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on Payment Systems and Financial Technology |
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Issue of 2024‒06‒17
twenty-six papers chosen by |
| By: | Olivier Armantier; Sebastian Doerr; Jon Frost; Andreas Fuster; Kelly Shue |
| Abstract: | Many digital applications rely on the willingness of users to voluntarily share personal data. Yet some users are more comfortable sharing data than others. To document these differences, we draw on questions to a representative sample of U.S. households added to the New York Fed's Survey of Consumer Expectations. We find that women a re less willing than men, and older individuals less willing than the young, to share their financial transaction data in exchange for better offers on financial services. Across these groups, there are significant differences in attitudes, such as willingness to take financial risks, concerns that data will become publicly available, and concerns around personal safety. Responses suggest that privacy regulation can increase the willingness to share data, but effects do not differ by gender. |
| Keywords: | data, privacy, CCPA, fintech, big tech, survey of consumer expectations |
| JEL: | C8 D8 |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:bis:biswps:1187&r= |
| By: | Marco Di Maggio; Pulak Ghosh; Soumya Kanti Ghosh; Andrew Wu |
| Abstract: | Interest in central bank digital currencies (CBDCs) has been burgeoning with 134 countries now exploring its implementation. In December 2022, India started its CBDC pilot program to continue its transition towards a digitized payments economy. This paper presents the first empirical analysis utilizing detailed transaction data to explore the dynamics between CBDCs and existing digital payment methods, as well as the implications of increased CBDC usage on traditional bank deposits. Our findings reveal that policies which increase transaction costs for current digital payment methods catalyze a substitution effect, bolstering CBDC adoption. Furthermore, an uptick in CBDC usage is associated with a notable decline in bank, cash, and savings deposits, suggesting potential paths to bank disintermediation. This study contributes critical insights into the evolving competition between digital currencies and established financial infrastructures, highlighting the transformative potential of CBDCs on the broader economy. |
| JEL: | E42 G21 G38 G51 |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:32457&r= |
| By: | Rim El Khoury; Nohad Nasrallah (LARGE - Laboratoire de Recherche en Gestion et Economie - UNISTRA - Université de Strasbourg); Khaled Hussainey; Rima Assaf |
| Abstract: | This study is epicentral to analyze the impact of the Russia–Ukraine war on the financial markets, specifically focusing on the connectedness and spillover dynamics of FinTech, Environmental, Social, and Governance (ESG), renewable energy, gold, and Morgan Stanley Capital International (MSCI) indices in developed and emerging countries. Data are collected from Thomson Reuters, ranging from May 8, 2020, to May 11, 2022, and a time-varying parameter vector autoregression (TVP-VAR) and the dynamic conditional correlation (DCC) generalized autoregressive conditional heteroskedasticity (GARCH) t-Copula (DCC-GARCH t-Copula) are used to analyze the data. The results show that FinTech, ESG, and MSCI are net transmitters in developed countries, whereas gold and renewable energy are net receivers pre- and during war periods. ESG and MSCI are net transmitters in emerging countries, while FinTech, renewable energy, and gold become net receivers in both periods. The hedging ratio sheds light on the costs and weights of efficient pair investments that might change in the context of each region and under the combined scenario. The study has important implications for merchant bankers, policymakers, investors, hedgers, and risk managers. |
| Date: | 2023–05–26 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04564870&r= |
| By: | Jung, Alexander |
| Abstract: | This study re-assesses the validity of the quantity theory of money (QTM) for the very long sample, 1870 to 2020, for 18 industrial countries using the dataset from Jordà et al. (2017). It considers structural changes in the economic and financial sectors and changes in monetary policy rameworks. Three findings are presented. First, the results from panel cointegration tests show that the long-run relationship between excess money growth and inflation holds if longer runs of data are used. Second, panel regressions confirm the presence of long and variable lags in the monetary policy transmission, as predicted by Milton Friedman. For the full sample, the average speed of adjustment from excess money growth to inflation in industrial countries was about two years amid heterogeneity across time and countries. Third, the results show that over recent decades, structural change - coinciding with the Great Moderation and, in part, reflecting changes in payment technologies - has led to a collapse of QTM. JEL Classification: B16, B23, E40, E50, N1 |
| Keywords: | excess money growth, great moderation, panel cointegration tests, payment technologies, structural change |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbwps:20242940&r= |
| By: | Ali Mohammadjafari |
