Transaction Fees Research Articles

Mean-entropy uncertain portfolio with risk curve and total mental accounts under multiple background risks

In the previous uncertain portfolio literature on background risk and mental account, only a general background risk and a few kinds of mental accounts were considered. Based on the above limitations, on the one hand, the multiple background risks are defined by linear weighting of different background asset risks in this paper; on the other hand, the total nine kinds of mental accounts are comprehensively considered. Especially, the risk curve is regarded as the risk measurement of different mental accounts for the first time. Under the framework of uncertainty theory, a novel mean-entropy portfolio model with risk curve and total mental accounts under multiple background risks is constructed. In addition, transaction fees, chance constraint, upper and lower limits and initial wealth constraints are also considered in our proposed model. In theory, the equivalent forms of the models with different uncertainty distributions (general, normal and zigzag) are presented by three theorems. Simultaneously, the corresponding concrete expressions of risk curves are obtained by another three theorems. In practice, two numerical examples verify the feasibility and effectiveness of our proposed model. Finally, we can obtain the following unique and meaningful findings: (1) investors will underestimate the potential risk if they ignore the existence of multiple background risks; (2) with the increase of the return threshold, the return of the sub-portfolio will inevitably increase, but investors also bear the risk that the risk curve is higher than the confidence curve at this time.

Reinforcement Learning with Self-Attention Networks for Cryptocurrency Trading

This work presents an application of self-attention networks for cryptocurrency trading. Cryptocurrencies are extremely volatile and unpredictable. Thus, cryptocurrency trading is challenging and involves higher risks than trading traditional financial assets such as stocks. To overcome the aforementioned problems, we propose a deep reinforcement learning (DRL) approach for cryptocurrency trading. The proposed trading system contains a self-attention network trained using an actor-critic DRL algorithm. Cryptocurrency markets contain hundreds of assets, allowing greater investment diversification, which can be accomplished if all the assets are analyzed against one another. Self-attention networks are suitable for dealing with the problem because the attention mechanism can process long sequences of data and focus on the most relevant parts of the inputs. Transaction fees are also considered in formulating the studied problem. Systems that perform trades in high frequencies cannot overlook this issue, since, after many trades, small fees can add up to significant expenses. To validate the proposed approach, a DRL environment is built using data from an important cryptocurrency market. We test our method against a state-of-the-art baseline in two different experiments. The experimental results show the proposed approach can obtain higher daily profits and has several advantages over existing methods.

Blockchain Technology, Its Applications and Open Research Challenges

Blockchain is a technology that operates on distributed peer-to-peer network and is widely adopted by large number of organizations due to its attractive features such as highly secured, transparent, traceable, low transaction fee, no need of central authority and confidentiality. There are lot of confusions and myths regarding the blockchain technology as it is emerging technology and too complex in nature. This paper is aimed at revealing blockchain technology in depth along with its applications and open research challenges. The past published literature was reviewed to achieve the objectives. The study found that apart from cryptocurrency, blockchain is also suitable for other applications such as smart cities, supply chain, healthcare, smart transportation and authenticating IoT (Internet of Things) devices. This study is also disclosing various open research challenges such as scalability issues, huge power consumption and delay in completing transactions. This paper is also sharing the mitigation approaches and the future of blockchain with smart contracts.

The Quality Control System of Green Composite Wind Turbine Blade Supply Chain Based on Blockchain Technology

Nowadays, blockchain technology is expected to promote the quality control of traditional industry due to its traceability, transparency and non-tampering characteristics. Although blockchain could offer the traditional industry new energy, there are still some predictable difficulties in the early stage of its application, such as the structure of the blockchain-based system, the role of regulators in the system and high transaction fee by block packing. In this paper, we establish a pioneering quality control system for the green composite wind turbine blade supply chain based on blockchain technology. Firstly, the framework of this system is proposed to ensure that the quality of the product could not only be examined and verified by regulator, but also be monitored by other related nodes. Next, we develop a new way to store the data by hash fingerprint and the cost of transaction fees is significantly reduced in the case of a large amount of data. Then, the information on-chain method is developed to realize the data traceability of each node. At last, the tests of this system are carried out to prove its validity, the satisfactory results are obtained and information supervision and sharing role of the regulators are discussed.

