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on Payment Systems and Financial Technology |
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Issue of 2024‒06‒10
twenty-one papers chosen by |
| By: | Ozili, Peterson K |
| Abstract: | Central banks are considering the issuance of a central bank digital currency to serve as a payment tool to support economic activities. A central bank digital currency can also serve secondary purposes that are related or unrelated to the statutory objectives of a central bank which is monetary and price stability. Many central banks are thinking too fast about central bank digital currencies – they are very optimistic about the potential benefits of central bank digital currencies. While such optimism is good, central banks also need to think slowly about central bank digital currency by paying serious attention to known risks and whether there is a unique use case for CBDC. This calls for cautious optimism and a need for central banks to think fast and slow about central bank digital currencies. |
| Keywords: | CBDC, central bank digital currency, cryptocurrency, digital payment, thinking fast and slow |
| JEL: | E40 E42 E49 E50 E52 E58 E59 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:120774&r= |
| By: | Sid Bhatia; Samuel Gedal; Himaya Jeyakumar Grace Lee; Ravinder Chopra; Daniel Roman; Shrijani Chakroborty |
| Abstract: | This paper examines the dynamics of the cryptocurrency market and proposes a novel blockchain-based protocol for real estate transactions. Our analysis includes a detailed review of price trends, volatility, and correlations within the cryptocurrency market, focusing on major assets like Bitcoin, Ethereum, and Tether. We provide a critical assessment of the impact of significant market events, such as the FTX bankruptcy, highlighting the vulnerabilities and resilience of the crypto market. The study also explores the potential of blockchain technology to innovate real estate transactions by enabling the secure and transparent handling of property deeds without traditional intermediaries. We introduce a blockchain protocol that reduces transaction costs, enhances security, and increases transparency, making real estate transactions more accessible and efficient. Our proposal aims to leverage the inherent benefits of blockchain to address real-world challenges in real estate transactions, providing a scalable and secure platform for property sales in a global market. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.02547&r= |
| By: | Yihang Fu; Mingyu Zhou; Luyao Zhang |
| Abstract: | In the distributed systems landscape, Blockchain has catalyzed the rise of cryptocurrencies, merging enhanced security and decentralization with significant investment opportunities. Despite their potential, current research on cryptocurrency trend forecasting often falls short by simplistically merging sentiment data without fully considering the nuanced interplay between financial market dynamics and external sentiment influences. This paper presents a novel Dual Attention Mechanism (DAM) for forecasting cryptocurrency trends using multimodal time-series data. Our approach, which integrates critical cryptocurrency metrics with sentiment data from news and social media analyzed through CryptoBERT, addresses the inherent volatility and prediction challenges in cryptocurrency markets. By combining elements of distributed systems, natural language processing, and financial forecasting, our method outperforms conventional models like LSTM and Transformer by up to 20\% in prediction accuracy. This advancement deepens the understanding of distributed systems and has practical implications in financial markets, benefiting stakeholders in cryptocurrency and blockchain technologies. Moreover, our enhanced forecasting approach can significantly support decentralized science (DeSci) by facilitating strategic planning and the efficient adoption of blockchain technologies, improving operational efficiency and financial risk management in the rapidly evolving digital asset domain, thus ensuring optimal resource allocation. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.00522&r= |
| By: | Joerg Mayer |
| Abstract: | Traditional trust-related de-dollarization motives have gained additional impetus from the declining share of the United States in global output, recent upheaval in dollar bond markets, geopolitical tensions, and a “weaponization†of the dollar. Several institutional innovations by China and the BRICS demonstrate the demand for de-dollarization but do not offer credible alternatives to the dollar’s value characteristics. By contrast, new financial technology, including distributed ledger technology (DLT), and related changes in cross-border payment infrastructure could reduce the network effects that have sustained dollar dominance. By allowing for leaner cross-border payment infrastructures and an easier, cheaper, and more transparent use of non-dollar currencies in cross-border payment and settlement, DLT-based wholesale central bank digital currency (wCBDC) platforms with a foreign-exchange conversion layer may indicate a direction of travel. Pilots of multicurrency wCBDC-platforms indicate how to enable interoperability and reduce exposure to foreign-exchange risk. Regarding institutional (legal, regulatory, and supervisory) frameworks required to fully benefit from infrastructural changes, interlinking common multicurrency wCBDC-platforms among limited numbers of like-minded central banks to form an interoperable hub-and-spoke global wCBDC-system could minimize fragmentation risks while accommodating diverging governance preferences, e.g., concerning data protection and developmental aspirations. By augmenting macroeconomic autonomy and reducing the need for costly dollar reserves, de-dollarization promises greater benefits for countries with non-dominant currencies. These countries should sit at the table when outstanding questions on interoperability and related economic, technical, legal and governance questions regarding multicurrency wCBDCs platforms are answered. |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:imk:fmmpap:102-2024&r= |