| Abstract: | Prediction of stock prices has been a crucial and challenging task, especially in the case of highly volatile digital currencies such as Bitcoin. This research examineS the potential of using neural network models, namely LSTMs and GRUs, to forecast Bitcoin's price movements. We employ five-fold cross-validation to enhance generalization and utilize L2 regularization to reduce overfitting and noise. Our study demonstrates that the GRUs models offer better accuracy than LSTMs model for predicting Bitcoin's price. Specifically, the GRU model has an MSE of 4.67, while the LSTM model has an MSE of 6.25 when compared to the actual prices in the test set data. This finding indicates that GRU models are better equipped to process sequential data with long-term dependencies, a characteristic of financial time series data such as Bitcoin prices. In summary, our results provide valuable insights into the potential of neural network models for accurate Bitcoin price prediction and emphasize the importance of employing appropriate regularization techniques to enhance model performance. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.08089&r= |
| By: | Andrew F. Haughwout; Donghoon Lee; Daniel Mangrum; Joelle Scally; Wilbert Van der Klaauw; Crystal Wang |
| Abstract: | This morning, the New York Fed’s Center for Microeconomic Data released the Quarterly Report on Household Debt and Credit for the first quarter of 2024. Household debt balances grew by $184 billion over the previous quarter, slightly less than the moderate growth seen in the fourth quarter of 2023. Housing debt balances grew by $206 billion. Auto loans saw a $9 billion increase, continuing their steady growth since the second quarter of 2020, while balances on other non-housing debts fell. Credit card balances fell by $14 billion, which is typical for the first quarter. However, an increasing number of borrowers are behind on credit card payments. In this post, we explore the relationship between credit card delinquency and changes in credit card “utilization rates.” |
| Keywords: | Consumer Credit Panel (CCP); household finance |
| JEL: | D12 |
| Date: | 2024–05–14 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fednls:98231&r= |
| By: | Prachi Mishra; Alvaro Ortiz; Antonio Spilimbergo; Tomasa Rodrigo; Sirenia Vázquez |
| Abstract: | Joint project with the IMF to analyze the evolution of e-commerce in Spain during and after Covid. The secular positive trend in e-commerce is well alive. The pandemic\'s boost was temporary with a moderating trend once the restrictions were lifted. Significant heterogeneity by individuals and categories of consumption Joint project with the IMF to analyze the evolution of e-commerce in Spain during and after Covid. The secular positive trend in e-commerce is well alive. The pandemic\'s boost was temporary with a moderating trend once the restrictions were lifted. Significant heterogeneity by individuals and categories of consumption |
| Keywords: | Big Data, Big Data, Digital consumption, Consumo digital, Spain, España, Analysis with Big Data, Análisis con Big Data, Consumption, Consumo, Digital Economy, Economía Digital, Working Paper, Documento de Trabajo |
| JEL: | E00 L81 C55 |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:bbv:wpaper:2408&r= |
| By: | Ozili, Peterson K |
| Abstract: | The study presents a review of existing research on bank profitability determinants in Africa. Using the literature review methodology, it was found that bank profitability determinants differ across countries in all regions of Africa. Some suggested areas for future research are to explore the impact of circular economy, fintech activities, the pandemic, and economic uncertainty on the profitability of African banks. The implication of the review findings is that bank profitability determinants will remain a contested research agenda in the banking literature. |
| Keywords: | African banks, profitability determinants, banks, bank profitability, Europe, Africa, regulation |
| JEL: | G00 G20 G21 G28 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:120777&r= |
| By: | Kaiser, Tim (University of Kaiserslautern); Lusardi, Annamaria (Stanford University) |
| Abstract: | This article provides a concise narrative overview of the rapidly growing empirical literature on financial literacy and financial education. We first discuss stylized facts on the demographic correlates of financial literacy. We next cover the evidence on the effects of financial literacy on financial behaviors and outcomes. Finally, we review the evidence on the causal effects of financial education programs focusing on randomized controlled trial evaluations. The article concludes with perspectives on future research priorities for both financial literacy and financial education. |
| Keywords: | financial education, financial literacy, financial behavior |
| JEL: | G53 D14 |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp16926&r= |
| By: | Korhonen, Iikka; Newby, Elisa; Elonen-Kulmala, Jonna |
| Abstract: | This article looks into global central bank messaging on the Twitter social media platform. At the end of 2021, a total of 122 central banks and monetary authorities had registered accounts on Twitter At that time, approximately two-thirds of world's central banks and monetary author- ities were using Twitter. Drawing on a database of central bank tweets up to the end of 2021, we document Twitter interactions of central banks by such measures as influence, connections and hashtag use. In addition to similarities among central bank strategies, we also find striking differences in influence and willingness to connect with the public. Tweeting activity during the Covid-19 pandemic provides insight in central bank crisis responses. |
