Mathew Fukuzawa, Brandon M. McConnell, Michael G. Kay, Kristin Thoney-Barletta · 5 authors
Purpose Demonstrate proof-of-concept for conducting NFL Draft trades on a blockchain network using smart contracts. Design/methodology/approach Using Ethereum smart contracts, the authors model several types of draft trades between teams. An example scenario is used to demonstrate contract interaction and draft results. Findings The authors show the feasibility of conducting draft-day trades using smart contracts. The entire negotiation process, including side deals, can be conducted digitally. Research limitations/implications Further work is required to incorporate the full-scale depth required to integrate the draft trading process into a decentralized user platform and experience. Practical implications Cutting time for the trade negotiation process buys decision time for team decision-makers. Gains are also made with accuracy and cost. Social implications Full-scale adoption may find resistance due to the level of fan involvement; the draft has evolved into an interactive experience for both fans and teams. Originality/value This research demonstrates the new application of smart contracts in the inter-section of sports management and blockchain technology.
The development of low-carbon power systems has not only elevated the investment costs of power enterprises, but also generated a vast amount of electricity data. The electricity data trading holds promising potential as a primary means to cover investment costs. However, there is a lack of research on the electricity data trading. To address this issue, this article designs an electricity data trading method based on price game and blockchain for low-carbon power systems. It encompasses a data trading framework and the corresponding trading mechanism. The proposed trading framework contains data providers, data consumers, and a blockchain-based information system that plays the role of the data servicer to handle the transactions between data providers and consumers. The proposed trading mechanism mainly consists of three parts: 1) valuation; 2) pricing; and 3) copyrights confirmation. Those parts are executed sequentially to complete the electricity data trading process from valuation to clearing. Specially, the information theory is employed to realize multidimensional electricity data valuation. Further, the data trading game pricing is formulated as a multiobjective optimization problem considering market power constraints to solve. In addition, the digital watermarking combined with blockchain is designed to protect the electricity data copyright. With those components, the designed electricity data trading method enables the power enterprises to make profit from the low-carbon smart energy systems. Finally, experiments demonstrate the effectiveness of the proposed method.
Andrea De Salve, Alessandro Brighente, Mauro Conti
Modeling and predicting the behavior of nodes and users in blockchains provide opportunities for business strategy optimization. Indeed, the number of interactions of a node is strictly related to its balance and its prediction may be used for analytics purposes and investment strategies. However, the amount and diversity of information stored on the blockchain demand advanced tools for the modeling and analysis of blockchain data. Such tools should be able to capture the dynamicity and interaction of multiple independent actors, considering a large number of variables and dynamic interaction graph topologies. This is exacerbated by the use of smart contracts, programs stored in blockchain blocks that bring automation to blockchain’s operations and thus increasing the variability of the resulting interaction graphs. Existing modeling methodologies are unable to keep track of all these details, as they are not able to capture the temporal variability of the network. In this paper, we propose a novel framework for modeling and predicting the behavior of smart contracts on a blockchain. We propose the concept of temporal smart contracts networks, i.e., graphs representing the temporal evolution of interactions and data flow. Our framework allows the creation of temporal smart contract networks with different granularity levels by considering different interaction patterns between smart contracts, externally owned accounts, and internal transactions. Thanks to these graphs, we are able to model features such as the node in degree and amount of ether received by a smart contract, which are directly related to its behavior. We incorporate our modeling approach in Ethereum Data Inspection Tool (EDIT), a novel tool able to model interactions and predict them based on historical data. We test different machine learning models to predict features extracted by EDIT, hence allowing for the prediction of the overall behavior of the smart contract. We test EDIT on the Ethereum blockchain and model several temporal smart contracts networks, which represent the interactions and the data flow resulting from about 4 000 000 consecutive blocks. The evaluation of different real case studies shows that the proposed framework is able to predict, with a mean absolute error close to 1%, the evolution of several interesting properties (e.g., amount of received ether) related to both accounts and smart contracts.
