Matheus V. X. Ferreira, Daniel J. Moroz, David C. Parkes, Mitchell Stern
In recent years, prominent blockchain systems such as Bitcoin and Ethereum have experienced explosive growth in transaction volume, leading to frequent surges in demand for limited block space and causing transaction fees to fluctuate by orders of magnitude. Existing systems sell space using first-price auctions; however, users find it difficult to estimate how much they need to bid in order to get their transactions accepted onto the chain. If they bid too low, their transactions can have long confirmation times. If they bid too high, they pay larger fees than necessary. In light of these issues, new transaction fee mechanisms have been proposed, most notably EIP-1559, aiming to provide better usability. EIP-1559 is a history-dependent mechanism that relies on block utilization to adjust a base fee. We propose an alternative design - a dynamic posted-price mechanism - which uses not only block utilization but also observable bids from past blocks to compute a posted price for subsequent blocks. We show its potential to reduce price volatility by providing examples for which the prices of EIP-1559 are unstable while the prices of the proposed mechanism are stable. More generally, whenever the demand for the blockchain stabilizes, we ask if our mechanism is able to converge to a stable state. Our main result provides sufficient conditions in a probabilistic setting for which the proposed mechanism is approximately welfare optimal and the prices are stable. Our main technical contribution towards establishing stability is an iterative algorithm that, given oracle access to a Lipschitz continuous and strictly concave function f, converges to a fixed point of f.
Non-fungible token (NFT) trade has grown drastically over recent years. While scholarship on the technical aspects and potential applications of NFTs has been steadily increasing, less attention has been directed to the human perception of or attitudes toward this new type of digital asset. The aim of this research is to investigate what concerns are expressed in relation to non-fungible tokens by those who engage with NFTs on the social media platform Twitter. In this study, data was gathered through online social media data mining of NFT-related posts on Twitter. Two datasets (with 18,373 and 36,354 individual tweet records, respectively) were obtained. Topic modeling was used as a method of data analysis. Our results reveal 19 overall themes of concerns around NFTs as expressed on Twitter, which broadly fall into two categories: concerns about attacks and threats by third parties; and concerns about trading and the role of marketplaces. Overall, this study offers a better understanding of the expressions of concern, uncertainty, and the perception of possible barriers related to NFT trading. These findings contribute to theoretical insight and can, moreover, function as a basis for developing practical design and policy interventions.
Open access
Digital Marketing and Social Media
Consumer Behavior in Brand Consumption and Identification
Aspiring to achieve an accurate Bitcoin price prediction based on people's opinions on Twitter usually requires millions of tweets, using different text mining techniques (preprocessing, tokenization, stemming, stop word removal), and developing a machine learning model to perform the prediction. These attempts lead to the employment of a significant amount of computer power, central processing unit (CPU) utilization, random-access memory (RAM) usage, and time. To address this issue, in this paper, we consider a classification of tweet attributes that effects on price changes and computer resource usage levels while obtaining an accurate price prediction. To classify tweet attributes having a high effect on price movement, we collect all Bitcoin-related tweets posted in a certain period and divide them into four categories based on the following tweet attributes: $(i)$ the number of followers of the tweet poster, $(ii)$ the number of comments on the tweet, $(iii)$ the number of likes, and $(iv)$ the number of retweets. We separately train and test by using the Q-learning model with the above four categorized sets of tweets and find the best accurate prediction among them. Especially, we design several reward functions to improve the prediction accuracy of the Q-leaning. We compare our approach with a classic approach where all Bitcoin-related tweets are used as input data for the model, by analyzing the CPU workloads, RAM usage, memory, time, and prediction accuracy. The results show that tweets posted by users with the most followers have the most influence on a future price, and their utilization leads to spending 80\% less time, 88.8\% less CPU consumption, and 12.5\% more accurate predictions compared with the classic approach.