| By: | Ravi Kashyap |
| Abstract: | We develop several innovations designed to bring the best practices of traditional investment funds to the blockchain landscape. Our innovations combine the superior mechanisms of mutual funds and hedge funds. Specifically, we illustrate how fund prices can be updated regularly like mutual funds and performance fees can be charged like hedge funds. We show how mutually hedged blockchain investment funds can operate with investor protection schemes - high water marks - and measures to offset trading slippage when redemptions happen. We provide detailed steps - including mathematical formulations and instructive pointers - to implement these ideas as blockchain smart contracts. We discuss how our designs overcome several blockchain bottlenecks and how we can make smart contracts smarter. We provide numerical illustrations of several scenarios related to the mechanisms we have tailored for blockchain implementation. The concepts we have developed for blockchain implementation can also be useful in traditional financial funds to calculate performance fees in a simplified manner. We highlight two main issues with the operation of mutual funds and hedge funds and show how blockchain technology can alleviate those concerns. The ideas developed here illustrate on one hand, how blockchain can solve many issues faced by the traditional world and on the other hand, how many innovations from traditional finance can benefit decentralized finance and speed its adoption. This becomes an example of symbiosis between decentralized and traditional finance - bringing these two realms closer and breaking down barriers between such artificial distinctions - wherein the future will be about providing better risk adjusted wealth appreciation opportunities to end customers through secure, reliable, accessible and transparent services - without getting too caught up about how such services are being rendered. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.02302&r= |
| By: | Yulin Liu; Luyao Zhang |
| Abstract: | Decentralized Autonomous Organizations (DAOs), utilizing blockchain technology to enable collective governance, are a promising innovation. This research addresses the ongoing query in blockchain governance: How can DAOs optimize human cooperation? Focusing on the Network Nervous System (NNS), a comprehensive on-chain governance framework underpinned by the Internet Computer Protocol (ICP) and liquid democracy principles, we employ theoretical abstraction and simulations to evaluate its potential impact on cooperation and economic growth within DAOs. Our findings emphasize the significance of the NNS's staking mechanism, particularly the reward multiplier, in aligning individual short-term interests with the DAO's long-term prosperity. This study contributes to the understanding and effective design of blockchain-based governance systems. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.13768&r= |
| By: | Pablo D. Azar; Adrian Casillas; Maryam Farboodi |
| Abstract: | The decentralized nature of blockchain markets has given rise to a complex and highly heterogeneous market structure, gaining increasing importance as traditional and decentralized (DeFi) finance become more interconnected. This paper introduces the DeFi intermediation chain and provides theoretical and empirical evidence for private information as a key determinant of intermediation rents. We propose a repeated bargaining model that predicts that profit share of Ethereum market participants is positively correlated with their private information, and employ a novel instrumental variable approach to show that a 1 percent increase in the value of intermediaries’ private information leads to a 1.4 percent increase in their profit share. |
| Keywords: | financial intermediation; oligopoly; blockchain; decentralized finance; cybersecurity |
| JEL: | G23 D82 L14 L22 G14 D43 |
| Date: | 2024–05–01 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fednsr:98219&r= |
| By: | Kozo UEDA; Hinata Sasaki |
| Abstract: | This study examines whether cashless spending stimulates spending through subdued salience. We use bank transaction data and leverage events related to Quick Response (QR) code campaign as an instrumental variable. Our estimation offers supporting evidence for subdued salience, demonstrating that an increase in QR code payments prompted by campaigns leads to an approximately samesized increase in other spending. However, this e ect is transitory. Nevertheless, the e ect of QR code campaigns on QR usage exerts a lasting impact over time, increasing the fraction of QR code users by a minimum of 1%. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:cnn:wpaper:24-008e&r= |
| By: | International Monetary Fund |
| Abstract: | Kazakhstan saw a significant increase in crypto mining in 2021 following a ban on mining in China. Volatility in crypto markets and energy shortages, coupled with a prohibition on the circulation of crypto assets in Kazakhstan, reduced the size of the market by the following year. While retail and institutional crypto holdings are limited, growing public sector experiments with distributed ledger technology, a pilot project to allow the circulation of crypto in the Astana International Financial Centre (AIFC), and mandates for crypto miners to store a proportion of their mining rewards in AIFC registered exchanges has the potential to increase the size of the sector. If incentives grow for users and firms to circulate crypto, the existing prohibition – which has dampened market growth, could become untenable. Although not a regulatory priority, the broad prohibition on crypto assets should be replaced by a robust regulatory framework, contingent on market growth, upskilling supervisors, and a globally coordinated move to implementing conduct and prudential regulation. |
