nep-pay New Economics Papers
on Payment Systems and Financial Technology
Issue of 2018‒05‒21
seventeen papers chosen by
Bernardo Bátiz-Lazo
Bangor University

  1. Network-based indicators of Bitcoin bubbles By Alexandre Bovet; Carlo Campajola; Jorge F. Lazo; Francesco Mottes; Iacopo Pozzana; Valerio Restocchi; Pietro Saggese; Nicol\'o Vallarano; Tiziano Squartini; Claudio J. Tessone
  2. On the Rise of FinTechs – Credit Scoring using Digital Footprints By Tobias Berg; Valentin Burg; Ana Gombović; Manju Puri
  3. The governance of blockchain financial networks By Paech, Philipp
  4. Bitcoin is not the New Gold - A Comparison of Volatility, Correlation, and Portfolio Performance By Thomas Walther; Tony Klein; Hien Pham Thu;
  5. Strengthened competition in payment services By Demary, Markus; Rusche, Christian
  6. Honesty in the Digital Age By Alain Cohn; Tobias Gesche; Michel André Maréchal
  7. Promises undone: How committed pledges impact donations to charity By Toke R. Fosgaard; Adriaan R. Soetevent
  8. Gains from Digitization: Evidence from Gift-Giving in Music By Dogan, Pinar; Bourreau, Marc
  9. The Internet of Things and Information Fusion: Who Talks to Who? By Saghafian, Soroush; Tomlin, Brian; Biller, Stephan
  10. Cost of Experimentation and the Evolution of Venture Capital By Michael Ewens; Ramana Nanda; Matthew Rhodes-Kropf
  11. Financial literacy and inclusive growth in the European Union By Uuriintuya Batsaikhan; Maria Demertzis
  12. Sentiment-Based Prediction of Alternative Cryptocurrency Price Fluctuations Using Gradient Boosting Tree Model By Tianyu Ray Li; Anup S. Chamrajnagar; Xander R. Fong; Nicholas R. Rizik; Feng Fu
  13. Money and the Commons: An Investigation of Complementary Currencies and their Ethical Implications By Camille Meyer; Marek Hudon
  14. Piecewise Solutions to Big Data By Henry Chacon; Anuradha Roy
  15. Communications Technology and Terrorism By Rafat Mahmood; Michael Jetter
  16. The Hidden Predictive Power of Cryptocurrencies: Evidence from US Stock Market By Kazeem Isah; Ibrahim D. Raheem
  17. A Dynamical Systems Approach to Cryptocurrency Stability By Carey Caginalp

  1. By: Alexandre Bovet; Carlo Campajola; Jorge F. Lazo; Francesco Mottes; Iacopo Pozzana; Valerio Restocchi; Pietro Saggese; Nicol\'o Vallarano; Tiziano Squartini; Claudio J. Tessone
    Abstract: The functioning of the cryptocurrency Bitcoin relies on the open availability of the entire history of its transactions. This makes it a particularly interesting socio-economic system to analyse from the point of view of network science. Here we analyse the evolution of the network of Bitcoin transactions between users. We achieve this by using the complete transaction history from December 5th 2011 to December 23rd 2013. This period includes three bubbles experienced by the Bitcoin price. In particular, we focus on the global and local structural properties of the user network and their variation in relation to the different period of price surge and decline. By analysing the temporal variation of the heterogeneity of the connectivity patterns we gain insights on the different mechanisms that take place during bubbles, and find that hubs (i.e., the most connected nodes) had a fundamental role in triggering the burst of the second bubble. Finally, we examine the local topological structures of interactions between users, we discover that the relative frequency of triadic interactions experiences a strong change before, during and after a bubble, and suggest that the importance of the hubs grows during the bubble. These results provide further evidence that the behaviour of the hubs during bubbles significantly increases the systemic risk of the Bitcoin network, and discuss the implications on public policy interventions.