Cryptographic forms of money are distributed peer-to-peer (P2P) computerized exchange mediums, where the exchanges or records are secured through a protected hash set of secure hash algorithm-256 (SHA-256) and message digest 5 (MD5) calculations. Since their initiation, the prices seem highly volatile and came to their amazing cutoff points during the COVID-19 pandemic. This factor makes them a popular choice for investors with an aim to get higher returns over a short span of time. The colossal high points and low points in digital forms of money costs have drawn in analysts from the scholarly community as well as ventures to foresee their costs. A few machines and deep learning algorithms like gated recurrent unit (GRU), long short-term memory (LSTM), autoregressive integrated moving average with explanatory variable (ARIMAX), and a lot more have been utilized to exactly predict and investigate the elements influencing cryptocurrency prices. The current literature is totally centered around the forecast of digital money costs disregarding its reliance on other cryptographic forms of money. However,Dashcoin is an individual cryptocurrency, but it is derived fromBitcoinandLitecoin. The change inBitcoinandLitecoinprices affects theDashcoin price. Motivated from these, we present a cryptocurrency price prediction framework in this paper. It acknowledges different cryptographic forms of money (which are subject to one another) as information and yields higher accuracy. To illustrate this concept, we have considered a price prediction ofDashcoin through the past days’ prices ofDash,Litecoin, andBitcoinas they have hierarchical dependency among them at the protocol level. We can portray the outcomes that the proposed scheme predicts the prices with low misfortune and high precision. The model can be applied to different digital money cost expectations.
Konstantinos Stylianou, Leonhard Spiegelberg, Maurice Herlihy, Nic Carter
When network products and services become more valuable as their userbase grows (network effects), this tendency can become a major determinant of how they compete with each other in the market and how the market is structured. Network effects are traditionally linked to high market concentration, early-mover advantages, and entry barriers, and in the market they have also been used as a valuation tool. The recent resurgence of Bitcoin has been partly attributed to network effects, too. We study the existence of network effects in six cryptocurrencies from their inception to obtain a high-level overview of the application of network effects in the cryptocurrency market. We show that, contrary to the usual implications of network effects, they do not serve to concentrate the cryptocurrency market, nor do they accord any one cryptocurrency a definitive competitive advantage, nor are they consistent enough to be reliable valuation tools. Therefore, while network effects do occur in cryptocurrency networks, they are not (yet) a defining feature of the cryptocurrency marketas a whole.
Júlia Almeida Valadares, Vinícius Cunha Oliveira, José Eduardo de Azevedo Sousa, Heder S. Bernardino · 7 authors
Ethereum é uma das maiores plataformas de cripto ativos atualmente, e vem se tornando um ambiente de negócios digitais entre usuários. O Ethereum foi concebido para permitir transações descentralizadas entre usuários anônimos. Contudo, o desenvolvimento de métodos para identificar perfis de comportamentos de usuários, mantendo suas identidades anônimas, têm o potencial para alavancar negócios nessa plataforma. Nesse trabalho, investigamos o uso de aprendizado de máquina para classificar um perfil de usuário como profissional ou comum a partir de atributos de suas transações. Essa classificação é desafiadora devido à pequena fração de usuários publicamente rotulados no Ethereum e ainda a fração consideravelmente menor de usuários profissionais. Para conduzir essa investigação, treinamos modelos considerando conjuntos cuidadosamente balanceados de transações com usuários rotulados. Nossos resultados mostram modelos de alto desempenho para a classificação de perfis, alcançando desempenho superior a 90% para acurácia, precisão, revocação e demais medidas relacionadas. Adicionalmente, identificamos as características mais relevantes em transações para essa classificação.
In the ongoing march of industrial evolution there comes along technologies that have the power to transform businesses as we know it. The Internet revolutionized business/marketing in the nineties and today and bockchain has the potential to do the same for commercial transactions. Blockchain is a peer-to-peer model that can speed up processes resulting in robust tracking and reducing costs of transactions. Given the implications of this far-reaching technology, it is essential for marketing students to understand its significance in the conduct of marketing activities. Consequently, it is imperative that faculty comprehend aspects of the technology and its applications in marketing to integrate them in marketing courses. The purpose of the paper is to enlighten marketing faculty on the concept of blockchain in a non-technical manner along with highlighting its applications in marketing that can be imparted through their courses. To do so, the paper is structured around three research questions relating to (a) understanding the idea of blockchain technology; (b) expounding on the importance of the technology in marketing applications; and (c) illustrating how marketing faculty can integrate the concepts and application in marketing courses.
<strong>The most accepted cryptographic money is the bitcoin, which is highly attracting the traders and investors for making buy or sell decisions. However, the prediction of the bitcoin prices is challenging due to its higher voltality. In this work, a new bitcoin prediction model is introduced with three major phases: Pre-processing, Feature Extraction and Prediction. The collected bit coin data corresponding to minute-by-minute and hour-by-hour data is subjected to pre-processing. From the pre-processed data, the original features are extracted along with the features based on technical indicators. Average True Range (ATR), Exponential Moving Average (EMA) and Relative Strength Index (RSI) are the technical indicators computed. All the extracted features are subjected to prediction phase, where the optimized Neural Network (NN) model is used. To make the prediction more accurate, the training of NN is carried out by the renowned Elephant Herding Optimization (EHO) via tuning the weight. Finally, the algorithmic analysis is carried out by varying the window size.</strong>
The tick rule is one of the most popular trade classification algorithms used when an order initiator in market data is not signed. Using 11.9 million trades of Bitcoin/USD on Bitstamp, this article tests the accuracy of the tick rule in the Bitcoin market. Evidence indicates that the overall success rate of the tick rule is 76.87%. It is also shown that the tick rule is inclined to fail in discerning trade intentions when there is a long period of time between trades. Furthermore, order imbalances computed using the tick rule lack sufficient accuracy in the Bitcoin market.