3-of-3 multisignature approach for enabling lightning network micro-payments on IoT devices

Bitcoin's success as a cryptocurrency enabled it to penetrate into many daily life transactions. Its problems regarding the transaction fees and long validation times are addressed through an innovative concept called the Lightning Network (LN) which works on top of Bitcoin by leveraging off-chain transactions. This made Bitcoin an attractive micropayment solution that can also be used within certain IoT applications (e.g., toll payments) since it eliminates the need for traditional centralized payment systems. Nevertheless, it is not possible to run LN and Bitcoin on resource-constrained IoT devices due to their storage, memory, and processing requirements. Therefore, in this paper, we propose an efficient and secure protocol that enables an IoT device to use LN's functions through a gateway LN node even if it is not trusted. The idea is to involve the IoT device only in signing operations, which is possible by replacing LN's original 2-of-2 multisignature channels with 3-of-3 multisignature channels. Once the gateway is delegated to open a channel for the IoT device in a secure manner, our protocol enforces the gateway to request the IoT device's cryptographic signature for all further operations on the channel such as sending payments or closing the channel. LN's Bitcoin transactions are revised to incorporate the 3-of-3 multisignature channels. In addition, we propose other changes to protect the IoT device's funds from getting stolen in possible revoked state broadcast attempts. We evaluated the proposed protocol using a Raspberry Pi considering a toll payment scenario. Our results show that timely payments can be sent and the computational and communication delays associated with the protocol are negligible.

Determining the potential of informal savings groups as a model for formal commitment saving devices

Background: Saving behaviour has attracted research attention over the past 20 years. Typically, individual and household saving rates among low-income groups are inadequate. Research suggests that informal savings groups are effective vehicles for encouraging saving among low-income individuals. Yet little is known about the drivers of positive saving behaviour among informal savings groups, which makes it difficult for formal providers to design interventions that promote higher levels of saving.Aim: This study aimed to explore both the rational and non-rational drivers of saving behaviour among low-income members of informal savings groups, the attributes of informal savings groups that positively influence their collective saving behaviour, and to identify the valued features of savings groups that encourage the adoption of informal commitment saving devices (CSDs).Methods: The study was informed by a literature review followed by field research in which semi-structured interviews were conducted with 10 savings groups and 10 individual members of savings groups. The participants’ perspectives were analysed and compared within the context of behavioural economic theory.Results: The study revealed seven characteristics of informal savings groups that potentially serve as interventions to explain non-rational saving behaviour. It also identified seven features valued by users of informal CSDs (including flexibility, restricted access to savings and no transaction fees) which could be salient to providers of formal CSDs.Conclusion: On the basis of the findings, a behavioural design framework was proposed to inform the design features of formal CSDs that may ensure customer retention and improved saving outcomes.

GasChecker: Scalable Analysis for Discovering Gas-Inefficient Smart Contracts

Ethereum, the largest blockchain for running smart contracts, charges the people who send transactions to deploy or invoke smart contracts for thwarting resource abuse. The amount of transaction fee depends on the size of that contract and the operations executed by that contract. Consequently, smart contracts with inefficient code will waste money. In this article, we propose and develop the first tool, named <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GasChecker</monospace> , for automatically identifying gas-inefficient code in smart contracts, and conduct the first empirical study on the prevalence of gas-inefficient code in the deployed smart contracts. More precisely, we first summarize ten gas-inefficient programming patterns and propose a new approach based on symbolic execution (SE) to detect them in the bytecode of smart contracts. To make our approach scalable to analyze millions of smart contracts, we parallelize SE by tailoring it to the MapReduce programming model, and propose a new feedback-based load balancing strategy to effectively utilize cloud resources. Extensive experiments show that <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GasChecker</monospace> scales well with the increase of workers. The empirical study demonstrates that lots of real smart contracts contain various inefficient code. Manual investigation demonstrates that only 2.5 percent of discovered gas-inefficient instances are false positives.

Payments on digital platforms: Resiliency, interoperability and welfare

Digital platforms, such as Alibaba and Amazon, operate an online marketplace to facilitate transactions. This paper studies a platform’s business model choice between accepting cash and issuing tokens, as well as the implications for welfare, resiliency, and interoperability. A cash platform free rides on the existing payment infrastructure and profits from collecting transaction fees. A token platform earns seigniorage, albeit bearing the costs of setting up the system and holding reserves to mitigate the cyber risk. Tokens earn consumers a return, insulating transactions from the liquidity costs of using cash, but also expose them to the remaining cyber risk. The platform issues tokens if the interest rate is high, the platform scope is large, and the cyber risk is small. Unbacked floating tokens with zero transaction fees or interest-bearing stablecoins can implement the equilibrium business model, which is not necessarily socially optimal because the platform does not internalize its impacts on off-platform activities. The model explains why Amazon does not issue tokens, but Alipay issues tokens circulatable outside its Alibaba platforms. Regulations such as a minimum reserve requirement can reduce welfare.

