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on Payment Systems and Financial Technology |
| By: | Marian Gidea; Daniel Goldsmith; Yuri Katz; Pablo Roldan; Yonah Shmalo |
| Abstract: | We analyze the time series of four major cryptocurrencies (Bitcoin, Ethereum, Litecoin, and Ripple) before the digital market crash at the end of 2017 - beginning 2018. We introduce a methodology that combines topological data analysis with a machine learning technique -- $k$-means clustering -- in order to automatically recognize the emerging chaotic regime in a complex system approaching a critical transition. We first test our methodology on the complex system dynamics of a Lorenz-type attractor, and then we apply it to the four major cryptocurrencies. We find early warning signals for critical transitions in the cryptocurrency markets, even though the relevant time series exhibit a highly erratic behavior. |
| Date: | 2018–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1809.00695&r=pay |
| By: | George Milunovich |
| Abstract: | We investigate connectedness within and across two major groups or assets: i) five popular cryptocurrencies, and ii) six major asset classes plus two commonly employed risk factors. Granger-causality tests uncover six direct channels of causality from the elements of the mainstream assets/risk factors group to digital assets. On the other hand there are two statistically significant causal links going in the other direction. In order to provide some perspective on the magnitude of the uncovered linkages we supplement the analysis by estimating networks from forecast error variance decompositions. The estimated connectedness within the groups is relatively large, whereas the linkages across the two groups are small in comparison. Namely, less than 2.2 percent of future uncertainty of any cryptocurrency is sourced from all non-crypto assets combined, while the joint contribution of all digital assets to non-crypto uncertainty does not exceed 1.5 percent. |
| Date: | 2018–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1809.03072&r=pay |
| By: | Ozili, Peterson K |
| Abstract: | This article provides a discussion on some issues in blockchain finance that regulators are concerned about – an area which bitcoin promoters have remained silent about. Blockchain technology in finance has several benefits for financial intermediation in the financial system; notwithstanding, several issues persist which if addressed can make the adoption of blockchain technology in finance easier and accepted by regulators. The blockchain issues discussed in this article are relevant for recent debates in blockchain finance. |
| Keywords: | Bitcoin; Blockchain; Finance, Fintech, Financial stability, Financial intermediation; Financial institutions; Banking regulation |
| JEL: | G21 G23 G28 |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:88811&r=pay |
| By: | David Yermack |
| Abstract: | The FinTech sector has begun to grow rapidly in sub-Saharan Africa. I document far greater adoption of social media, digital currency, ride sharing, and other FinTech applications in countries with a common law legal heritage compared to those with a civil law system, suggesting that legal origin plays a critical role in setting the stage for growth through entrepreneurship in the developing world. The electrical, telecom, and Internet infrastructure required for FinTech has been built out more extensively in common law countries. Financial inclusion outcomes are also better in emerging markets that have a common law heritage. |
| JEL: | O14 O17 O30 O55 R00 |
| Date: | 2018–09 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:25007&r=pay |
| By: | Meng Liu; Erik Brynjolfsson; Jason Dowlatabadi |
| Abstract: | Digital platforms like Uber can enhance market transparency and mitigate moral hazard via ratings of buyers and sellers, real-time monitoring, and low-cost complaint channels. We compare driver choices at Uber with taxis by matching trips so they are subject to the same optimal route. We also study drivers who switch from taxis to Uber. We find: (1) drivers in taxis detour about 7% on airport routes, with non-local passengers experiencing longer detours; (2) these detours lead to longer travel times; and (3) drivers on the Uber platform are more likely to detour on airport routes with high surge pricing. |
| JEL: | D8 D86 L15 L91 M52 |
| Date: | 2018–09 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:25015&r=pay |
| By: | GAUTIER Axel, (Université de Liège and CORE); SOMOGYI Robert, (CORE, Université catholique de Louvain) |
| Abstract: | This paper analyzes two business practices on the mobile internet market, paid prioritization and zero-rating. Both violate the principle of net neutrality by allowing the internet service provider to discriminate different content types. In recent years these practices have attracted considerable media attention and regulatory interest. The EU, and until recently the US have banned paid prioritization but tolerated zero-rating under conditions. With prioritization, the ISP delivers content at different speeds and it is equivalent to a discrimination in terms of quality. With zero-rating, the ISP charges different prices for content and it is equivalent to a discrimination in terms of prices. We first show that neither of these practices lead to the exclusion of a content provider, a serious concern of net neutrality advocates. The ISP chooses prioritization when traffic is highly valuable for content providers and congestion is severe, and zero-rating in all other cases. Furthermore, investment in network capacity is suboptimal in the case of prioritization and socially optimal under zero-rating. |
| Keywords: | net neutrality, paid prioritization, zero-rating, sponsored data, data cap, congestion |
| JEL: | D21 L12 L51 L96 |
| Date: | 2018–09–03 |
| URL: | http://d.repec.org/n?u=RePEc:cor:louvco:2018023&r=pay |
| By: | Paul Belleflamme (Aix-Marseille Univ., CNRS, EHESS, Centrale Marseille, AMSE); Martin Peitz (Department of Economics and MaCCI, University of Mannheim) |
| Abstract: | On many two-sided platforms, users on one side not only care about user participation and usage levels on the other side, but they also care about participation and usage of fellow users on the same side. Most prominent is the degree of seller competition on a platform catering to buyers and sellers. In this paper, we address how seller competition affects platform pricing, product variety, and the number of platforms that carry trade. |