Data sharing between upstream and downstream entities is vital for the success of a supply chain. However, distrust, privacy concerns, data misuse, and the asymmetric valuation of shared data between entities often hinder data sharing. This problem calls for a secure, efficient, fair, and trustworthy data‐sharing mechanism. The key to such a successful system hinges on how to trace the data usage, determine the value of the seller’s data to the buyer and then compensate the seller accordingly. To this end, we design and implement a blockchain‐enabled data‐sharing marketplace for a stylized supply chain. We demonstrate how a blockchain can be used to overcome these impediments in supply‐chain data sharing and provide a detailed tutorial with a step‐by‐step implementation for how to set up such a data exchange prototype using Hashgraph.
In the e-commerce market, many e-commerce platforms act as resellers when selling products, and act as agents when selling other products. In the sales process, e-commerce platforms can either build their own blockchain anti-counterfeit traceability platforms or cooperate with third-party blockchain anti-counterfeit traceability platforms. This will generate four scenarios: (a) reseller, building its own platform (RE); (b) reseller, cooperating with a third-party platform (RO); (c) agent, building its own platform (ME); (d) agent, cooperating with a third-party platform (MO). Therefore, this paper constructs a differential game model under four modes to explore the interaction between the choice of sales mode and the choice of anti-counterfeit traceability service strategy. The results show that suppliers’ profits are influenced by various aspects. On the one hand, in small-scale markets, the situation in which suppliers can realize higher profits evolves from ME to RO as the wholesale price increases, and in large-scale markets, suppliers are more profitable in the ME mode. On the other hand, with the increase of market scale and the decrease of unit price of anti-counterfeit traceability service of third-party platform, the situation that suppliers can achieve higher profit evolves from RE to RO and then to RE. For e-commerce platform, self-built platform is a better choice. In the small-scale market, as the market size increases, the cost performance of anti-counterfeit traceability service decreases, and the best choice for e-commerce platform evolves from resale to agency sales, and in the large-scale market, the best choice for e-commerce platform is resale.
Abstract The development of blockchain-based applications, to date mostly virtual currencies, touches many areas of law and economics. The most well-known applications of public blockchains rely on Proof of Work as a consensus mechanism in which miners compete to solve a cryptographic puzzle. We argue that economic tools for market definition may be adapted to delineate relevant cryptocurrency mining markets. Antitrust law can help to prevent network attacks and exclusion of transactions with lower fees by large miners. When multiple blockchains are part of the same market, the role of network effects in securing the leading position of more established cryptocurrencies can potentially lead to exclusionary behaviour.
N. Bora Keskin, Chenghuai Li, Jing-Sheng Jeannette Song
Motivated by blockchain applications in the fresh produce industry, we consider a newsvendor problem in which a retailer faces stochastic and freshness-dependent consumer demand. The retailer can adopt blockchain technology to have more transparent information on the freshness of supply. We quantify the value of blockchain-enabled freshness transparency by deriving closed-form expressions for the retailer’s expected profit growth and food waste reduction brought by blockchain adoption. Using publicly available data, we provide a numerical example illustrating that for Walmart’s strawberry business in the United States (which is about only 4% of Walmart’s fresh produce sales), blockchain can increase annual profit by [Formula: see text] million while eliminating 23 million pounds of food waste annually through operational improvements. Despite this substantial value for the retailer, blockchain adoption can decrease the expected profit of the retailer’s supplier. We design a family of threshold-type smart contracts contingent on a blockchain-based freshness consensus and examine when such contracts offer a win-win proposition to the retailer and the supplier. Moreover, when the retailer offers freshness-based price discounts, we find that less fresh supply leads to less food waste. In contrast, when the supplier adjusts the wholesale price based on freshness, less fresh supply causes more food waste. We also generalize our findings to the cases of (i) dual sourcing, (ii) noisy measurements in the Internet of Things sensors feeding data into blockchain, and (iii) the retailer’s culling processes. This paper was accepted by David Simchi-Levi, operations management. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.02949 .