Ingrid Bauer, Qianyu Liu, Claudio J. Tessone, Gerhard Schwabe
While a wealth of potentially valuable data is generated and stored every year, many businesses suffer from inefficiencies, information asymmetries, and high storage costs, and lack knowledge on how to monetize their data assets. Blockchain is said to offer crucial building blocks to enable a verified, traceable exchange and trading with sensitive data goods and to address current challenges. While the technology's potentials for decentralized data markets have been discussed, the question of how to realize it to optimize trading and welfare remains open. Applying design-science research methods and computational simulation to a real-world business-oriented blockchain project, this study proposes a market model. By adopting the consortium blockchain, we are thinking outside the confines of tokens tied to a blockchain when applying blockchain to the data trading market. Our marketplace is designed outside the speculative tokens space and can focus on the data trading marketplace. We evaluate the effects of different pricing functions on market welfare and trading in on-chain data goods. The results indicate that data trading and welfare can be maximized through a logarithmic pricing function. Further, in a market of heterogeneous agents, unexpectedly, we observe a tipping point in transaction fees above which market operations collapse. Monitoring the market's consumer price elasticity helps us to avoid this collapse node, and we can also impact it by controlling transaction costs. Academics and practitioners can learn about the idiosyncrasies of blockchain in market design and operation.
Purpose This paper aims to explore the problem of power imbalance within decentralized autonomous organizations (DAOs) and propose potential solutions that could contribute to enhancing the democratic nature of DAOs. Design/methodology/approach In this paper, the authors apply a qualitative methodology. Using a thematic coding analysis, the authors process data collected from interviews with 11 experts. Findings Multiple factors contribute to the perceived lack of democracy within DAOs, such as token concentration and effective stakeholder communication. Next, quadratic voting has the potential to enhance democracy within DAOs, but this mechanism must be implemented mindfully. Finally, the results were nuanced when it comes to the effectiveness of liquid democracy in DAOs to enhance voter participation and representation. Originality/value To the best of the authors’ knowledge, this paper is one of the first research contributions to propose recommendations to address the power imbalance within DAOs and to contribute to the advancement of decentralized decision-making structures.
Open-cry electronic auctions have revolutionized the landscape of high-value transactions for buying and selling goods. Online platforms such as eBay and Tradera have popularized these auctions due to their global accessibility and convenience. However, these centralized auctioning platforms rely on trust in a central entity to manage and control the processing of bids, e.g., the submission time and validity. The use of blockchain technologies for constructing decentralized systems has gained popularity for their versatility and useful properties toward decentralization. However, blockchain-based open-cry auctions, are sensitive to the order of transactions and deadlines which, in the absence of a governing party, need to be provided in the system design. In this paper, we identify the key properties for the development of decentralized open-cry auctioning systems, including verifiability, transaction immutability, ordering, and time synchronization. Three prominent blockchain platforms, namely, Ethereum, Hyperledger Fabric, and R3 Corda were analyzed in terms of their capabilities to ensure these properties for gap identification. We propose a solution design that addresses these key properties and presents a proof-of-concept (PoC) implementation of such design. Our PoC uses Hyperledger Fabric and mitigates the identified gaps related to the time synchronization of this system by utilizing an external component. During the chaincode execution, the creation and submission of bids initiate requests to the time service API. This API service retrieves trusted timestamps from NTP services to obtain accurate bid times. We then analyzed the system design and implementation in the context of the identified key properties. Lastly, we conducted a performance evaluation of the time service and the PoC system implementation in time-sensitive scenarios and assessed its overall performance.