    Date: 2018–05
  2. By: Tobias Berg; Valentin Burg; Ana Gombović; Manju Puri
    Abstract: We analyze the information content of the digital footprint – information that people leave online simply by accessing or registering on a website – for predicting consumer default. Using more than 250,000 observations, we show that even simple, easily accessible variables from the digital footprint equal or exceed the information content of credit bureau (FICO) scores. Furthermore, the discriminatory power for unscorable customers is very similar to that of scorable customers. Our results have potentially wide implications for financial intermediaries’ business models, for access to credit for the unbanked, and for the behavior of consumers, firms, and regulators in the digital sphere.
    JEL: D12 G20 O33
    Date: 2018–04
  3. By: Paech, Philipp
    Abstract: Since the emergence of the virtual currency Bitcoin in 2009, a new, Internet-based way of recording entitlements and enforcing rights has increasingly captured the interest of businesses and governments. The technology is commonly called ‘blockchain’ and is often associated with a closely related phenomenon, the ‘smart contract’. The market is now exploring ways of using these concepts for financial assets, such as securities, legal tender and derivative contracts. This article develops a conceptual framework for the governance of blockchain-based networks in financial markets. It constructs a vision of how financial regulation and private law should set the boundaries of this new technology in order to protect market participants and societies at large, while at the same time allowing for the necessary room for innovation.
    Keywords: blockchain technology; fintech; financial assets; financial regulation; private law; private international law
    JEL: K11 K12 K22 K33
    Date: 2016–11–30
  4. By: Thomas Walther; Tony Klein; Hien Pham Thu;
    Abstract: Cryptocurrencies such as Bitcoin are establishing themselves as an investment asset and are often named the New Gold. This study, however, shows that the two assets could barely be more di?erent. Firstly, we analyze and compare conditional variance properties of Bitcoin and Gold as well as other assets and ?nd di?erences in their structure. Secondly, we implement a BEKK-GARCH model to estimate time-varying conditional correlations. Gold plays an important role in ?nancial markets with ?ight-to-quality in times of market distress. Our results show that Bitcoin behaves as the exact opposite and it positively correlates with downward markets. Lastly, we analyze the properties of Bitcoin as portfolio component and ?nd no evidence for hedging capabilities. We conclude that Bitcoin and Gold feature fundamentally di?erent properties as assets and linkages to equity markets. Our results hold for the broad cryptocurrency index CRIX. As of now, Bitcoin does not re?ect any distinctive properties of Gold other than asymmetric response in variance.
    Keywords: BEKK, Bitcoin, CRIX, Cryptocurrency, Gold, GARCH, Conditional Correlation, Asymmetry, Long memory
    JEL: C10 C58 G11
    Date: 2018–03
  5. By: Demary, Markus; Rusche, Christian
    Abstract: Starting on January 13, 2018, the Second Payment Services Directive (PSD2) will apply in the European Union. Among other things, the Directive's aim is to adapt regulation to the innovations in payment services and to promote the Single Market for non-cash payments. However, PSD2 will only strengthen competition between payment services under a common standard for the access to banking accounts.
    Date: 2018
  6. By: Alain Cohn; Tobias Gesche; Michel André Maréchal
    Abstract: Modern communication technologies enable efficient exchange of information, but often sacrifice direct human interaction inherent in more traditional forms of communication. This raises the question of whether the lack of personal interaction induces individuals to exploit informational asymmetries. We conducted two experiments with 866 subjects to examine how human versus machine interaction influences cheating for financial gain. We find that individuals cheat significantly more when they interact with a machine rather than a person, regardless of whether the machine is equipped with human features. When interacting with a human, individuals are particularly reluctant to report unlikely favorable outcomes, which is consistent with social image concerns. The second experiment shows that dishonest individuals prefer to interact with a machine when facing an opportunity to cheat. Our results suggest that human interaction is key to mitigating dishonest behavior and that self-selection into communication channels can be used to screen for dishonest people.
