|
on Payment Systems and Financial Technology |
|
Issue of 2019‒04‒15
thirty-six papers chosen by |
| By: | Manlio De Domenico; Andrea Baronchelli |
| Abstract: | The blockchain technology promises to transform finance, money and even governments. However, analyses of blockchain applicability and robustness typically focus on isolated systems whose actors contribute mainly by running the consensus algorithm. Here, we highlight the importance of considering trustless platforms within the broader ecosystem that includes social and communication networks. As an example, we analyse the flash-crash observed on 21st June 2017 in the Ethereum platform and show that a major phenomenon of social coordination led to a catastrophic cascade of events across several interconnected systems. We propose the concept of ``emergent centralisation'' to describe situations where a single system becomes critically important for the functioning of the whole ecosystem, and argue that such situations are likely to become more and more frequent in interconnected socio-technical systems. We anticipate that the systemic approach we propose will have implications for future assessments of trustless systems and call for the attention of policy-makers on the fragility of our interconnected and rapidly changing world. |
| Date: | 2019–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1904.04192&r=all |
| By: | Zhiyong Tu (Peking University HSBC Business School University Town, Shenzhen, China); Lan Ju (Peking University HSBC Business School University Town, Shenzhen, China) |
| Abstract: | Bitcoin as well as other cryptocurrencies are all plagued by the impact from bifurcation. Since the marginal cost of bifurcation is theoretically zero, it causes the coin holders to doubt on the existence of the coin's intrinsic value. This paper suggests a normative dual-value theory to assess the fundamental value of Bitcoin. We draw on the experience from the art market, where similar replication problems are prevalent. The idea is to decompose the total value of a cryptocurrency into two parts: one is its art value and the other is its use value. The tradeoff between these two values is also analyzed, which enlightens our proposal of an image coin for Bitcoin so as to elevate its use value without sacrificing its art value. To show the general validity of the dual-value theory, we also apply it to evaluate the prospects of four major cryptocurrencies. We find this framework is helpful for both the investors and the exchanges to examine a new coin's value when it first appears in the market. |
| Date: | 2019–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1904.05028&r=all |
| By: | Pelin Berkmen; Kimberly Beaton; Dmitry Gershenson; Javier Arze del Granado; Kotaro Ishi; Meeyeon Kim; Emanuel Kopp; Marina V Rousset |
| Abstract: | In Latin America and the Caribbean (LAC), financial technology has been growing rapidly and is on the agenda of many policy makers. Fintech provides opportunities to deepen financial development, competition, innovation, and inclusion in the region but also creates new and only partially understood risks to consumers and the financial system. This paper documents the evolution of fintech in LAC. In particular, the paper focuses on financial development, fintech landscape for domestic and cross border payments and alternative financing, cybersecurity, financial integrity and stability risks, regulatory responses, and considerations for central bank digital currencies. |
| Date: | 2019–03–26 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:19/71&r=all |
| By: | Absher, Samuel; Grier, Kevin |
| Abstract: | We explore the effect of mobile phone and internet access on levels of collective violent action within the Libyan Revolution. Eastern Libya experienced a state-implemented blackout shortly after widespread riots and protests began. However, with luck, ingenuity, and foreign aid, Libyan rebels forged an independent mobile phone network. We exploit the exogeneity of the timing of the network’s reactivation and use a variation of difference-in-differences (DID) to measure the effect on the frequency of collective violent action. While the dominant view in the literature is that cell access increases violence by lowering the costs of organizing, we find that the reactivation of the mobile phone network reduced violent collective action by 21%. We find this negative effect for all conflicts and for conflicts that can be identified as initiated by non-state actors. We also study mobile phone’s effect on collective deadly action and fatalities using a different source for conflicts, finding similar negative effects. We propose mechanisms that may explain the aggregate negative effect: (1) substitution of physical protests to digital protests, (3) the reduction of dissatisfaction toward the state, and (3) the use of mobile phones to avoid conflict with state actors. |
| Keywords: | Mobile phones and violence, natural experiments, Libyan revolution |
| JEL: | F51 |
| Date: | 2019–03–04 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:92627&r=all |
| By: | Arianna Martinelli; Andrea Mina; Massimo Moggi |
| Abstract: | Technological revolutions mark profound transformations in socio-economic systems. They are associated with the diffusion of general purpose technologies that display very high degrees of pervasiveness, dynamism and complementarity. This paper provides an in-depth examination of the technologies underpinning the øfactory of the futureù as profiled by the Industry 4.0 paradigm. It contains an exploratory comparative analysis of the technological bases and the emergent patterns of development of Internet of Things (IoT), big data, cloud, robotics, artificial intelligence and additive manufacturing. By qualifying the øenablingù nature of these technologies, it explores to what extent their diffusion and convergence can be configured as the trigger of a fourth industrial revolution, and identifies key themes for future research on this topic from the viewpoint of industrial and corporate change. |
| Keywords: | Industry 4.0; technological paradigm; enabling technology; general purpose technology; disruptive innovation. |
| Date: | 2019–04–11 |
| URL: | http://d.repec.org/n?u=RePEc:ssa:lemwps:2019/09&r=all |
