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.
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 .
As the most successful application of the sharing economy, ride-hailing service is popular worldwide and serves millions of users per day worldwide. Ride-hailing service providers (SPs) usually collect users’ personal data to improve their services via big data technologies. However, SPs may also use the collected user data to apply personalized prices to different users, which raises price fairness concerns. In this paper, we propose a smart price auditing system named Spas. Spas allows a user to purchaseFair Price Insurancein the form ofPrice Auditing Contract, then the price of ride-hailing service (RHS) order will be audited automatically once completed. According to the auditing result, the contract punishes misbehaving SPs and also compensates affected users automatically. By replacing an untrustworthy centralized auditor with carefully designed smart contracts, we construct a decentralized price auditing system which is trustworthy and transparent. We demonstrate a theoretical model for practical payment flows based on real RHS user data and we implement Spas in Hyperledger Fabric to show that decentralizing and automating price auditing for RHS with financial incentives is technically feasible.
Guido Felipe Valderrama Herrera, Leonardo Garcia Sanchez
Este trabajo analiza la relación que tienen las ventas en corto, los sesgos de los agentes junto la interacción de los fundamentales de oferta y demanda respecto la formación de precios en el mercado del Bitcoin. Dentro del análisis encontramos que las tendencias aportadas en la literatura se mantienen en cuanto a las ventas en corto y sus restricciones a pesar de no encontrar evidencia estadística.
Hans‐Peter Burghof, Achim Fecker, Patrick Jaquart, Benedikt Notheisen
The 5th European Retail Investment Conference was hosted at Börse Stuttgart, Germany, from April 10th to 12th 2019. The conference chairs invited academics and practitioners to participate and discuss empirical and theoretical research focusing on retail investor products and services, the impact of technology on retail investors, investors’ decision-making, investor protection schemes, and market microstructure. Albert Menkveld, Professor of Finance at Vrije Universiteit Amsterdam and Fellow at the Tinbergen Institute, held the keynote about the fundamental value of bitcoin.
Purpose The authors develop new quantitative methods to estimate the level of speculation and long-term sustainability of Bitcoin and Blockchain. Design/methodology/approach The authors explore the practical application of speculative bubble models to cryptocurrencies. They then show how the approach can be extended to provide estimated brand values using data from Google Trends. Findings The authors confirm previous findings of speculative bubbles in cryptocurrency markets. Relatedly, Google searches for cryptocurrencies seem to be primarily driven by recent price rises. Overall results are sufficient to question the long-term sustainability of Bitcoin with the suggestion that Ethereum, Bitcoin Cash and Ripple may all enjoy technical advantages relative to Bitcoin. Our results also demonstrate that Blockchain has a distinct value and identity beyond cryptocurrencies – providing foundational support for the second generation of academic work on Blockchain. However, a relatively low estimated long-term growth rate suggests that the benefits of Blockchain may take a long time to be fully realised. Originality/value The authors contribute to an emerging academic literature on Blockchain and to a more established literature exploring the use of Google data within business analytics. Their original contribution is to quantify the business value of Blockchain and related technologies using Google Trends
Bitcoin is the first and most successful Blockchain system so far. In the Bitcoin system, miners use transaction attached fees as a driving force to mine a new block and package transactions, while users compete by bidding transaction fees for faster confirmation. Considering the particularity of Bitcoin trading system, we take time series into consideration to analyze the transaction rules of Bitcoin system from the perspective of multiple cycles and establish a dynamic game model related to time under Generalised Second Price(GSP) mechanism, and also confirm the model's superiority on saving users' fees, compared with the static game model. Also, we propose the quantification of the user experience quantified by calculating the price difference between the transactions uploaded by the same user within adjacent times, making the transaction process of the Bitcoin system no longer the final say of the transaction price. The dynamic game model shows that there is a perfect Bayesian game equilibrium solution in the payment decision, so there is no incentive for users to change the attached fee, and the whole system is maintained stably. In addition, we verify the dynamic game model from computational experiment. Firstly, it is proved that with the help of revenue discount, the cost saving of the dynamic model is generally higher than that of the static model. Then the user's revenue under the dynamic model is showing an upward trend, and the transactions order under the dynamic model is more stable than that under static model, which can be illustrated mathematically and computationally that the proposed dynamic game model in this paper will help all transactions be processed more efficiently in a uniform pipeline.
Garud Iyengar, Fahad Saleh, Jay Sethuraman, Wenjun Wang
We construct an economic framework for understanding the incentives of the participants of a permissioned blockchain for supply chains and other related industries. Our study aims to determine whether adoption of blockchain is socially beneficial and whether such adoption arises in equilibrium. We find that blockchain reduces information asymmetry for consumers, thereby enhancing consumer welfare. Consumer welfare gains can be sufficiently large that blockchain adoption is socially beneficial; nonetheless, we find that blockchain adoption does not arise in equilibrium. This situation arises because blockchain adoption costs are borne by manufacturers, and manufacturers cannot extract consumer gains through prices due to the competitive nature of the manufacturing sector. We offer a system of transfers to generate blockchain adoption in equilibrium when it is socially beneficial. This paper was accepted by Vishal Gaur, operations management. Funding: This research was partially supported by a seed grant from the Columbia–IBM Center for Blockchain and Data Transparency. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4532 .
