In the blockchain economy, non-fungible tokens (NFTs), which theoretically reflect ownership of a digital asset registered on a public blockchain like Ethereum, have swiftly grown to be a significant component. This chapter explores the complex world of NFTs in marketing, offering a fair analysis of the potential and present difficulties that exist at this nexus. In the first section, the benefits that NFTs provide to marketing are highlighted. They are shown to be able to raise customer engagement, encourage brand loyalty, and transform digital ownership experiences. The ethical implications of ideas like manufactured scarcity, intellectual property, and cultural effects are examined. In conclusion, the chapter offers a comprehensive framework for understanding NFTs in marketing from the perspectives of risk, reward, and ethics. The chapter provides marketers, investors, and stakeholders with a sophisticated understanding to help them carefully and strategically navigate the complicated terrain of NFTs in marketing by deconstructing these crucial components.
Considering that cryptocurrencies are now present in practically every financial transaction because they are widely accepted as an alternate means of making payments and exchanging currencies, academics and economists have more opportunities to study cryptocurrency prices. Over the years, investors, traders and investment banks have found it difficult to predict the closing daily price of Ethereum due to its rapid price fluctuation. The daily closing price of cryptocurrency is essential to consider when trading or investing in Ethereum. This report focuses on carrying out a comparative study of the predictive capabilities of deep machine learning algorithms with a stacking ensemble modelling framework using daily historical observations of the price of Ethereum obtained from Coindesk, tweets extracted from Twitter ranging from the 1st of August 2022 to the 8th of August 2022 and other five covariates (closing price lag1, closing price lag2, noltrend, daytype and month) engineered from the closing price of Ethereum. Seven models are used to compute the forecasts for the daily closing price of Ethereum; these are the recurrent neural network, ensemble stacked recurrent neural network, gradient boosting machine, generalized linear model, distributed random forest, deep neural networks and stacked ensemble for gradient boosting machine, generalized linear model, distributed random forest and deep neural networks. The main evaluation metric used is the mean absolute error. According to MAE, RNN forecasts outperform the other modelâs forecasts in this study, producing an MAE of 0.0309.
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.
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.
Dayan A. Perera, Jessica Lim, Shuta Gunraku, Wern Han Lim
This research introduces an innovative approach to forecasting cryptocurrency prices by combining user-generated content (UGC) and sentiment analysis with quantitative data. The primary goal is to overcome limitations in existing methods for market forecasting, where accurate forecasting is crucial for informed decision-making and risk mitigation. The paper suggests a robust prediction methodology by integrating sentiment analysis and quantitative data. The study reviews prior research on sentiment analysis and quantitative analysis of cryptocurrency and stock price prediction. It explores the integration of machine learning and deep learning techniques, an area not extensively explored before. The methodology employs Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), Bidirectional LSTM and Gated Recurrent Unit (GRU) models to capture temporal dependencies. Prediction accuracy is assessed using metrics including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and a confusion matrix. Results show that GRU models excel in prediction, while RNN models outperform in predicting price movements; with an emphasis on the significance of a suitable data preprocessing pipeline towards improving model performance. In summary, this study demonstrates the effectiveness of integrating sentiment analysis and quantitative data for cryptocurrency price forecasting using UGC data.
As the global economy continues to grow, more companies are outsourcing their logistics activities to third-party logistics (3PL) providers. This is because consumers have a high demand for various types of goods delivery, \nincluding for small and large packages, light and heavy items, and so on. As a result, package delivery services have become more competitive, offering a range of services to meet these diverse needs. However, this increase in competition has also made it more important for consumers to carefully consider their options and choose a delivery service that is efficient,cost-effective, and reliable. The solution is to use a logistics recommendation system and smart contracts that allow consumers to easily determine and order logistics services according to their needs. Therefore, in this study the author want to state that this paper as proposed paper.
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 institutionsâ control. Byleveraging lending pool dynamics, we propose a method that inte-grates borrowersâ 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.