| By: | Shihao Gu (University of Chicago - Booth School of Business); Bryan T. Kelly (Yale SOM; AQR Capital Management, LLC; National Bureau of Economic Research (NBER)); Dacheng Xiu (University of Chicago - Booth School of Business) |
| Abstract: | We synthesize the field of machine learning with the canonical problem of empirical asset pricing: measuring asset risk premia. In the familiar empirical setting of cross section and time series stock return prediction, we perform a comparative analysis of methods in the machine learning repertoire, including generalized linear models, dimension reduction, boosted regression trees, random forests, and neural networks. At the broadest level, we find that machine learning offers an improved description of expected return behavior relative to traditional forecasting methods. Our implementation establishes a new standard for accuracy in measuring risk premia summarized by an unprecedented out-of-sample return prediction R2. We identify the best performing methods (trees and neural nets) and trace their predictive gains to allowance of nonlinear predictor interactions that are missed by other methods. Lastly, we find that all methods agree on the same small set of dominant predictive signals that includes variations on momentum, liquidity, and volatility. Improved risk premia measurement through machine learning can simplify the investigation into economic mechanisms of asset pricing and justifies its growing role in innovative financial technologies. |
| Keywords: | Machine Learning, Big Data, Return Prediction, Cross-Section of Returns, Ridge Regression, (Group) Lasso, Elastic Net, Random Forest, Gradient Boosting, (Deep) Neural Networks, Fintech |
| Date: | 2018–11 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp1871&r=all |
| By: | Held, Michael (Federal Reserve Bank of New York) |
| Abstract: | Keynote Address at the 1LoD Summit, New York City. |
| Keywords: | enforcement; compliance; Bangladesh; financial crime; digital currencies; trade-based money laundering; Financial Crimes Enforcement Network (FinCEN); beneficial owner; regulations; de-risking; Federal Financial Institutions Examination Council (FFIEC); information sharing; suspicious activity reports (SARs) |
| Date: | 2019–04–02 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fednsp:313&r=all |
| By: | Diane Coyle; Shane O'Connor |
| Abstract: | The sharing economy appears to have been growing rapidly. This paper contributes to the debate about its definition and measurement through an analysis of interviews conducted with UK platforms identifying themselves as part of the sharing economy. We conclude there are common features that enable a sufficiently clear definitional boundary, namely peer-to-peer digital matching and greater utilisation of under-used assets or skills. We find that the larger sharing economy platforms reduce costs and entry barriers for smaller platforms, contributing to a rich ecosystem. This implies a useful definition should include business-to-business peer-matching transactions, as well as business-to-consumer transactions. In addition to their economic impacts – transactions that would not otherwise occur, lower consumer prices and additional choice, the scope to earn additional income in a flexible manner, and the greater use of assets with spare capacity – all the interviewees expressed overt non-financial motivations, such as positive environmental impact, contributing to the community, and building trust. We argue this common intrinsic motivation means measurement of the sharing economy for some purposes should also include those platforms which enable free rather than monetary exchanges. |
| Keywords: | sharing economy, digital platforms |
| JEL: | L22 |
| Date: | 2019–02 |
| URL: | http://d.repec.org/n?u=RePEc:nsr:escoed:escoe-dp-2019-04&r=all |
| By: | Aleksynska, Mariya (IZA); Bastrakova, Anastasia (Kiev International Institute of Sociology); Kharchenko, Natalia Nikolaevna (Kiev International Institute of Sociology) |
| Abstract: | Online labour platforms matching labour supply and demand are profoundly modifying the world of work. Businesses use them to outsource tasks to a world-wide pool of workers; while workers can access work opportunities transcending national boundaries. Increasingly, workers are located in developing and transition economies. This paper is based on survey of online workers of Ukraine, which in 2013-2017 occupied the first place in Europe, and the fourth place in the world in terms of the amount of financial flows and the number of tasks executed by workers through online labour platforms. Focusing on working conditions of digital workers, the paper shows that while the majority of these workers are satisfied with their online work, a sizeable proportion faces risk of being in disguised or dependent employment relationship, works informally, and has a poor social protection. The earnings through the platforms are generally comparable to the earnings in the local labour market, but they do undercut payments for equivalent work that could have been performed in other countries. There is an important gender pay gap in online work. The paper also shows how these working conditions are shaped by both local and international business practices of posting tasks on such platforms. Based on these findings, it presents a set of policy reflections, both for Ukraine and for the future global governance in the world of digital work. |
| Keywords: | gig-economy, online labour, digital labour platforms, working conditions |
| JEL: | J2 J3 J4 L2 |
| Date: | 2019–03 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp12245&r=all |
| By: | Timothy E Dore; Traci L. Mach |
| Abstract: | In 2005, Prosper launched the first peer-to-peer lending website in the US, allowing for consumers to apply for and receive loans entirely online. To understand the effect of this new credit source, we match application-level data from Prosper to credit bureau data. Post application, borrowers' credit scores increase and their credit card utilization rates fall relative to non-borrowers in the short run. In the longer run, total debt levels for borrowers are higher that of non-borrowers. Differences in mortgage debt are particularly large and increasing over time. Despite increased debt levels relative to non-borrowers, delinquency rates for borrowers are significantly lower. |
| