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on Payment Systems and Financial Technology |
| By: | Eric Darmon; Thomas LE TEXIER; Zhiwen LI; Thierry Pénard |
| Abstract: | Antitrust authorities are concerned with the dominant market position of Tech Giants such as Google, Meta, or Amazon. These digital conglomerates are characterized by platform-based business models and multimarket contact (MMC). In traditional one-sided markets, theory and empirical evidence show that MMC tends to relax competition. In this paper, we revisit this result in the context of platform competition with competitive bottleneck and cross-market externalities, and provide new insights into the impact of MMC on platform competition. In this context, when platforms charge the two groups of users (bilateral pricing), we find that MMC always decreases the profitability of platforms regardless of the nature and magnitude of cross-market externalities. Then we consider the case in which platforms can only charge one group of users (unilateral pricing). When platforms charge the side on which they are not directly competing for users (i.e. the side that is not the competitive bottleneck), MMC may relax competition only if cross-group externalities and cross-market externalities are both sufficiently small. From a competition policy perspective, our paper provides insights into how antitrust authorities should review conglomerate mergers in digital markets and assesses the effects of the diversification strategies of digital platforms in the context of cross-market externalities and competitive bottleneck. |
| Keywords: | two-sided markets, platform competition, digital markets, multimarket contact, cross-market externalities, competitive bottleneck, competition policy |
| JEL: | D43 L13 L41 L86 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:drm:wpaper:2025-22 |
| By: | Lin William Cong; Simon Mayer |
| Abstract: | We model the competition between digital forms of fiat money and private digital money (PDM). Countries strategically digitize their fiat money — upgrading existing or launching new payment systems (including CBDCs) — to enhance adoption and counter PDM competition. A pecking order emerges: less dominant currencies digitize earlier, reflecting a first-mover advantage; dominant currencies delay digitization until they face competition; the weakest currencies forgo digitization. Delayed digitization allows PDM to gain dominance, eventually weakening fiat money’s role. We also highlight how geopolitical considerations, stablecoins, and interoperability between fiat and private digital money shape the digitization of money and monetary competition. |
| JEL: | E50 E58 F30 G18 G50 O33 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33593 |
| By: | Lin William Cong; Eswar S. Prasad; Daniel Rabetti |
| Abstract: | Oracles are software components that enable data exchange between siloed blockchains and external environments, enhancing smart contract capabilities and platform interoperability. Oracles play key roles in decentralized finance and blockchain applications in centralized finance. We find that integration into decentralized oracle networks is positively associated with key measures of economic activity such as Total Value Locked, triggered by positive network effects in adoption and usage. Our study reveals symbiotic gains from enhanced interoperability and network effects across protocols on a given chain and among integrated chains. Oracle integration appears to improve risk-sharing and mitigates contagion, increasing resilience during turbulent periods in crypto markets. Overall, oracles emerge as a crucial component to enable informational and economic integration in decentralized finance ecosystems. |
| JEL: | F15 F36 G15 G29 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33639 |
| By: | Kenechukwu E. Anadu; Pablo D. Azar; Catherine Huang; Marco Cipriani; Thomas M. Eisenbach; Gabriele La Spada; Mattia Landoni; Marco Macchiavelli; Antoine Malfroy-Camine; J. Christina Wang |
| Abstract: | Stablecoins are crypto assets whose value is pegged to that of a fiat currency, usually the U.S. dollar. In our first Liberty Street Economics post, we described the rapid growth of stablecoins, the different types of stablecoin arrangements, and the May 2022 run on TerraUSD, the fourth largest stablecoin at the time. In a subsequent post, we estimated the impact of large declines in the price of bitcoin on cumulative net flows into stablecoins and showed the existence of flight-to-safety dynamics similar to those observed in money market mutual funds during periods of stress. In this post, we document the growth of stablecoins since 2019, including the evolution of the reported collateral backing major stablecoins. Then, we estimate the impact on the stablecoin industry of large bitcoin price increases that occurred between 2021 and 2025. |
| Keywords: | stablecoins; crypto assets |
| JEL: | G23 |
| Date: | 2025–04–23 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fednls:99893 |
| By: | Lin William Cong; Zhiheng He; Ke Tang |