Decentralized exchanges are becoming a competitive necessity for Web3 users. However, they cannot beat centralized exchanges in terms of user experience. Due to expensive gas fees, the blockchain cannot implement the classic order book model which is the pillar for traditional finance exchanges. Instead, most decentralized exchanges operate on the Automated Market Maker (AMM) model using a pre-defined mathematical pricing rule. AMM is more efficient to run on the blockchain but the biggest tradeoff is impermanent loss for liquidity providers and price slippage for traders. This remains the most noticeable drawback of todayâs AMM. In this paper, we make the following contributions. First, we observe that if AMM is virtualized as an order book, its âorder-book" shape is awkwardly different from that of a real-world order book. We argue that this is conceptually connected to the above weakness. We are thus motivated to design an AMM, the first of its kind, that mimics the price impact behaviors of real-world order books. Second, the proposed AMM, thanks to this property, significantly outperforms the state-of-the-art AMM in impermanent loss. Interestingly, our AMM can even result in impermanent gain. We are also better for large orders where price slippage is a concern. Third, another feature is that, while todayâs AMM typically requires a fixed inventory ratio for the liquidity pool, the new AMM allows this ratio to vary, giving liquidity providers flexible options for joining or exiting the pool. All these advantages are offered without losing desirable properties of an AMM regarding split-order exploitation, arbitrage risks, and liquidity continuity. Our findings are validated by theoretical analysis with mathematical proofs and, also, experimental evaluation which was comprehensively conducted using two real-world datasets and a synthetic dataset representing different market scenarios. Our research is the first in the literature on AMM design that factors in statistical properties from the order book model.
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.
We consider a manufacturer and an e-platform where the manufacturer sells products through offline and e-platform channels under the cap-and-trade scheme. The interaction between the two channels produces the cross-channel effect (CCE). This manufacturer cooperates with the e-platform in the agency or reseller mode. In addition, the platform shares its observed data with the manufacturer and the blockchain can achieve traceability and high transparency of these data. We formulate a Stackelberg game to derive the following results: In the agency mode, the optimal service level is independent of (decreases with) CCE without (with) blockchain. CCE positively affects the optimal service level in the reseller mode and the optimal service level has no impact on (increases with) the cap in the agency (reseller) mode. Second, when the platform-enabled power is high (low), the platform is (not) willing to adopt blockchain. Third, coordination of the manufacturer and platform only can be achieved in the agency mode when CCE is low with blockchain. The reseller mode can (cannot) achieve coordination of the two firms with (without) blockchain. We also consider the cases where the manufacturer serves as the leader and the omni-channel strategy is adopted to check the robustness of the coordination results.
Md. Nafis Tahmid Akhand, Md. Ahsan Habib, Kazi Md. Rokibul Alam
Cryptocurrency has emerged as a popular investment option due to its decentralized nature and potential for high returns. However, the cryptocurrency market is characterized by high volatility and price fluctuations, making it difficult for investors and traders to make informed decisions. This paper aims to address this challenge by performing a time series analysis of cryptocurrency prices to estimate values in real-time. To achieve this goal, historical price data for three cryptocurrenciesâBitcoin, Ethereum, and Litecoin are gathered and preprocessed. Following this, a range of time series techniques are used to analyze the patterns and trends in the data. This study focuses on hourly and daily data of the cryptocurrencies and employs three hybrid models such as CNN-LSTM (CLT), CNN-GRU (CGR), and CNN-BiLSTM (CBL) to forecast upcoming prices. Among three models, the CLT technique outperforms other models with RMSE of 235.97, MAE of 135.42, and MAPE of 0.47% on hourly Bitcoin price prediction. The experimental results demonstrate the effectiveness of the proposed methods in predicting the prices of cryptocurrencies in real-time.
Bitcoin trading offers security, ownership verifications, easy transactions, and traceability in almost every sector and industry. The base of Bitcoin is blockchain technology, which uses a mining process to secure and validate Bitcoin transactions. With promising positive returns, Bitcoin mining demands large energy and material resources and contributes to environmental degradation. The study uses behavioral reasoning theory to understand intentions to practice Bitcoin mining. We present a relative view of personal, technological, psychological, and environmental factors that attract or repel miners to perform bitcoin mining, where the institutional environment moderates the relationship between attitude and intention by analyzing the cross-sectional data from 308 bitcoin miners. The study indicates that attracting factors positively and significantly explain minersâ attitudes, which leads to positive intentions toward Bitcoin mining. The regulatory environment weakens the positive relationship between attitude and intentions. The study provides a comparative view of facilitating and inhibiting factors with significant implications for academicians and policymakers.