| Keywords: | central banks, communications, Twitter, Covid-19 |
| JEL: | E58 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:bofecr:294868&r= |
| By: | Joanna Stavins |
| Abstract: | Buy now, pay later (BNPL), a short-term, interest-free credit option for retail purchases, is becoming increasingly popular. At roughly 9 percent (as of fall 2023), the share of all consumers using BNPL is still relatively low, but it has grown by about 40 percent from two years earlier. This brief shows that BNPL use is significantly higher among financially vulnerable consumers and disproportionately high among women, Black, and Latino consumers. |
| Keywords: | consumer credit; payments; BNPL |
| JEL: | G21 G51 D14 E42 |
| Date: | 2024–05–22 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedbcq:98288&r= |
| By: | Kundan Mukhia; Anish Rai; SR Luwang; Md Nurujjaman; Sushovan Majhi; Chittaranjan Hens |
| Abstract: | This paper identifies the cryptocurrency market crashes and analyses its dynamics using the complex network. We identify three distinct crashes during 2017-20, and the analysis is carried out by dividing the time series into pre-crash, crash, and post-crash periods. Partial correlation based complex network analysis is carried out to study the crashes. Degree density ($\rho_D$), average path length ($\bar{l}$), and average clustering coefficient ($\overline{cc}$) are estimated from these networks. We find that both $\rho_D$ and $\overline{cc}$ are smallest during the pre-crash period, and spike during the crash suggesting the network is dense during a crash. Although $\rho_D$ and $\overline{cc}$ decrease in the post-crash period, they remain higher than pre-crash levels for the 2017-18 and 2018-19 crashes suggesting a market attempt to return to normalcy. We get $\bar{l}$ is minimal during the crash period, suggesting a rapid flow of information. A dense network and rapid information flow suggest that during a crash uninformed synchronized panic sell-off happens. However, during the 2019-20 crash, the values of $\rho_D$, $\overline{cc}$, and $\bar{l}$ did not vary significantly, indicating minimal change in dynamics compared to other crashes. The findings of this study may guide investors in making decisions during market crashes. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.05642&r= |
| By: | Maria S. Mavillonio |
| Abstract: | In this paper, we leverage recent advancements in large language models to extract information from business plans on various equity crowdfunding platforms and predict the success of firm campaigns. Our approach spans a broad and comprehensive spectrum of model complexities, ranging from standard textual analysis to more intricate textual representations - e.g. Transformers-, thereby offering a clear view of the challenges in understanding of the underlying data. To this end, we build a novel dataset comprising more than 640 equity crowdfunding campaigns from major Italian platforms. Through rigorous analysis, our results indicate a compelling correlation between the use of intricate textual representations and the enhanced predictive capacity for identifying successful campaigns. |
| Keywords: | Crowdfunding, Text Representation, Natural Language Processing, Transformers |
| JEL: | C45 C53 G23 L26 |
| Date: | 2024–05–01 |
| URL: | http://d.repec.org/n?u=RePEc:pie:dsedps:2024/308&r= |
| By: | Fujiwara, Thomas (Princeton University, Department of Economics and SPIA, and NBER); Muller, Karsten (National University of Singapore, Business School); Schwarz, Carlo (Universita Bocconi, Department of Economics and IGIER, and PERICLES) |
| Abstract: | We study how social media affects election outcomes in the United States. We use variation in the number of Twitter users across counties induced by early adopters at the 2007 South by Southwest (SXSW) festival, a key event in Twitter’s rise to popularity. We show that this variation is unrelated to observable county characteristics and electoral outcomes before the launch of Twitter. Our results indicate that Twitter lowered the Republican vote share in the 2016 and 2020 presidential elections, but had limited effects on Congressional elections and previous presidential elections. Evidence from survey data, primary elections, and text analysis of millions of tweets suggests that Twitter’s relatively liberal content may have persuaded voters with moderate views to vote against Donald Trump. |
| Keywords: | JEL Classification: |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:cge:wacage:700&r= |
| By: | Ayse D. Lokmanoglu; Carol K. Winkler; Kareem El Damanhoury; Virginia Massignan; Esteban Villa-Turek; Keyu Alexander Chen |
| Abstract: | With globalization's rise, economic interdependence's impacts have become a prominent factor affecting personal lives, as well as national and international dynamics. This study examines RT's public diplomacy efforts on its non-Russian Facebook accounts over the past five years to identify the prominence of economic topics across language accounts. Computational analysis, including word embeddings and statistical methods, investigates how offline economic indicators, like currency values and oil prices, correspond to RT's online economic content changes. The results demonstrate that RT uses message reinforcement associated economic topics as an audience targeting strategy and differentiates their use with changing currency and oil values. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.01798&r= |