Handling congestion in blockchain systems is a fundamental problem given that the security and decentralization objectives of such systems lead to designs that compromise on (horizontal) scalability (what sometimes is referred to as the "blockchain trilemma"). Motivated by this, we focus on the question whether it is possible to design a transaction inclusion policy for block producers that facilitates fee and delay predictability while being incentive compatible at the same time. Reconciling these three properties is seemingly paradoxical given that the dominant approach to transaction processing is based on first-price auctions (e.g., as in Bitcoin) or dynamic adjustment of the minimum admissible fee (e.g. as in Ethereum EIP-1559) something that breaks fee predictability. At the same time, in fixed fee mechanisms (e.g., as in Cardano), fees are trivially predictable but are subject to relatively inexpensive bribing or denial of service attacks where transactions may be delayed indefinitely by a well funded attacker, hence breaking delay predictability. In this work, we set out to address this problem by putting forward blockchain space tokenization (BST), namely a new capability of a blockchain system to tokenize its capacity for transactions and allocate it to interested users who are willing to pay ahead of time for the ability to post transactions regularly for a period of time. We analyze our system in the face of worst-case transaction-processing attacks by introducing a security game played between the mempool mechanism and an adversary. Leveraging this framework, we prove that BST offers predictable and asymptotically optimal delays, predictable fees, and is incentive compatible, thus answering the question posed in the affirmative.
This paper introduces a novel framework for rate discovery in de-centralized finance (DeFi), focusing on the unique challenges andopportunities within decentralized lending platforms. We explorethe mechanisms of interest rate formation in a decentralized en-vironment, free from traditional banking institutions’ control. Byleveraging lending pool dynamics, we propose a method that inte-grates borrowers’ risk profiles with market liquidity conditions todetermine fair borrowing rates without third party involvment. Ourmodel aims to offer a transparent and reliable solution for rate dis-covery in DeFi. Through a series of simulations, we demonstratethe potential of our framework to improve lending practices in theDeFi ecosystem, making it a viable and competitive alternative toconventional financial systems. The findings suggest that our ap-proach not only enhances the transparency and fairness of the lend-ing process but also encourages a more informed participation oflenders and borrowers, ultimately contributing to the stability andgrowth of the DeFi market.
Smart contracts are computer programs running on blockchains to automate the transaction execution between users. The absence of contract specifications poses a real challenge to the correctness verification of smart contracts. Program invariants are properties that are always preserved throughout the execution, which characterize an important aspect of the program behaviors. In this paper, we propose a novel invariant generation framework, INVCON+, for Solidity smart contracts. INVCON+ extends the existing invariant detector, InvCon, to automatically produce verified contract invariants based on both dynamic inference and static verification. Unlike INVCON+, InvCon only produces likely invariants, which have a high probability to hold, yet are still not verified against the contract code. Particularly, INVCON+ is able to infer more expressive invariants that capture richer semantic relations of contract code. We evaluate INVCON+ on 361 ERC20 and 10 ERC721 real-world contracts, as well as common ERC20 vulnerability benchmarks. The experimental results indicate that INVCON+ efficiently produces high-quality invariant specifications, achieving a recall of 80%, which can be used to secure smart contracts from 17 types of common vulnerabilities.
Decentralized Autonomous Organizations (DAOs) are organizations governed by smart contracts and blockchain technology. DAOs can use a constitution to clarify their operations. Despite their increasing prevalence, the contents of DAO constitutions and their role in governing these organizations remain relatively unexplored. This study aims to fill this gap by investigating the contents of existing DAO constitutions. Using a grounded theory approach, we collected a sample of 27 constitutional documents from active DAOs. These documents were systematically coded, resulting in a framework of unified concepts that populate DAO constitutions. Our findings reveal several thematic areas DAO constitutions, including technology, governance, finance, and community. After its creation, expert interviews validated the framework, confirming its relevance and suggesting refinements. This research contributes to a deeper understanding of the governance mechanisms within DAOs and provides a foundational framework for future studies in this area.