    Keywords: cheating, honesty, private information, communication, digitization, lying costs
    JEL: C99 D82 D83
    Date: 2018
  7. By: Toke R. Fosgaard (Department of Food and Resource Economics, University of Copenhagen); Adriaan R. Soetevent (Tinbergen Institute, University of Groningen)
    Abstract: The declining use of cash in society urges charities to experiment with digital payment instruments in their o -line fund raising activities. Cash and card payments di er in that the latter do not require individuals to donate at the time of the ask, disconnecting the decision to give from the act of giving. Evidence shows that people who say they will give mostly do not follow through. Our theory shows that having people to formally state the intended amount may alleviate this problem. We report on a field experiment the results of which show that donors who have pledged an amount are indeed more likely to follow through. The firmer the pledge, the more closely the amount donated matches the amount that was pledged. 45% of all participants however refuses to pledge. This proves that donors value exibility over commitment in intertemporal charitable giving.
    Keywords: Charitable fundraising, Field experiment, Image motivation
    JEL: C93 D64 D91 H41
    Date: 2018–05
  8. By: Dogan, Pinar (Harvard University); Bourreau, Marc (Telecom Paris Tech)
    Abstract: In this paper, we focus on recorded music gifts during the holiday season and estimate the reduction in deadweight loss due to the transition from physical CD gift-giving to digital music gift-giving with gift cards. Based on our survey data, we find that music CD gifts generate an average deadweight loss between 15 and 38 percent of the price. According to our estimates of gift music album sales which are based on U.S. data, the welfare gains from digitization, in terms of eliminated deadweight loss as a percentage of total spending on music albums, were between 5 to 13 percent during the week when digital sales peak in 2014.
    JEL: D10 L82 O33
    Date: 2018–02
  9. By: Saghafian, Soroush (Harvard University); Tomlin, Brian (Dartmouth College); Biller, Stephan (IBM Technology)
    Abstract: The promised operational benefits of the Internet of Things (IoT) are predicated on the notion that better decisions will be enabled through a multitude of autonomous sensors (often deployed by different firms) providing real-time knowledge of the state of things. This knowledge will be imperfect, however, due to sensor quality limitations. A sensor can improve its estimation quality by soliciting a state estimate from other sensors operating in its general environment. Target selection (choosing from which other sensors to solicit estimates) is challenging because sensors may not know the underlying inference models or qualities of sensors deployed by other firms. This lack of trust (or familiarity) in others’ inference models creates noise in the received estimate, but trust builds and noise reduces over time the more a sensor targets any given sensor. We characterize the initial and long run information sharing network for an arbitrary collection of sensors operating in an autoregressive environment. The state of the environment plays a key role in mediating quality and trust in target selection. When qualities are known and asymmetric, target selection is based on a deterministic rule that incorporates qualities, trusts, and state. Furthermore, each sensor eventually settles on a constant target set in all future periods, but this long run target set is sample path dependent and also varies by sensor. When qualities are unknown, a deterministic target selection rule may be suboptimal, and sensors may not settle on a constant target set. Moreover, the inherent targeting trade-off between quality and trust is influenced by a sensor’s ambiguity attitude. Our findings shed light on the evolution of inter-firm sensor communication over time, and this is important for predicting and understanding the inter-firm connectedness and relationships that will arise as a result of the IoT.
    Date: 2018–02
  10. By: Michael Ewens; Ramana Nanda; Matthew Rhodes-Kropf
    Abstract: We study how technological shocks to the cost of starting new businesses have led the venture capital model to adapt in fundamental ways over the prior decade. We both document and provide a framework to understand the changes in the investment strategy of venture capitalists (VCs) in recent years — an increased prevalence of a “spray and pray” investment approach — where investors provide a little funding and limited governance to an increased number of startups that they are more likely to abandon, but where initial experiments significantly inform beliefs about the future potential of the venture. This adaptation and related entry by new financial intermediaries has led to a disproportionate rise in innovations where information on future prospects is revealed quickly and cheaply, and reduced the relative share of innovation in complex technologies where initial experiments cost more and reveal less.