| Date: | 2024–04–24 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfscr:2024/095&r= |
| By: | Sabrina Leo; Andrea Delle Foglie; Luca Barbaro; Edoardo Marangone; Ida Claudia Panetta; Claudio Di Ciccio |
| Abstract: | Credit Guarantee Schemes (CGSs) are crucial in mitigating SMEs' financial constraints. However, they are renownedly affected by critical shortcomings, such as a lack of financial sustainability and operational efficiency. Distributed Ledger Technologies (DLTs) have shown significant revolutionary influence in several sectors, including finance and banking, thanks to the full operational traceability they bring alongside verifiable computation. Nevertheless, the potential synergy between DLTs and CGSs has not been thoroughly investigated yet. This paper proposes a comprehensive framework to utilise DLTs, particularly blockchain technologies, in CGS processes to improve operational efficiency and effectiveness. To this end, we compare key architectural characteristics considering access level, governance structure, and consensus method, to examine their fit with CGS processes. We believe this study can guide policymakers and stakeholders, thereby stimulating further innovation in this promising field. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.19555&r= |
| By: | Joshua S. Gans |
| Abstract: | Mobile app commissions paid by app developers to a monopolist device maker/app store operator are examined. Three results are demonstrated. First, unregulated app commissions are set at a level that maximises consumer surplus. Second, eliminating app commissions will lead to higher device prices. Third, requiring a menu of options for consumers as to how device makers receive subsidies from app developers constrains app commissions in a way that provides a more equal balance between consumer versus app developer interests. |
| JEL: | L11 L40 |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:32339&r= |
| By: | Delgado-Rojas, Martha Elena (FEDESARROLLO); Benavides, Juan (FEDESARROLLO) |
| Abstract: | We analyze the retail sector in Colombia, the traditional and online market stakeholders. Chapter 1 includes an evolution analysis of online sales in Latin America and the Caribbean, and the importance of internet adoption to increasing consumer value. Chapter 2 focuses on retail industry in Colombia, in supply and demand side. Chapter 3 presents the study results. |
| Keywords: | E-commerce; Online Retail; Comercio Electrónico Ventas en Línea |
| JEL: | L81 |
| Date: | 2023–06–16 |
| URL: | http://d.repec.org/n?u=RePEc:col:000124:021031&r= |
| By: | Claudio Bellei; Muhua Xu; Ross Phillips; Tom Robinson; Mark Weber; Tim Kaler; Charles E. Leiserson; Arvind; Jie Chen |
| Abstract: | Subgraph representation learning is a technique for analyzing local structures (or shapes) within complex networks. Enabled by recent developments in scalable Graph Neural Networks (GNNs), this approach encodes relational information at a subgroup level (multiple connected nodes) rather than at a node level of abstraction. We posit that certain domain applications, such as anti-money laundering (AML), are inherently subgraph problems and mainstream graph techniques have been operating at a suboptimal level of abstraction. This is due in part to the scarcity of annotated datasets of real-world size and complexity, as well as the lack of software tools for managing subgraph GNN workflows at scale. To enable work in fundamental algorithms as well as domain applications in AML and beyond, we introduce Elliptic2, a large graph dataset containing 122K labeled subgraphs of Bitcoin clusters within a background graph consisting of 49M node clusters and 196M edge transactions. The dataset provides subgraphs known to be linked to illicit activity for learning the set of "shapes" that money laundering exhibits in cryptocurrency and accurately classifying new criminal activity. Along with the dataset we share our graph techniques, software tooling, promising early experimental results, and new domain insights already gleaned from this approach. Taken together, we find immediate practical value in this approach and the potential for a new standard in anti-money laundering and forensic analytics in cryptocurrencies and other financial networks. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.19109&r= |
| By: | Ariane Reyns |
| Abstract: | This dissertation explores the potential alternative currencies have as flexible tools to generate resilience. Their flexible nature, because complementary to conventional ones, makes them particularly useful to economic agents as it empowers them to utilize these when needed. Therefore, this thesis asserts that their strength lies significantly in a transitional power, offering a capacity to adapt amid uncertainties and thus creating resilience. This relationship between resilience and alternative currencies is investigated using robust empirical methods. Given vast differences in design and purpose, the study distinguishes between Complementary Currencies (CCs), considered to benefit local sustainability and economic development, and cryptocurrencies. This differentiation is crucial in understanding their varied impacts at different levels of the economic system: CCs influence organizational and potentially regional resilience, impacting local economic dynamics. In contrast, cryptocurrencies, studied for portfolio diversification, may