| Abstract: | Tokens offer convenience in digital networks and earn rewards when staked for consensus generation or economic activities. In our continuous-time model, agents dynamically allocate wealth over on-platform transactions and staking. Aggregate staking ratio crucially shapes platform productivity, grows userbase, and links staking to endogenous reward rates and price dynamics. Transaction fees, token issuance, and user heterogeneity all affect platform lifecycle. Empirical findings support the theoretical predictions: (i) correlation between staking ratio and reward rate is cross-sectionally positive, but time-series-wise negative; (ii) staking ratios positively predict excess returns; (iii) the convenience wedge generates UIP violations and significant crypto carry premia. |
| JEL: | C73 E42 F43 L86 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33640 |
| By: | Zahra Ebrahimi; Maxi Guennewig; Bryan Routledge; Ariel Zetlin-Jones |
| Abstract: | Blockchain is a database technology that enables a group of self-interested users to maintain a distributed ledger without relying on a trusted third party, such as a bank. In this paper, we develop a new game-theoretic framework for analyzing blockchain systems, wherein each user determines how to update the distributed ledger. The usefulness of blockchains depends on whether users’ updating strategies achieve consensus—meaning that they agree on the correct version of the ledger and have no incentive to omit or alter past data. We show that the currently implemented strategy—the longest chain rule—fails to achieve consensus when users are sufficiently heterogeneous. We then establish the existence of new equilibrium strategies, which are slight modifications of the longest chain rule and ensure consensus regardless of the degree of heterogeneity. In practice, these equilibrium strategies enhance the resilience of blockchain systems against threats such as double-spending and 51% attacks. Our findings underscore the critical role economic incentives play in determining the security and stability of blockchain ledgers. |
| Keywords: | Blockchain, consensus, double-spending |
| JEL: | D80 D83 E42 G2 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:bon:boncrc:crctr224_2025_685 |
| By: | Pablo D. Azar; Sergio Olivas; Nish Sinha |
| Abstract: | This paper investigates the speed of price discovery when information becomes publicly available but requires costly processing to become common knowledge. We exploit the unique institutional setting of hacks on decentralized finance (DeFi) protocols. Public blockchain data provides the precise time a hack’s transactions are recorded—becoming public information—while subsequent social media disclosures mark the transition to common knowledge. This empirical design allows us to isolate the price impact occurring during the interval characterized by information asymmetry driven purely by differential processing capabilities. Our central empirical finding is that substantial price discovery precedes common knowledge: approximately 36 percent of the total 24-hour price decline (∼27 percent) materializes before the public announcement. This evidence suggests sophisticated traders rapidly exploit their ability to process complex, publicly available on-chain data, capturing informational rents. We develop a theoretical model of informed trading under processing costs which predicts strategic, slow information revelation, consistent with our empirical findings. Our results quantify the limits imposed by information processing costs on market efficiency, demonstrating that transparency alone does not guarantee immediate information incorporation into prices. |
| Keywords: | information asymmetry; price discovery; common knowledge; information processing costs; market microstructure; event study; high-frequency data; cryptocurrency; DeFi; cybersecurity hacks; market efficiency |
| JEL: | G12 G14 G18 G23 L86 |
| Date: | 2025–04–01 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fednsr:99907 |
| By: | Ufuk Akcigit; Raman Singh Chhina; Seyit M. Cilasun; Javier Miranda; Nicolas Serrano-Velarde |
| Abstract: | Beginning in January 2021, over less than two years, credit card usage by small U.S. businesses nearly doubled, interest payments rose by 60%, and delinquencies reached 2.8%. In this paper, we utilize near real-time QuickBooks data from over 1.6 million small businesses and a targeted survey to highlight the critical role that credit card financing plays in small business activity. We find, first, monthly credit card payments were up to three times higher than loan payments during this time. Second, we use targeted surveys of these small businesses to establish credit cards as a key financing source in response to firm-level shocks, such as uncertain cash flows and overdue invoices. Third, we highlight the critical role of credit cards as a key financial transmission mechanism. Following the Federal Reserve’s rate hikes in early 2022, banks cut credit card supply, leading to a 15.75% drop in balances and a 10% decline in revenue growth, as well as a 1.5% decrease in employment growth among U.S. small businesses. These higher rates also rendered interest payments unsustainable for many, contributing to half of the observed increase in delinquencies. Lastly, a simple heterogeneous firm model with a cash-in-hand constraint illustrates the significant macroeconomic impact of credit card financing on small business activity. |