    JEL: G24 O31 O32
    Date: 2018–04
  11. By: Uuriintuya Batsaikhan; Maria Demertzis
    Abstract: Growing financialization and complexity demands financial literacy to be an integral part of the research agenda and policy design globally. It applies particularly to developed countries, since research findings suggest that financial literacy becomes more important with higher levels of economic development. Financial literacy is financial education, such as basic economics, statistics and numeracy skills combined with the ability to employ these skills in making financial decisions. Research has shown that as people become more financially literate, they make better saving and borrowing decisions, are more likely to plan for retirement and hold more diverse assets in their balance sheet. As more and more households are asked to make their own decisions about such issues, financial illiteracy can become a serious threat to their life-time welfare. The European Union contains in itself the world’s best performers (Sweden, Denmark) as well as those that score below global average (Romania, Portugal) in financial literacy rankings. The findings for the EU echo those that are also applicable to other developed economies, namely that low-income individuals, women, young people and less educated people tend to consistently underperform in literacy tests. Financial literacy matters for the EU for three reasons - 1) in the face of rapidly ageing population, the pressure on the pension system could be mitigated through shifting towards more occupational and personal insurance systems. This shifts more and more responsibilities to the individual who can greatly enhance their decision-making with higher levels of financial literacy. 2) mortgage-debt makes up an overwhelming share of total debt of euro-area households. Understanding the implications of indebtedness and how financial literacy can help is especially important for young households, first-time homeowners and those at the lower end of the income distribution. 3) financial literacy is negatively associated with the main elements of inclusive growth in the EU, namely poverty, inequality, social exclusion and social immobility. Financial literacy can therefore help access the benefits of economic growth and contribute to the inclusive growth agenda in the EU. In light of these findings, the policy recommendations entail starting financial literacy programmes from a young age; promoting programmes that are tailored to the specific needs of communities, especially young people, women and low-income groups; providing targeted financial education for people on the verge of major financial decisions, such as the first mortgage, student loan, or retirement investment. However, at the same time it is important to resist information overload, support more research into financial literacy, especially behavioural aspects of financial decision-making, and increase private sector involvement since they are at the forefront of financial education and service provision.
    Date: 2018–05
  12. By: Tianyu Ray Li; Anup S. Chamrajnagar; Xander R. Fong; Nicholas R. Rizik; Feng Fu
    Abstract: In this paper, we analyze Twitter signals as a medium for user sentiment to predict the price fluctuations of a small-cap alternative cryptocurrency called \emph{ZClassic}. We extracted tweets on an hourly basis for a period of 3.5 weeks, classifying each tweet as positive, neutral, or negative. We then compiled these tweets into an hourly sentiment index, creating an unweighted and weighted index, with the latter giving larger weight to retweets. These two indices, alongside the raw summations of positive, negative, and neutral sentiment were juxtaposed to $\sim 400$ data points of hourly pricing data to train an Extreme Gradient Boosting Regression Tree Model. Price predictions produced from this model were compared to historical price data, with the resulting predictions having a 0.81 correlation with the testing data. Our model's predictive data yielded statistical significance at the $p
    Date: 2018–05
  13. By: Camille Meyer; Marek Hudon
    Abstract: The commons is a concept increasingly used with the promise of creating new collective wealth. In the aftermath of the economic and financial crises, finance and money have been criticized and redesigned to serve the collective interest. In this article, we analyze three types of complementary currency (CC) systems: community currencies, inter-enterprise currencies, and cryptocurrencies. We investigate whether these systems can be considered as commons. To address this question, we use two main theoretical frameworks that are usually separate: the “new commons” in organization studies and the “common good” in business ethics. Our findings show that these monetary systems and organizations may be considered as commons under the “common good” framework since they promote the common interest by creating new communities. Nevertheless, according to the “new commons” framework, only systems relying on collective action and self-management can be said to form commons. This allows us to suggest two new categories of commons: the “social commons”, which fit into both the “new commons” and the “common good” frameworks, and the “commercial commons”, which fit the “common good” but not the “new commons” framework. This research advances a new conceptualization of the commons and of the ethical implications of complementary currencies.