affect overall portfolio resilience at a broader economic level. Key contributions in the initial chapters unveil deliberate and strategic business engagement with CCs, recognizing a distinct economic rationale. Findings suggest this economic rationale manifests as an "insurance" function when CCs actively mitigate the detrimental effects of economic crises. Notably, such dynamics prove most effective for the more vulnerable enterprises. These results further validate our conceptualization of the transitional power of CCs, emphasizing their ability to foster resilience in the face of economic challenges. The last chapter explores parallel dynamics for cryptocurrencies, highlighting Bitcoin's potential in wartime portfolio diversification, serving as an alternative asset. Overall, this research contributes to our understanding of alternative currencies' effectiveness in enhancing resilience, providing timely insights for reshaping economic systems amid contemporary global challenges. |
| Keywords: | Alternative currencies; Complementary currencies; Mutual Credit Systems; Cryptocurrencies; Financial resilience; Regional resilience; Small firms |
| Date: | 2024–05–02 |
| URL: | http://d.repec.org/n?u=RePEc:ulb:ulbeco:2013/373465&r= |
| By: | Qi Deng; Zhong-guo Zhou |
| Abstract: | We propose that the liquidity of an asset includes two components: liquidity jump and liquidity diffusion. We find that the liquidity diffusion has a higher correlation with crypto washing trading than the liquidity jump. We demonstrate that the treatment of washing trading significantly reduces the liquidity diffusion, but only marginally reduces the liquidity jump. We find that the ARMA-GARCH/EGARCH models are highly effective in modeling the liquidity-adjusted return with and without treatment on wash trading. We argue that treatment on wash trading is unnecessary in modeling established crypto assets that trade in mainstream exchanges, even if these exchanges are unregulated. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.07222&r= |
| By: | Silvia Garc\'ia-M\'endez; Francisco de Arriba-P\'erez; Ana Barros-Vila; Francisco J. Gonz\'alez-Casta\~no |
| Abstract: | Microblogging platforms, of which Twitter is a representative example, are valuable information sources for market screening and financial models. In them, users voluntarily provide relevant information, including educated knowledge on investments, reacting to the state of the stock markets in real-time and, often, influencing this state. We are interested in the user forecasts in financial, social media messages expressing opportunities and precautions about assets. We propose a novel Targeted Aspect-Based Emotion Analysis (TABEA) system that can individually discern the financial emotions (positive and negative forecasts) on the different stock market assets in the same tweet (instead of making an overall guess about that whole tweet). It is based on Natural Language Processing (NLP) techniques and Machine Learning streaming algorithms. The system comprises a constituency parsing module for parsing the tweets and splitting them into simpler declarative clauses; an offline data processing module to engineer textual, numerical and categorical features and analyse and select them based on their relevance; and a stream classification module to continuously process tweets on-the-fly. Experimental results on a labelled data set endorse our solution. It achieves over 90% precision for the target emotions, financial opportunity, and precaution on Twitter. To the best of our knowledge, no prior work in the literature has addressed this problem despite its practical interest in decision-making, and we are not aware of any previous NLP nor online Machine Learning approaches to TABEA. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.08665&r= |
| By: | Hulusi Mehmet Tanrikulu; Hakan Pabuccu |
| Abstract: | Forecasting cryptocurrencies as a financial issue is crucial as it provides investors with possible financial benefits. A small improvement in forecasting performance can lead to increased profitability; therefore, obtaining a realistic forecast is very important for investors. Successful forecasting provides traders with effective buy-or-hold strategies, allowing them to make more profits. The most important thing in this process is to produce accurate forecasts suitable for real-life applications. Bitcoin, frequently mentioned recently due to its volatility and chaotic behavior, has begun to pay great attention and has become an investment tool, especially during and after the COVID-19 pandemic. This study provided a comprehensive methodology, including constructing continuous and trend data using one and seven years periods of data as inputs and applying machine learning (ML) algorithms to forecast Bitcoin price movement. A binarization procedure was applied using continuous data to construct the trend data representing each input feature trend. Following the related literature, the input features are determined as technical indicators, google trends, and the number of tweets. Random forest (RF), K-Nearest neighbor (KNN), Extreme Gradient Boosting (XGBoost-XGB), Support vector machine (SVM) Naive Bayes (NB), Artificial Neural Networks (ANN), and Long-Short-Term Memory (LSTM) networks were applied on the selected features for prediction purposes. This work investigates two main research questions: i. How does the sample size affect the prediction performance of ML algorithms? ii. How does the data type affect the prediction performance of ML algorithms? Accuracy and area under the ROC curve (AUC) values were used to compare the model performance. A t-test was performed to test the statistical significance of the prediction results. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.19324&r= |