| By: | Maddalena Davoli; Uschi Backes-Gellner |
| Abstract: | We analyse financial literacy in Switzerland and its relationship with various economic outcomes using novel survey data collected in 2023. While the overall financial literacy levels are high in an international comparison, with over 50% of the respondents correctly answering the Big Three financial literacy questions about interest, inflation and risk diversification, there is significant heterogeneity within the population. Women, the young, the less educated and French and Italian-speaking respondents exhibit particularly low financial literacy. Women have lower financial literacy than men. The young have lower financial literacy than respondents in their working age, and Italian-speaking respondents exhibit particularly low financial literacy as compared to French- or German-speaking respondents. Financial literacy is also correlated with higher educational levels. We also find that financial literacy is positively linked to respondents' ability to cope with adverse economic outcomes: more financially literate individuals are better able to manage their expenses, save, and face economic shocks. |
| Keywords: | inancial literacy, personal finance, financial fragility, Switzerland |
| JEL: | G53 D1 I3 |
| Date: | 2024–05 |
| URL: | https://d.repec.org/n?u=RePEc:iso:educat:0220&r= |
| By: | Sofia Priazhkina; Samuel Palmer; Pablo Martín-Ramiro; Román Orús; Samuel Mugel; Vladimir Skavysh |
| Abstract: | We build a network formation game of firms with trade flows to study the adoption and usage of a new digital currency as an alternative to correspondent banking. We document endogenous heterogeneity and inefficiency in adoption outcomes and explain why higher usage may correspond to lower adoption. Next, we frame the model as a quadratic unconstrained binary optimization (QUBO) problem and apply it to data. Method-wise, QUBO presents an extension to the potential function approach and makes broadly defined network games applicable and empirically feasible, as we demonstrate with a quantum computer. |
| Keywords: | Central bank research; Digital currencies and fintech; Digitalization; Economic models; Financial institutions; Payment clearing and settlement systems; Sectoral balance sheet |
| JEL: | E21 E44 E62 G51 |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:bca:bocawp:24-17&r= |
| By: | Christopher J. Waller |
| Date: | 2024–05–17 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgsq:98254&r= |
| By: | Abdoulaye Ndiaye |
| Abstract: | This paper studies the optimal transaction fee mechanisms for blockchains, focusing on the distinction between price-based ($\mathcal{P}$) and quantity-based ($\mathcal{Q}$) controls. By analyzing factors such as demand uncertainty, validator costs, cryptocurrency price fluctuations, price elasticity of demand, and levels of decentralization, we establish criteria that determine the selection of transaction fee mechanisms. We present a model framed around a Nash bargaining game, exploring how blockchain designers and validators negotiate fee structures to balance network welfare with profitability. Our findings suggest that the choice between $\mathcal{P}$ and $\mathcal{Q}$ mechanisms depends critically on the blockchain's specific technical and economic features. The study concludes that no single mechanism suits all contexts and highlights the potential for hybrid approaches that adaptively combine features of both $\mathcal{P}$ and $\mathcal{Q}$ to meet varying demands and market conditions. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.00235&r= |
| By: | Michelle W. Bowman |
| Date: | 2024–05–15 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgsq:98242&r= |
| By: | Theodore Panagiotidis (Department of Economics, University of Macedonia); Georgios Papapanagiotou (Department of Economics, University of Macedonia) |
| Abstract: | We aim to identify the determinants of non-fungible tokens (NFTs) returns. The ten most popular NFTs based on their price, trading volume, and market capitalisation are examined. Twenty-three potential drivers of the returns of each NFT are considered. We employ a Bayesian LASSO model which takes into account stochastic volatility and leverage effect. The results indicate that NFTs returns are primarily driven by volatility and ethereum returns. We find a weak connection between NFTs returns and conventional assets, such as stock, oil, and gold markets. |
| JEL: | C11 C22 G12 G15 |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:mcd:mcddps:2024_02&r= |
| By: | Ajit Desai; Anneke Kosse; Jacob Sharples |
| Abstract: | We propose a flexible machine learning (ML) framework for real-time transaction monitoring in high-value payment systems (HVPS), which are a central piece of a country’s financial infrastructure. This framework can be used by system operators and overseers to detect anomalous transactions, which—if caused by a cyber attack or an operational outage and left undetected—could have serious implications for the HVPS, its participants and the financial system more broadly. Given the substantial volume of payments settled each day and the scarcity of actual anomalous transactions in HVPS, detecting anomalies resembles an attempt to find a needle in a haystack. Therefore, our framework uses a layered approach. In the first layer, a supervised ML algorithm is used to identify and separate “typical” payments from “unusual” payments. In the second layer, only the unusual payments are run through an unsupervised ML algorithm for anomaly detection. We test this framework using artificially manipulated transactions and payments data from the Canadian HVPS. The ML algorithm employed in the first layer achieves a detection rate of 93%, marking a significant improvement over commonly used econometric models. Moreover, the ML algorithm used in the second layer marks the artificially manipulated transactions as nearly twice as suspicious as the original transactions, proving its effectiveness. |