Solidity is very useful language programming in modeling investment and Finance exercises blockchain technology. The book covers BA and BS, MA and MS in Risk Management, Business Administration, Financial Services, International Business and Financial Derivatives and BS in Computational Finance. Most importantly, the book includes a range of materials to help the student, the practitioners and the investors to reinforce their learning skills in cryptocurrency programming. The market will be very responsive for our book especially that a lot of international students experience problem with their English and their numerical skills. It will cover the basic needs of postgraduate students and those who are interesting in investment and tokens creation. Readers would be able to understand token contracts. Buyers would be allowed to buy tokens using Ethers. Cryptocurrency companies could raise funds by making use of initial token offering, (ITO), by selling their tokens to other investors. The market in the next five years will become very complicated and would require the use of sophisticated risk management techniques and technological solutions in order to hedge market, operational, and credit risk. Good luck in your future career as crypto developer and investment programmer in the major investment banks such as JP Morgan, Merrill Lynch, Morgan Stanley, Deutsche Bank, Goldman Sachs, and Bank of America. I would like to thank the Schweser Kaplan organization for the professional education that covers the syllabus of the Chartered Financial Institute, (CFA). Parts of the CFA examples are used to calibrate the financial models. Please e-mail me if you have any questions or if you would like to suggest investment exercises. My e-mail is guirguismichel@gmail.com
Krzysztof Gogol, Robin Fritsch, Malte Schlosser, Johnnatan Messias · 6 authors
This paper studies liquid staking tokens (LSTs) on automated market makers (AMMs), both theoretically and empirically. LSTs are tokenized representations of staked assets on proof-of-stake blockchains. First, we model LST-liquidity on AMMs theoretically, categorizing suitable AMM types for LST liquidity and deriving formulas for the necessary returns from trading fees to adequately compensate liquidity providers under the particular price trajectories of LSTs. For the latter, two relevant metrics are considered: (1) losses compared to holding the liquidity outside the AMM (loss-versus-holding, or "impermanent loss"), and (2) the relative profitability compared to fully staking the capital (loss-versus-staking) which is specifically tailored to the case of LST-liquidity. Next, we empirically measure these metrics for Ethereum LSTs across the most relevant AMM pools. We find that, while trading fees often compensate for impermanent loss, fully staking is more profitable for many pools, raising questions about the sustainability of the current LST liquidity allocation to AMMs.
Dongze Li, Kejia Zhang, Shiwei Li, Gang Du · 5 authors
As decentralized finance (DeFi) grows and decentralized exchanges (DEXs) expand, the security of Ethereum smart contracts and blockchain transactions is receiving increasing scholarly attention. The ERC20 token standard has facilitated the emergence of numerous honeypot contracts, which deceive traders by allowing token purchases but blocking withdrawals. This study proposes a lightweight honeypot contract detection system integrated into the go-Ethereum client (Geth). Unlike previous work, our detector does not rely on contract interaction records or source code provided by contract creators. Instead, our approach performs static data flow analysis on contract bytecode to identify honeypot mechanisms. By focusing exclusively on the control flow of the ERC20 Transfer method, our system achieves faster detection than full-contract analysis methods, with an average processing time of 9.74 milliseconds per contract. Experiments on both known honeypot contracts and real-world token contracts demonstrate the effectiveness of our approach in detecting malicious ERC20 contracts.
Burak Öz, Danning Sui, Thomas Thiery, Florian Matthes
The MEV-Boost block auction contributes approximately 90% of all Ethereum blocks. Between October 2023 and March 2024, only three builders produced 80% of them, highlighting the concentration of power within the block builder market. To foster competition and preserve Ethereum's decentralized ethos and censorship-resistance properties, understanding the dominant players' competitive edges is essential. In this paper, we identify features that play a significant role in builders' ability to win blocks and earn profits by conducting a comprehensive empirical analysis of MEV-Boost auctions over a six-month period. We reveal that block market share positively correlates with order flow diversity, while profitability correlates with access to order flow from Exclusive Providers, such as integrated searchers and external providers with exclusivity deals. Additionally, we show a positive correlation between market share and profit margin among the top ten builders, with features such as exclusive signal, non-atomic arbitrages, and Telegram bot flow strongly correlating with both metrics. This highlights a "chicken-and-egg" problem where builders need differentiated order flow to profit, but only receive such flow if they have a significant market share. Overall, this work provides an in-depth analysis of the key features driving the builder market towards centralization and offers valuable insights for designing further iterations of Ethereum block auctions, preserving Ethereum's censorship resistance properties.