| JEL: | G3 O4 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33618 |
| By: | Ran, Ling |
| Abstract: | This paper examines the evolving landscape of cross-border trading enabled by blockchain technology and artificial intelligence (AI). It explores the mechanisms through which blockchain decentralizes trading infrastructure, enhances transaction transparency, and eliminates intermediaries to reduce operational costs. AI integration is analyzed in the context of high-frequency trading, focusing on real-time data processing, algorithmic decision-making, and smart contract automation. The discussion addresses technical and regulatory barriers, including algorithmic failures, cyber threats, jurisdictional discrepancies, and integration complexity. The paper evaluates the resulting shifts in market liquidity, compliance strategies, fraud mitigation, and overall trading efficiency. The convergence of blockchain and AI is framed as a paradigm shift in financial technology infrastructures, with both opportunities and limitations in scalability, regulation, and cost of deployment. The findings suggest a potential for optimized trade execution and autonomous risk-adjusted decision systems under constrained legal and technical environments. |
| Date: | 2025–04–23 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:knz94_v1 |
| By: | Yu Zhang; Yafei Li; Claudio Tessone |
| Abstract: | Decentralized exchanges, such as those employing constant product market makers (CPMMs) like Uniswap V2, play a crucial role in the blockchain ecosystem by enabling peer-to-peer token swaps without intermediaries. Despite the increasing volume of transactions, there remains limited research on identifying optimal trading paths across multiple DEXs. This paper presents a novel line-graph-based algorithm (LG) designed to efficiently discover profitable trading routes within DEX environments. We benchmark LG against the widely adopted Depth-First Search (DFS) algorithm under a linear routing scenario, encompassing platforms such as Uniswap, SushiSwap, and PancakeSwap. Experimental results demonstrate that LG consistently identifies trading paths that are as profitable as, or more profitable than, those found by DFS, while incurring comparable gas costs. Evaluations on Uniswap V2 token graphs across two temporal snapshots further validate LG's performance. Although LG exhibits exponential runtime growth with respect to graph size in empirical tests, it remains viable for practical, real-world use cases. Our findings underscore the potential of the LG algorithm for industrial adoption, offering tangible benefits to traders and market participants in the DeFi space. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.15809 |
| By: | Baston, George |
| Abstract: | This paper investigates the integration of artificial intelligence (AI) into trading ecosystems from 2020 to 2023. It outlines the historical progression of AI applications in financial markets, emphasizing the transition from rule-based algorithms to data-driven machine learning models. The analysis covers AI-driven innovations in biometric identity verification, predictive analytics, and personalized trading systems. The economic contributions of AI are quantified using institutional estimates, with a focus on regional implementation strategies. Regulatory structures, particularly in Singapore, are examined in the context of their role in enabling AI adoption while ensuring compliance. Challenges including data governance, ethical constraints, regulatory inconsistencies, and technical limitations in blockchain infrastructure are analyzed. The discussion also highlights the impact of expertise shortages and the critical need for government-industry collaboration in fintech development. |
| Date: | 2025–04–21 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:te83v_v1 |
| By: | Birzoim, Ammoon |
| Abstract: | This study investigates the integration of Random Forest algorithms and blockchain technology in the domain of decentralized personalized advertising. Through multiple case studies, the report demonstrates applications in fraud detection, transparency enhancement, tokenized loyalty programs, and ad impact measurement. The Random Forest algorithm supports high-dimensional feature selection, classification accuracy, and robustness against data irregularities, while blockchain ensures immutability, auditability, and transactional security. Challenges in federated learning, including non-IID data distributions, heterogeneous device constraints, and communication overhead, are identified. Technical discussions address algorithm convergence, model aggregation frequency, and trust enforcement mechanisms. Future directions include optimization of algorithm performance in decentralized settings, secure model update protocols, and cross-industry adaptation of Blockchain-Federated Learning systems for scalable advertising solutions. |
| Date: | 2025–04–21 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:rvby3_v1 |
| By: | Shagun Tripathi; Georgios Petropoulos; Harris Kyriakou |