Incentives for Ridesharing: A Case Study of Welfare and Traffic Congestion

Traffic congestion is largely due to the high proportion of solo drivers during peak hours. Ridesharing, in the sense of carpooling, has emerged as a travel mode with the potential to reduce congestion by increasing the average vehicle occupancy rates and reduce the number of vehicles during commuting periods. In this study, we propose a simulation-based optimization framework to explore the potential of subsidizing ridesharing users, drivers, and riders, so as to improve social welfare and reduce congestion. We focus our attention on a realistic case study representative of the morning commute on Sydney’s M4 Motorway in Australia. We synthesize a network model and travel demand data from open data sources and use a multinomial logistic model to capture users’ preferences across different travel roles, including solo drivers, ridesharing drivers, ridesharing passengers, and a reserve option that does not contribute to congestion on the freeway network. We use a link transmission model to simulate traffic congestion on the freeway network and embed a fixed-point algorithm to equilibrate users’ mode choice in the long run within the proposed simulation-based optimization framework. Our numerical results reveal that ridesharing incentives have the potential to improve social welfare and reduce congestion. However, we find that providing too many subsidies to ridesharing users may increase congestion levels and thus be counterproductive from a system performance standpoint. We also investigate the impact of transaction fees to a third-party ridesharing platform on social welfare and traffic congestion. We observe that increasing the transaction fee for ridesharing passengers may help in mitigating congestion effects while improving social welfare in the system.

Stability of Transaction Fees in Bitcoin: A Supply and Demand Perspective

Cryptocurrencies such as Bitcoin are breakthrough financial technologies that promise to revolutionize the digital economy. Unfortunately, their long-term adoption in the business world is imperiled by a lack of stability that manifests as dramatic swings in transaction fees and severe participant dissatisfaction. To date, there has been little academic effort to study how system participants react to volatility in fee movements. Our study addresses this research gap by conceptualizing the Bitcoin platform as a data space market and studying how market equilibrium forms between users who demand data space while trying to avoid transaction delays, and miners who supply data space while trying to maximize fee revenues. Our empirical analysis based on past bitcoin transactions reveals the existence of a relatively flat downward-sloping demand curve and a much steeper upward-sloping supply curve. Regarding users, the inelastic nature of demand signals the utility of Bitcoin as a niche platform for transactions that are otherwise difficult to conduct. This result challenges the belief that users may easily abandon Bitcoin technology given rising transaction costs. We also find that the use of bitcoins as a trading asset is associated with higher levels of tolerance to fees. Regarding miners, the comparatively elastic nature of supply indicates that higher fees stimulate mining by a larger magnitude than suppressing demand. This finding implies that, ceteris paribus, the Bitcoin system turns to self-regulate transaction fees in an efficient manner. Our work has implications for the management of congestion in blockchain-based systems and more broadly for the stability of cryptocurrency markets.

Transaction fee mechanism design

Demand for blockchains such as Bitcoin and Ethereum is far larger than supply, necessitating a mechanism that selects a subset of transactions to include "on-chain" from the pool of all pending transactions. EIP-1559 is a proposal to make several tightly coupled changes to the Ethereum blockchain's transaction fee mechanism, including the introduction of variable-size blocks and a burned base fee that rises and falls with demand. These changes are slated for deployment in Ethereum's "London fork," scheduled for late summer 2021, at which point it will be the biggest economic change made to a major blockchain to date. This short note provides an overview of recent work by the author that formally investigates and compares the incentive guarantees offered by Ethereum's current transaction fee mechanism and the new mechanism proposed in EIP-1559.

Blockchain competition: The tradeoff between platform stability and efficiency

Since 2017, the Bitcoin blockchain system has experienced 105 fork divergences. The rapidly increasing blockchain forks have resulted in fierce competition and created significant controversies in blockchain community. To analyze this competitive aspect, we consider blockchain as a two-sided platform that serves both customers and miners. We develop a game-theoretic model to investigate how a blockchain platform's decision on its settings, such as block size and transaction fee, affects the competition between blockchain platforms as well as the participation behavior of customers and miners. Our findings suggest that increasing the transaction fee alleviates congestion on the platform when customers have a relatively balanced need between efficiency and safety. In contrast, it induces congestion when efficiency is valued over safety. In addition, under hard fork competition, the difference in blockchain platforms' block sizes directs the attention of miners towards different types of mining rewards. Moreover, it also affects the optimal types of customers the blockchain platforms should target. Furthermore, we find that the degree of congestion and the risk attitudes of participants play vital roles in differentiating different block-sized platforms' optimal transaction fees. We provide empirical evidence on the theoretical findings and practical implications for blockchain platform competition with respect to the behaviors of platform participants.