Zoltán Bán, Jan Lánský, Stanislava Mildeová, Petr Tesar
Cryptocurrencies have become a major phenomenon in recent years. For IT, a breakthrough is both the cryptocurrency itself as a commodity and the technology that cryptocurrency development has brought. The article focuses on the bitcoin cryptocurrency as the most important cryptocurrency. A relatively unexplored topic is what goods or services are purchased for bitcoins. To track what bitcoins are spent on, it is necessary to look for places that are dedicated to trading cryptocurrencies. The bitcointalk.org forum was chosen as a source for our data mining. The aim of the article is to find an answer to the research question: What are bitcoins on the discussion forum bitcointalk.org planned to be spent on? As part of the research, an application was developed using a PHP script to gather information from the discussion forum (bitcointalk.org). There is some evidence which suggests what types of products or services people spend cryptocurrencies on. This research has proven that cryptocurrencies are used to buy and sell goods or services in the electronics and computer world segments. Today, these segments are widespread, which may speed up the integration of cryptocurrencies into everyday life. This applies, of course, only if the risks associated with cryptocurrencies do not increase.
Liudmila Zavolokina, Gianluca Miscione, Gerhard Schwabe
The second-hand automotive market is one with the least trust from consumers. Customers on the second-hand car market suffer from such problems as the car being in worse condition than initially indicated, accident damage that is not disclosed, fraud, etc. Akerlof, described the market for used cars as an example of the problem of information asymmetries and resulting quality uncertainty. In order to cope with quality uncertainties, used car buyers actively engage themselves in information seeking. Blockchain technology promises to automatize the tracking of cars through their lifecycles and provide reliable information at any point in time it is needed. In our study, we investigate the problems car buyers face during information seeking and propose requirements for the design of a blockchain-based system to address these.
This paper sets forth a framework for deep reinforcement learning as applied to market making (DRLMM) for cryptocurrencies. Two advanced policy gradient-based algorithms were selected as agents to interact with an environment that represents the observation space through limit order book data, and order flow arrival statistics. Within the experiment, a forward-feed neural network is used as the function approximator and two reward functions are compared. The performance of each combination of agent and reward function is evaluated by daily and average trade returns. Using this DRLMM framework, this paper demonstrates the effectiveness of deep reinforcement learning in solving stochastic inventory control challenges market makers face.
Oliver James Scholten, Nathan Hughes, Sebastian Deterding, Anders Drachen · 6 authors
Ethereum crypto-games are a booming and relatively unexplored area of the games industry. While there is no consensus definition yet, 'crypto-games' commonly denotes games that store tokens, e.g. in-game items, on a distributed ledger atop a cryptocurrency network. This enables the trading of game items for cryptocurrency, which can then be exchanged for regular currency. Together with their chance-based mechanics, this makes crypto-games part of the recent convergence of digital gaming and gambling. In a first effort to scope the field, this paper surveys popular crypto-games, which use the Ethereum cryptocurrency, to tease out characteristic technical properties and gameplay. It then compares the games' features with criteria found in current legal and psychological definitions of gambling. We find that the popular crypto-games selected meet a combined legal and psychological definition of gambling, and conclude with ramifications for future research.
Domhnall Carlin, Jonah Burgess, Philip O’Kane, Sakir Sezer
Traditional malicious attacks have evolved beyond file-based methods, with malicious files now existing as processes and services to evade detection. This article examines the rise of cryptojacking-the use of another's machine for profit through cryptocurrency mining-and how we're all at risk.
In the Bitcoin system, miners are incentivized to join the system and validate transactions through fees paid by the users. A simple "pay your bid" auction has been employed to determine the transaction fees. Recently, Lavi, Sattath and Zohar [Lavi et al., 2019] proposed an alternative fee design, called the monopolistic price (MP) mechanism, aimed at improving the revenue for the miners. Although MP is not strictly incentive compatible (IC), they studied how close to IC the mechanism is for iid distributions, and conjectured that it is nearly IC asymptotically based on extensive simulations and some analysis. In this paper, we prove that the MP mechanism is nearly incentive compatible for any iid distribution as the number of users grows large. This holds true with respect to other attacks such as splitting bids. We also prove a conjecture in [Lavi et al., 2019] that MP dominates the RSOP auction in revenue (originally defined in [Goldberg et al., 2006] for digital goods). These results lend support to MP as a Bitcoin fee design candidate. Additionally, we explore some possible intrinsic correlations between incentive compatibility and revenue in general.
(eng) The main conceptual element this thesis orbits around is the idea of using social networks as a data source. First, classical trading theory and current usage of data obtained from social networks is reviewed. Taking all this information into account, a forecasting of the Bitcoin price is performed using both classical methods and machine learning Neural Networks. In order to obtain data from social networks, another complexity layer needs to be added by accessing the sources through APIs and directly web-scrapping the net. The results of all of this complex implementation are given with a strong focus on visualisation using several different techniques. Finally, after a critical discussion a Future Work chapter is introduced, where many possible follow-ups are drawn up.