| Abstract: | In recent years, several automated caps, or algorithmic quantity regulations (AQRs), have been deployed to police supply conditions in sharing economy platforms. AQRs constitute a paradigm shift in platform regulation, as they enable exhaustive, and low-cost enforcement, thus comprehensively influencing interactions both within and outside the focal platform. However, their actual impact is not known, and has not been studied so far. In this work, we employ a series of difference-in-differences analyses to provide causal evidence on the impact of AQR both within, as well as outside a focal sharing platform - Airbnb. First, within Airbnb, we find that the quality of platform offerings was negatively affected after the introduction of an algorithmic quantity regulation - marked by 6% decline in ratings. Additionally, we find that the AQR affected certain platform participants disproportionately. Providers without organic and designated trust building signals, i.e., inexperienced hosts and non-superhosts, bore the cost of the AQR, ending up worse off than their counterparts. Second, we find that the occupancy rates of providers in the competing platform Vrbo declined by 3.6% as a result to Airbnb’s AQR. Third, we find a reduction in housing prices by 0.402 units after the introduction of the AQR. This research provides novel empirically grounded insights on the implications of AQRs that can shape the future of sharing economy platforms’ regulation. |
| Keywords: | sharing platforms, algorithmic quantity regulation, anticipatory effect, spillover effect, causal inference. |
| JEL: | L51 L86 R31 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11811 |
| By: | Leonardo Bursztyn (University of Chicago & NBER); Matthew Gentzkow (Stanford University & NBER); Rafael Jiménez-Durán (Bocconi University, IGIER, CESifo, & Chicago Booth Stigler Center); Aaron Leonard; Filip Milojević (University of Chicago); Christopher Roth (University of Cologne, NHH Norwegian School of Economics, Max Planck Institute for Research on Collective Goods, CESifo, & CEPR) |
| Abstract: | Market definition is essential for antitrust analysis, but challenging in settings with network effects, where substitution patterns depend on changes in network size. To address this challenge, we conduct an incentivized experiment to measure substitution patterns for TikTok, a popular social media platform. Our experiment, conducted during a time of high uncertainty about a potential U.S. TikTok ban, compares changes in the valuation of other social apps under individual and collective TikTok deactivations. Consistent with a simple framework, the valuations of alternative social apps increase more in response to a collective TikTok ban than to an individual TikTok deactivation. Our framework and estimates highlight that individual and collective treatments can even lead to qualitatively different conclusions about which alternative goods are substitutes. |
| Keywords: | Markets, Network Goods, Coordination, Collective Interventions |
| JEL: | D83 D91 P16 J15 |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:ajk:ajkdps:363 |
| By: | Pablo D. Azar; Adrian Casillas; Maryam Farboodi |
| Abstract: | In our previous Liberty Street Economics post, we introduced the decentralized finance (DeFi) intermediation chain and explained how various players have emerged as key intermediaries in the Ethereum ecosystem. In this post, we summarize the empirical results in our new Staff Report that explains how the need for transaction privacy across the DeFi intermediation chain gives rise to intermediaries’ market power. |
| Keywords: | financial intermediation; market power; decentralized finance |
| JEL: | G23 D82 L14 L22 G14 D43 |
| Date: | 2025–04–21 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fednls:99874 |
| By: | Oliver Tronn Scott-Simons; Chris Colman; FrostByte |
| Abstract: | Decentralised exchanges (DEXs) have transformed trading by enabling trustless, permissionless transactions, yet they face significant challenges such as impermanent loss and slippage, which undermine profitability for liquidity providers and traders. In this paper, we introduce QubitSwap, an innovative DEX model designed to tackle these issues through a hybrid approach that integrates an external oracle price with internal pool dynamics. This is achieved via a parameter $z$, which governs the balance between these price sources, creating a flexible and adaptive pricing mechanism. Through rigorous mathematical analysis, we derive a novel reserve function and pricing model that substantially reduces impermanent loss and slippage compared to traditional DEX frameworks. Notably, our results show that as $z$ approaches 1, slippage approaches zero, enhancing trading stability. QubitSwap marks a novel approach in DEX design, delivering a more efficient and resilient platform. This work not only advances the theoretical foundations of decentralised finance but also provides actionable solutions for the broader DeFi ecosystem. |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.06281 |
| By: | Nir Lavee; Noam Nisan; Mallesh Pai; Max Resnick |