Blockchain technology has been adopted to improve traceability and authenticity in wine supply chains (WSCs). However, whether through outsourcing or self-implementation of a blockchain-based wine traceability system (BTS), there are significant costs involved, as well as concerns regarding consumer privacy. Motivated by observations of real-world practice, we explore the value of blockchain in enhancing traceability and authenticity in WSCs through a Stackelberg game-theoretical analysis. By comparing the equilibrium solutions of the scenarios with and without blockchain, we uncover the value of blockchain in tracing wine products. Our findings show that blockchain adoption can increase WSC prices under certain conditions. We derive the threshold for a third-party BTS service fee that determines blockchain adoption for tracing wine products and reveal the moderating effect of consumer traceability preferences and privacy concerns. Furthermore, the investigation of who should lead the implementation of BTS finds that the equal cost sharing between the manufacturer and the retailer results in no difference in BTS implementation leadership. Otherwise, the manufacturer always benefits from taking the lead in the implementation of BTS, and the retailer should undertake a leadership role in BTS implementation if they need to bear higher costs.
Currently, over 90% of Ethereum blocks are built using MEV-Boost, an auction that allows validators to sell their block-building power to builders who compete in an open English auction in each slot. Shortly after the merge, when MEV-Boost was in its infancy, most block builders were neutral, meaning they did not trade themselves but rather aggregated transactions from other traders. Over time, integrated builders, operated by trading firms, began to overtake many of the neutral builders. Outside of the integrated builder teams, little is known about which advantages integration confers beyond latency and how latency advantages distort on-chain trading. This paper explores these poorly understood advantages. We make two contributions. First, we point out that integrated builders are able to bid truthfully in their own bundle merge and then decide how much profit to take later in the final stages of the PBS auction when more information is available, making the auction for them look closer to a second-price auction while independent searchers are stuck in a first-price auction. Second, we find that latency disadvantages convey a winner's curse on slow bidders when underlying values depend on a stochastic price process that change as bids are submitted.
Many e-commerce platforms, such as Amazon and JD.com, consider product information disclosure as a crucial retail strategy. However, due to potential consumer distrust in the disclosed information, these platforms may employ blockchain technology to validate the information and alleviate consumersâ doubts. This study presents a game-theoretical model to investigate the interaction among the manufacturerâs choice of selling mode, the platformâs decision regarding blockchain adoption, and information disclosure strategies in the presence of information asymmetry. Optimal pricing and information disclosure strategies are derived, and the impact of various parameters on the equilibrium results is analyzed. We find that the consumersâ privacy concerns and blockchain implementation costs both have negative effects on the equilibrium retail price, disclosed information amount and profits of the manufacturer and platform. Moreover, we also find that the introduction of blockchain has different effects on a platformâs pricing and information disclosure strategies depending on the market size. In addition, conditions are identified under which the manufacturer selects the optimal selling mode and the platform makes an optimal decision on blockchain adoption. The findings demonstrate that the cost of consumersâ privacy concerns and the commission rate play significant roles in determining the equilibrium selling mode selection and blockchain adoption strategies.
In the era of big data, there is an urgent need to establish data trading markets for effectively releasing the tremendous value of the drastically explosive data. Data security and data pricing, however, are still widely regarded as major challenges in this respect, which motivate this research on the novel multi-blockchain based framework for data trading markets and their associated pricing mechanisms. In this context, data recording and trading are conducted separately within two separate blockchains: the data blockchain (DChain) and the value blockchain (VChain). This enables the establishment of two-layer data trading markets to manage initial data trading in the primary market and subsequent data resales in the secondary market. Moreover, pricing mechanisms are then proposed to protect these markets against strategic trading behaviors and balance the payoffs of both suppliers and users. Specifically, in regular data trading on VChain-S2D, two auction models are employed according to the demand scale, for dealing with users' strategic bidding. The incentive-compatible Vickrey-Clarke-Groves (VCG) model is deployed to the low-demand trading scenario, while the nearly incentive-compatible monopolistic price (MP) model is utilized for the high-demand trading scenario. With temporary data trading on VChain-D2S, a reverse auction mechanism namely two-stage obscure selection (TSOS) is designed to regulate both suppliers' quoting and users' valuation strategies. Furthermore, experiments are carried out to demonstrate the strength of this research in enhancing data security and trading efficiency.