| Keywords: | Digital currencies and fintech; Financial institutions; Financial services; Financial system regulation and policies; Payment clearing and settlement systems |
| JEL: | C45 C55 D83 E42 |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:bca:bocawp:24-15&r= |
| By: | OECD |
| Abstract: | Data plays an increasingly important role for online platforms and the majority of digital business models. Along with data becoming central to competition and the conduct of actors in digital markets, there has been an increase in data privacy regulations and enforcement worldwide. The interplay between competition and data privacy has prompted questions about whether data privacy and the collection of consumers’ data constitute an antitrust issue. Should competition considerations be factored into decisions by data protection authorities, and, if so, how can synergies between the two policy areas be enhanced and tensions overcome? This paper explores the links between competition and data privacy, their respective objectives, and how considerations pertaining to one policy area have been, or could be, included into the other. It investigates enforcement interventions and regulatory measures that could foster synergies or lead to potential challenges, and offers insights into models for co-operation between competition and data protection authorities. This is a joint working paper from the OECD Competition and Digital Economy Policy Secretariat. |
| Date: | 2024–06–07 |
| URL: | https://d.repec.org/n?u=RePEc:oec:dafaac:310-en&r= |
| By: | Gen Norman Thomas; Siti Mutiara Ramadhanti Nur; Lely Indriaty |
| Abstract: | This study aims to analyze the impact of financial literacy, social capital and financial technology on financial inclusion. The research method used a quantitative research method, in which questionnaires were distributed to 100 active students in the economics faculty at 7 private colleges in Tangerang, Indonesia. Based on the results of data processing using SPSS version 23, it results that financial literacy, social capital and financial technology partially have a positive and significant influence on financial inclusion. The results of this study provide input that financial literacy needs to be increased because it is not yet equivalent to financial inclusion, and reducing the gap between financial literacy and financial inclusion is only 2.74%. Another benefit of this research is to give an understanding to students that students should be independent actors or users of financial technology products and that students should become pioneers in delivering financial knowledge, financial behavior and financial attitudes to the wider community. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.06570&r= |
| By: | Michael S. Barr |
| Date: | 2024–05–20 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgsq:98259&r= |
| By: | Lattermann, Fritz Günther |
| Abstract: | This dissertation addresses the complex relationship between transparency, financial statements, and regulatory oversight in the global financial landscape. Four chapters explore different facets of this complex interaction. Chapter 2 analyzes the legislative process of the Financial Market Integrity Strengthening Act, focusing on the restructuring of the enforcement framework. The analysis shows that the legislator only incorporated the professionals' recommendations to a limited extent into the final legislation, and that the law appears to place the main responsibility for the failures in the Wirecard case on the FREP even before the conclusion of the legal proceedings. Chapter 3 examines the impact of enforcement costs on voluntary delisting and downlisting decisions in Germany, thus contributing to the discourse on the trade-off between listing incentives and costs. The results suggest that an error announcement itself is not a driving factor. Rather, the analysis points to the enforcement process itself as a significant financial burden for firms. Chapter 4 focuses on fair value adjustments on investment property and its characteristics as a recurring income that has at the same time non-recurring characteristics. The results indicate limited positive reactions to positive earnings surprises when fair value adjustments materially affect reported earnings. This suggests that fair value adjustments, having characteristics of non-recurring earnings, are viewed by investors with heightened uncertainty and perceived risk due to their uncertain translation into future cash flows, while such uncertainty is reduced in transparent real estate markets. The final chapter shifts to the NFT market, which operates in an unregulated but transparent environment. The analysis suggests that experienced investors outperform, indicating a degree of information inefficiency. Moreover, traders with more transactions may benefit more from pump-and-dump schemes, highlighting the need for a regulatory framework despite theoretical transparency. |
| Date: | 2024–04–26 |
| URL: | http://d.repec.org/n?u=RePEc:dar:wpaper:144607&r= |