| Abstract: | Blockchains have block-size limits to ensure the entire cluster can keep up with the tip of the chain. These block-size limits are usually single-dimensional, but richer multidimensional constraints allow for greater throughput. The potential for performance improvements from multidimensional resource pricing has been discussed in the literature, but exactly how big those performance improvements are remains unclear. In order to identify the magnitude of additional throughput that multi-dimensional transaction fees can unlock, we introduce the concept of an $\alpha$-approximation. A constraint set $C_1$ is $\alpha$-approximated by $C_2$ if every block feasible under $C_1$ is also feasible under $C_2$ once all resource capacities are scaled by a factor of $\alpha$ (e.g., $\alpha =2$ corresponds to doubling all available resources). We show that the $\alpha$-approximation of the optimal single-dimensional gas measure corresponds to the value of a specific zero-sum game. However, the more general problem of finding the optimal $k$-dimensional approximation is NP-complete. Quantifying the additional throughput that multi-dimensional fees can provide allows blockchain designers to make informed decisions about whether the additional capacity unlocked by multidimensional constraints is worth the additional complexity they add to the protocol. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.15438 |
| By: | Ma, Shuang (Guangzhou University); Mu, Ren (Texas A&M University) |
| Abstract: | This study investigates broadband internet's impact on rural-urban migration in China, using the Universal Broadband and Telecommunication Services pilot program as a quasi-experimental setting. Analyzing China Household Finance Survey data (2013-2021) through difference-in-differences estimation, we find that improved internet access significantly increased rural-urban migration. Effects were strongest in villages with initially low migrant populations, locations closer to county centers, and those with better road infrastructure. At the individual level, impacts were most pronounced among females, younger people, the more educated, and those from higher-income households. Increased attention to economic information, rather than enhanced e-commerce opportunities, appears to drive these migration decisions. Our findings suggest broadband creates “digital routes” facilitating outmigration rather than “digital roots” anchoring residents to rural areas. |
| Keywords: | migration, urbanization, information and communications technology, China |
| JEL: | O15 R2 L86 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17752 |
| By: | Bo Wu |
| Abstract: | Domestic and foreign scholars have conducted many studies on the influencing factors of digital inclusive finance, but few studies on the impact of the "Belt and Road" and the establishment of node cities on digital inclusive finance. Therefore, it is necessary to study the impact of the establishment of node cities in the "Belt and Road" on the development of digital inclusive finance. This paper mainly uses descriptive analysis and literature analysis to summarize and analyze the development status of China's digital inclusive finance and relevant theoretical research on the development of the "Belt and Road" Initiative, and analyzes and collates the background knowledge needed for the demonstration from the aspects of the development status of China's digital inclusive finance and the impact of digital inclusive finance on the economy. In this process, referring to the relevant economic theories, the theoretical model of this paper is proposed and the influence machine analysis is carried out. Empirically, this paper selects the development level of digital inclusive finance in 31 provinces in China from 2011 to 2020 as the explained variable, takes the establishment of "Belt and Road" node cities as a quasi-natural experiment, and verifies the promoting effect of the establishment of "Belt and Road" node cities on the development of digital inclusive finance in provinces through the differential differential method. And verify whether the level of Internet development is a mediating variable. The empirical results show that the establishment of node cities in the "Belt and Road" does promote the development of digital inclusive finance in provinces with the level of Internet development as an intermediary variable. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.15316 |
| By: | Mario R. Pinheiro; Mario J. Pinheiro |
| Abstract: | The proposed framework introduces a novel multidimensional representation of money using tensor analysis, enabling a more granular examination of economic interactions and capital flow. By treating money as a multidimensional entity, this approach allows for detailed tracking and modeling of sectoral, temporal, and agent-based dynamics. This enhanced perspective facilitates the design of adaptive economic policies that can effectively respond to evolving macroeconomic conditions, ensuring resilience and inclusivity in financial systems. Furthermore, the tensor-based modeling framework bridges traditional economic analyses with advanced computational techniques, offering a robust foundation for algorithmic governance and data-driven decision-making in complex economies. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.06286 |