| Keywords: | network effects, two-sided markets, platform competition, intermediation, pricing, Imperfect Competition |
| JEL: | D43 L13 L86 |
| Date: | 2018–06 |
| URL: | http://d.repec.org/n?u=RePEc:aim:wpaimx:1820&r=pay |
| By: | Tommy Andersson (Lund University, Department of Economics); Agnes Cseh (Hungarian Academy of Sciences, Centre for Economic and Regional Studies, Institute of Economics); Lars Ehlers (Université de Montréal, Département de Sciences Économiques); Albin Erlanson (Stockholm School of Economics, Department of Economics) |
| Abstract: | A time bank is a group of individuals and/or organizations in a local community that set up a common platform to trade services among themselves. There are several well-known problems associated with this type of banking, e.g., high overhead costs for record keeping and difficulties to identify feasible trades. This paper demonstrates that these problems can be solved by organizing time banks as a centralized matching market and, more specifically, by organizing trades based on a non-manipulable mechanism that selects an individually rational and time-balanced allocation which maximizes exchanges among the members of the time bank (and those allocations are efficient). Such a mechanism does not exist on the general preference domain but on a smaller yet natural domain where agents classify services as unacceptable and acceptable (and for those services agents have specific upper quotas representing their maximum needs). On the general preference domain, it is demonstrated that the proposed mechanism at least can prevent some groups of agents from manipulating the mechanism without dispensing individual rationality, efficiency, or time-balance. |
| Keywords: | market design; time banking; priority mechanism; non-manipulability |
| JEL: | D82 |
| Date: | 2018–08 |
| URL: | http://d.repec.org/n?u=RePEc:has:discpr:1818&r=pay |
| By: | Natasha Merat (Institute for Transport Studies); Ruth Madigan (Institute for Transport Studies); Sina Nordhoff (Delft University of Technology) |
| Abstract: | This paper provides an overview of the social-psychological factors that are likely to influence the trust and acceptance of shared SAE Level 4 Automated Vehicles (AVs). It begins with a short summary of what influences users’ engagement in ride-sharing for conventional vehicles, followed by the factors that affect user acceptance and trust of robotic systems. Using studies of human robot interaction (HRI), recommendations are made on how to improve users’ trust, acceptance and use of shared AVs. Results from real-world studies and on-line surveys provide some contradictory views regarding willingness to accept and use the systems, which may be partly due to the fact that on-line users have not had actual interactions with AVs. We recommend that the pathway to adoption and acceptance of AVs should be incremental and iterative, providing users with hands-on experience of the systems at every stage. This removes unrealistic, idealised, expectations, which can ultimately hamper acceptance. Manufacturers may also use new technologies, social-networks and crowd-sourcing techniques to receive feedback and input from consumers themselves, in order to increase adoption and acceptance of shared AVs. |
| Date: | 2017–07–20 |
| URL: | http://d.repec.org/n?u=RePEc:oec:itfaab:2017/10-en&r=pay |
| By: | WATANABE Toshiya; HIRAI Yuri; AKUTSU Masami; HIOKI Tomomi; NAGAI Norihito |
| Abstract: | As the Internet of Things (IoT), big data, and artificial intelligence (AI) progress in the Fourth Industrial Revolution, data are expected to bring innovative outcomes. Against this background, a questionnaire survey was conducted on 6,278 firms with the aim of grasping the actual situation of data utilization in Japan and the important factors in obtaining outcomes through its use, with 562 effective responses collected. The results of our analysis reveal that, in order to obtain outcomes by utilizing data, it is important to master the contract model, design data sufficiently, and interact smoothly with stakeholders. In addition, we prepare three model cases of businesses using machine learning. We then examine rational and practical contracts on data utilization to provide commercially useful services, and organize issues. |
| Date: | 2018–09 |
| URL: | http://d.repec.org/n?u=RePEc:eti:rdpsjp:18028&r=pay |
| By: | Enwei Zhu; Stanislav Sobolevsky |
| Abstract: | Urban house prices are strongly associated with local socioeconomic factors. In literature, house price modeling is based on socioeconomic variables from traditional census, which is not real-time, dynamic and comprehensive. Inspired by the emerging concept of "digital census" - using large-scale digital records of human activities to measure urban population dynamics and socioeconomic conditions, we introduce three typical datasets, namely 311 complaints, crime complaints and taxi trips, into house price modeling. Based on the individual housing sales data in New York City, we provide comprehensive evidence that these digital census datasets can substantially improve the modeling performances on both house price levels and changes, regardless whether traditional census is included or not. Hence, digital census can serve as both effective alternatives and complements to traditional census for house price modeling. |
| Date: | 2018–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1809.03834&r=pay |
| By: | Hunt Allcott; Matthew Gentzkow; Chuan Yu |
| Abstract: | We measure trends in the diffusion of misinformation on Facebook and Twitter between January 2015 and July 2018. We focus on stories from 570 sites that have been identified as producers of false stories. Interactions with these sites on both Facebook and Twitter rose steadily through the end of 2016. Interactions then fell sharply on Facebook while they continued to rise on Twitter, with the ratio of Facebook engagements to Twitter shares falling by approximately 60 percent. We see no similar pattern for other news, business, or culture sites, where interactions have been relatively stable over time and have followed similar trends on the two platforms both before and after the election. |
| Date: | 2018–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1809.05901&r=pay |