the college students, as the objects of ideological and political education in universities, have been deeply embedded in their learning, social interaction, entertainment, and consumption behaviors, presenting new characteristics of the times. From the aspects of the background of intelligent technology on College Students' network behavior, the types of College Students' network behavior, the multiple effects of intelligent technology, the nature of College Students' network behavior, etc., provide some basis for ideological and political education.
【Abstract】In this research, we provided an answer to a very important trading question, what is the optimal number of technical tools in order to achieve the best trading results for both swing trade that uses daily bars and intraday trade that uses minutes bars? We designed Machine Learning (ML) systems that can trade four major cryptocurrencies: Bitcoin, Ethereum, BNB, and Solana. We found that more indicators do not necessarily mean better trading performance. Swing traders that use daily bars should trade Bitcoin and Solana using Ichimoku Cloud (IC) plus Moving Average Convergence Divergence (MACD), Ethereum with IC plus Chaikin Money Flow (CMF), and BNB with IC alone. With regard to intraday trading, we documented that different cryptocurrencies should be trading using different time frames. These results emphasize that the optimal number of indicators that are used to trade daily bars is one or, at maximum, two. The Multi-Layer (MUL) system that consists of all three examined technical indicators failed to improve the trading results for both days (swing) and intraday trades. The main implication of this study for traders is that more indicators does not necessarily improve trades performances.
【Abstract】This article analyzes national, political, global geopolitical, ethical, social, and environmental issues associated with Web3 and the metaverse. It also introduces the feature articles, columns, and departments that appear in this issue.
【Abstract】The rapid growth of blockchain technology is giving rise to siloed blockchains. Blockchains can efficiently store values and assets, but their inability to interoperate is reducing their usability. Lack of cross-chain exchange of assets and data is hindering the novel developments using blockchain technology. Interoperability solutions designed for blockchains mostly use a trusted third party: centralized or decentralized. Atomic swap solutions provide cross-chain asset exchange without involving any trusted third party. However, most of the atomic swap solutions proposed in the literature are for private blockchain and are not practically implemented. This paper proposes a solution for implementing an atomic swap between public blockchains using Hash Time Locked Contract (HTLC). We have also formulated the time-lock equations using the confirmation time of probabilistic blockchains to be used in HTLC. The accuracy of proposed time-lock equations and the performance of the atomic swap solution is evaluated by implementing HTLC between Ethereum and Tron blockchains. Redeem and refund functions of HTLC are implemented as conflicting events, and only one of the two can execute for an atomic swap. The implemented atomic swap solution maintains atomicity and adheres to time-lock values calculated using the proposed time-lock equations.
【Abstract】In recent years, blockchain technology has been developing rapidly. More and more traditional industries are using blockchain as a platform for information storage and financial transactions, mainly because of its new characteristics of non-tamperability and decentralization compared with the traditional systems. As a representative of blockchain 2.0, Ethereum has gained popularity upon its introduction. However, because of the anonymity of blockchain, Ethereum has also attracted the attention of some unscrupulous people. Currently, millions of contracts are deployed on Ethereum, many of which are fraudulent contracts deployed by unscrupulous people for profit, and these contracts are causing huge losses to investors worldwide. Ponzi contracts are typical of these contracts, which mainly reward the funds invested by later investors to early investors, and later investors will have no gain. However, although there are some studies for identifying Ponzi contracts on Ethereum, there is some room for progress in the research. Therefore, we propose a method to detect Ponzi scheme contracts on Ethereum-CTRF. This method forms a dataset by extracting the word features and sequence features of the smart contract's code and the features of transactions. The dataset is divided into a training set and a test set. Oversampling is performed on the training set to deal with the problem of positive and negative sample imbalance. Finally, the model is trained on the training set and tested on the test set. The experimental results show that the model has significantly improved recall compared with existing Ponzi contract detection methods.