Direct selling, agent selling, or dual-format selling: Electronic channel configuration considering channel competition and platform service

Flourishing e-commerce has prompted manufacturers to design different electronic channel structures for consumers' electronic shopping needs. As a result, the study of e-commerce channel configuration decision has become an important research topic. Toward this end, we establish three-stage sequential game models and investigate a manufacturer’s electronic channel configuration strategy from the perspective of both the manufacturer and the whole supply chain. In addition to a traditional channel, the main innovation of this paper is the framework involving three different electronic channel structures: the first is direct selling, in which the manufacturer sells products directly to consumers; the second is agent selling, in which the manufacturer sells products through a third-party platform; and the third is dual-format selling, in which the manufacturer sells products through both direct selling and agent selling. In these frameworks, the manufacturer’s selling price and the platform’s transaction fee are obtained by backward induction. Another innovation is the simultaneous incorporation of key factors such as the cross-channel effect, channel competition and platform service into our discussion. Sensitivity analysis is carried out and numerical examples are given to reveal the impact of exogenous parameters on equilibrium outcomes and channel configuration strategies both theoretically and numerically. Our analysis shows that channel competition intensity and transaction fee impact factor are critical to determining channel configuration. When these two factors are both either large or small, dual-format selling is the optimal configuration; otherwise, agent selling is preferred. In addition, we find that an electronic channel consisting solely of direct selling will never be chosen. In the extended models, we also find that our main results are robust for cases of positive entry cost, endogenous pricing, and demand-driven data value.

Ransomware activity and blockchain congestion

I examine blockchain congestion episodes caused by more than 4,400 triggers for ransomware attacks over a two-year period. When demand for settlement exceeds blockchain capacity, blockchain users engage in fee competition to prioritize their transaction settlement. A typical surge in ransomware activity causes transaction fees to increase by 2.1% and up to 28% in extreme cases. Consistent with theory literature, some users forgo blockchain settlement when transaction fees increase. An event study around an extreme spike in ransomware activity supports the findings of the main analysis.

Mining

In the context of blockchain networks, mining describes a permissionless process intended to ensure the global consistency of a decentralised ledger. Mining requires the consumption of a costly computational resource to participate in a probabilistic competition that confers specific privileges to a node. These privileges typically relate to the proposal of a new block, including the identity and order of transactions contained within. Mining is incentivised via an algorithmically regulated provision of rewards, usually in the form of newly generated coins and/or transaction fees.

Formula omitted]-HTLC: Neo hashed time-Lock commitment to defend against wormhole attack in payment channel networks

In today’s cryptocurrency, the Payment Channel Network (PCN) is noticed as one of the most gifted off-chain solutions for scalability issues. Besides this, it consumes lesser transaction fees and low transaction confirmation time. However, security and privacy issues need to be addressed appropriately to make the solution even more effective. Most of the existing HTLC (Hashed Time-Lock Contract) protocols revealed the sender’s information to the intermediate users in the payment route. In this work, we propose an effective secure and privacy-preserving Payment Channel Network protocol, named Neo Hashed Time-Lock Commitment (n-HTLC) protocol. (n-HTLC) does not require the sender to send any information to each intermediate user along the payment route, thus preserves the identity of the sender. But, (n-HTLC) is not compatible with Sphinx onion packet format. Therefore, a symmetric key encryption-based protocol called kTLC has been proposed. kTLC is compatible with the Sphinx onion packet format, which is used in the current Lightning network atop of the Bitcoin blockchain network. The security of both n-HTLC and kTLC are proved using the Universal Composability (UC) framework. It is observed that both ensure that no attacker can extract information on the payment route if at least one of the users in the path is honest. To analyze the performance of both n-HTLC and kTLC payment protocol, we conduct experiments using the snapshots of Ripple network11According to the network snapshots (Dec 2015) Malavolta et al. (2017a) pre-processed by Mazumdar and Ruj (2020)., Lightning network22According to the network snapshots (May 2020) Lns pre-processed by Mazumdar and Ruj (2020)., and synthetic network of Mazumdar and Ruj (2020). Our experimental results show that both n-HTLC and kTLC outperform state-of-the-art off-chain payment protocols in terms of computational and communication overhead.

Multi-agent reinforcement learning approach for hedging portfolio problem.