    Keywords: Common good; Commons; Complementary currencies; Community currencies; Cryptocurrencies; Ethics in finance
    JEL: F35 G21 G28 L31 M14
    Date: 2018–05–14
  14. By: Henry Chacon; Anuradha Roy (UTSA)
    Abstract: Outliers in the financial market data often carry important information, which requires attention and investigation. Many outlier detection techniques, including both parametric and nonparametric, have been developed over the years which are specific to certain application domains. Nonetheless, outlier detection is not an easy task, because sometimes the occurrence of them is pretty easy and evident, but in some other times, it may be extremely cumbersome. Financial series, which are not only pretty sensitive in reflecting the world market conditions due to the interactions of a very large number of participants in its operation, but also influenced by other stock markets that operate in other parts of the world, produce a non-synchronous process. In this research, we detect the presence of outliers in financial time series over the S&P 500 during the year 2016. We detect the beginning of some shocks (outliers) such as the Brexit referendum and the United States Presidential election held in the year 2016. Generally, the impacts of these events were not drastic. Histogram time series was implemented over a daily closing price on intervals of five minutes for the S&P 500 index during 2015 and 2016. In this case, the linear dependency between days of atypical returns were analyzed on quantiles [0 ?? 40]% and [60 ?? 100]%, while Wassertein distance and an approximation of entropy were used to quantify the presence of instant shocks in the index.
    Keywords: Big data, Outlier detection, Financial market, Histogram time series, Entropy
    JEL: M10 P20 A19
    Date: 2017–12–10
  15. By: Rafat Mahmood; Michael Jetter
    Abstract: By facilitating the flow of information in society, communications technology (CT; e.g., newspapers, radio, television, the internet) can help terrorists to (i) spread their message, (ii) recruit followers, and (iii) coordinate among group members. However, CT also facilitates monitoring and arresting terrorists. This paper formulates the hypothesis that a society’s level of CT is systematically related to terrorism. We introduce a simple theoretical framework, suggesting that terrorism first becomes more attractive with a rise in CT, but then decreases, following an inverted U-shape. Accessing data for 199 countries from 1970-2014, we find evidence for these predictions: Terrorism peaks at intermediate ranges of CT and corresponding magnitudes are sizeable. Our estimations control for a range of potentially confounding factors, as well as country- and year-fixed effects. Results are consistent throughout a battery of robustness checks and placebo regressions. Finally, we find no evidence of a potential reporting bias explaining our findings.
    Keywords: communications technology, GTD, information flows, terrorism, panel data
    JEL: D74 D83 L82 L86 L96 P16
    Date: 2018
  16. By: Kazeem Isah (Centre for Econometric and Allied Research, University of Ibadan); Ibrahim D. Raheem (School of Economics, University of Kent, Canterbury, UK)
    Abstract: This paper is motivated by the news that the surge in cryptocurrencies is an important candidate to in explaining the plummeting stock markets. To validate this believe, we construct a predictive model in which cryptocurrencies are identified as the predictors of US stock returns. The inherent statistical properties of cryptocurrencies such as persistence, endogeneity, and conditional heteroscedasticity are being accounted for in the Westerlund and Narayan (2015) estimator. Three salient results emanated from our estimations. First, we validated the importance of cryptocurrencies in predicting US stock prices; second, the cryptocurrencies predictive model outperforms the conventional time-series models such as Autoregressive Integrated Moving Average (ARIMA) model and the Autoregressive Fractionally Integrated Moving Average (ARFIMA); third, our results are robust to different method of forecast performance evaluation measures and different sub-sample periods. These results have important policy implications for the investors and policymakers.
    Keywords: Stock Prices, Cryptocurrency, Digital Asset Prices, Predictive Model, Forecast Evaluation
    JEL: C52 C53 G11 G14 G17
    Date: 2018–05
  17. By: Carey Caginalp
    Abstract: Recently, the notion of cryptocurrencies has come to the fore of public interest. These assets that exist only in electronic form, with no underlying value, offer the owners some protection from tracking or seizure by government or creditors. We model these assets from the perspective of asset flow equations developed by Caginalp and Balenovich, and investigate their stability under various parameters, as classical finance methodology is inapplicable. By utilizing the concept of liquidity price and analyzing stability of the resulting system of ordinary differential equations, we obtain conditions under which the system is linearly stable. We find that trend-based motivations and additional liquidity arising from an uptrend are destabilizing forces, while anchoring through value assumed to be fairly recent price history tends to be stabilizing.
    Date: 2018–05

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