| By: | Antonio Scala; Marco Delmastro |
| Abstract: | Networks have always played a special role for human beings in shaping social relations, forming public opinion, and driving economic equilibria. Nowadays, online networked platforms dominate digital markets and capitalization leader-boards, while social networks drive public discussion. Despite the importance of networks in many economic and social domains (economics, sociology, anthropology, psychology, ...), the knowledge about the laws that dominate their dynamics is still scarce and fragmented. Here, we analyse a wide set of online networks (those financed by advertising) by investigating their value dynamics from several perspectives: the type of service, the geographic scope, the merging between networks, and the relationship between economic and financial value. The results show that the networks are dominated by strongly nonlinear dynamics. The existence of non-linearity is often underestimated in social sciences because it involves contexts that are difficult to deal with, such as the presence of multiple equilibria -- some of which are unstable. Yet, these dynamics must be fully understood and addressed if we aim to understand the recent evolution in the economic, political and social milieus, which are precisely characterised by corner equilibria (e.g., polarization, winner-take-all solutions, increasing inequality) and nonlinear patterns. |
| Date: | 2022–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2208.04813&r= |
| By: | Petry, Johannes |
| Abstract: | The chapter analysis the role of exchanges as infrastructure providers in capital markets. While traditionally regarded as mere marketplaces, neutral spaces facilitating financial transactions, exchanges have evolved into powerful actors in their own right. Over time, exchanges have become complex organisations that enable the functioning of capital markets. While financial markets are used by investors to allocate financial assets, provide corporate financing and facilitate economic growth, certain infrastructural arrangements must exist to enable these transactions: from market data, indices, financial products, trading platforms to clearing, exchanges shape the infrastructures that underpin global capital markets. This chapter explores the commonalities and differences among exchanges, investigating their common role in the provision of financial infrastructures but also emphasizing their embeddedness within institutional environments and hierarchical positioning within the global financial system. Moreover, it addresses emerging challenges and potential contestations, particularly with the rise of exchanges in emerging markets, amidst an increasingly fragmented global economy. |
| Date: | 2024–04–12 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:5gwte&r= |
| By: | Wang, Qi (State Key Laboratory of Pollution Control & Resource Reuse, School of Environment, Nanjing University); Liu, Mengdi (School of International Trade and Economics, University of International Business and Economics); Xu, Jintao (The National School of Development, Peking University); Zhang, Bing (State Key Laboratory of Pollution Control & Resource Reuse, School of Environment, Nanjing University) |
| Abstract: | Scholars have demonstrated that governments allow citizens to express their opinions and selectively respond to them, yet little is known about how local governments interact with netizens via social media. In this paper, we measure government responsiveness based on whether the government verbally responds to public environmental complaints on social media. Using crawled real-world interactions between citizens and governments on Weibo (a Twitter-like platform), we find that higher bargaining power is associated with a lower likelihood that the government will respond to an environmental complaint against a firm. Local governments in China are more likely to respond to appeals that are likely to attract the attention of the upper-level government. Moreover, involving upper-level government through social media can weaken the bargaining power of industrial giants. Finally, public complaints have a significant short-term impact on corporate environmental performance but have a limited effect on firms with high bargaining power. |
| Keywords: | government responsiveness; bargaining power; social media; environmental complaint |
| JEL: | Q58 |
| Date: | 2023–03–22 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:gunefd:2023_005&r= |
| By: | Dyer, Travis; Köchling, Gerrit; Limbach, Peter |
| Abstract: | We show that investors acquire more public information about firms to which they are more socially proximate. On average, a standard deviation increase in the Social Connectedness Index (Bailey et al., 2018) between a firm's headquarter county and a searcher county is associated with 30% more EDGAR filing downloads from the searcher county. The effect of social proximity on traditional investment research is distinct from the effect of geographic proximity. We find similar results studying headquarter relocations, investor-level data, and EDGAR downloads from European regions, for which physical distance should be irrelevant. Social proximity matters more during times of high market-wide uncertainty and for firms with weaker information environments. Finally, information gathered by socially proximate investors predicts short-term earnings and stock returns, but also heightened volatility. Collectively, the evidence indicates that social networks mitigate informational frictions and foster information acquisition in financial markets. |
| Keywords: | Corporate disclosures, EDGAR, Geography, Information acquisition, Social networks, Social connections |
| JEL: | D80 D83 G10 G41 M40 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:cfrwps:294838&r= |