Developing a hedging strategy to reduce risk of losses for a given set of stocks in a portfolio is a difficult task due to cost of the hedge. In Vietnam stock market, cross-hedge is involved hedging a long position of a stock because there is no put option for the stock. In addition, only VN30 stock index futures contracts are traded on Hanoi Stock Exchange. Inspired by recently achievement of deep reinforcement learning, we explore feasibility to construct a hedging strategy automatically by leveraging cooperative multi-agent in reinforcement learning techniques without advanced domain knowledge. In this work, we use 10 popular stocks on Ho Chi Minh Stock Exchange, and VN30F1M (VN30 Index Futures contracts within one month settlement) to develop a stock market simulator (including transaction fee, tax, and settlement date of transactions) for reinforcement learning agent training. We use daily return as input data for training process. Results suggest that the agent can learn trading and hedging policy to make profit and reduce losses. Furthermore, we also find that our agent can protect portfolios and make positive profit in case market collapses systematically. In practice, this work can help Vietnam’s stock market investors to improve performance and reduce losses in trading, especially when the volatility cannot be controlled.

Throughput-Efficient Lagrange Coded Private Blockchain for Secured IoT Systems

We develop a new Lagrange coded blockchain model for Internet-of-Things (IoT) systems based on Lagrange coded computing (LCC). In the model, a mining task assigned to a blockchain node (BN) is encoded with a specific encoding function. Thus, the final result, i.e., newly generated block or block verification result, can be decoded even when only some mining outputs returned by BNs are correct, while other outputs are erroneous or discarded due to delays. To be decoded correctly, the number of mining outputs returned prior to decoding must be at least a given decoding threshold. Then, security against malicious BNs and resilience against stragglers can be guaranteed if the number of mining tasks allocated to BNs is not less than the sum of decoding threshold, number of stragglers, and double of the number of malicious BNs. Unlike other IoT blockchains and LCC-based methods showing enhanced throughput but yielding poor security, our model can improve throughput without compromising on security. This is achieved through optimized load allocations when the higher loads (two or more mining tasks) are allocated to the fastest BNs leading to: 1) increased number of mining outputs returned prior to decoding required to meet the decoding threshold and 2) increased number of allocated mining tasks to strengthen security and resilience. To overcome the limitation of our model related to higher loads and, hence, higher mining costs to BNs, we develop a contract-theoretic mechanism that incentivizes each BN to complete its mining task through joint load and transaction fee allocations.

RobustPay+: Robust Payment Routing With Approximation Guarantee in Blockchain-Based Payment Channel Networks

The past decade has witnessed an explosive growth in cryptocurrencies, but the blockchain-based cryptocurrencies have also raised many concerns, among which a crucial one is the scalability issue. Suffering from the large overhead of global consensus and security assurance, even the leading cryptocurrencies can only handle up to tens of transactions per second, which largely limits their applications in real-world scenarios. Among many proposals to improve the cryptocurrency scalability, one of the most promising and mature solutions is the payment channel network (PCN), which offers the off-chain settlement of transactions with minimal involvement of expensive blockchain operations. However, transaction failures may occur due to external attacks or unexpected conditions, e.g., an uncooperative user becoming unresponsive. In this paper, we present a distributed robust payment routing protocol RobustPay <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> to resist transaction failures, which achieves robustness, efficiency, distributedness and approximate optimization. Specifically, we investigate the problem of robust routing in PCNs from an optimization perspective, which is to find a pair of payment paths for a payment request, while minimizing the worst-case transaction fee, subject to the timeliness and feasibility constraints. We present a distributed 2-approximation algorithm for this problem. Moreover, we modify the original Hashed Time-lock Contract (HTLC) protocol and adapt it to the robust payment routing protocol to achieve robustness and efficiency. Extensive simulations demonstrate that RobustPay <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> significantly outperforms baseline algorithms in terms of the success ratio and the average accepted value.

A New Method for Peer Matching and Negotiation of Prosumers in Peer-to-Peer Energy Markets

This article presents a scalable mechanism for peer-to-peer (P2P) energy trading among prosumers in a smart grid. In the proposed mechanism, prosumers engage in a non-mediated negotiation with their peers to reach an agreement on the price and quantity of energy to be exchanged. Instead of concurrent bilateral negotiation between all peers with high overheads, an iterative peer matching process is employed to match peers for bilateral negotiation. The proposed negotiation algorithm enables prosumers to come to an agreement, given that they have no prior knowledge about the preference structure of their trading partners. A greediness factor is introduced to model the selfish behavior of prosumers in the negotiation process and to investigate its impact on the negotiation outcome. In order to recover the costs related to power losses, a transaction fee is applied to each transaction that enables the grid operator to recover incurred losses due to P2P trades. The case studies demonstrate that the proposed mechanism discourages greedy behavior of prosumers in the negotiation process as it does not increase their economic surplus. Also, it has an appropriate performance from the computation overheads and scalability perspectives.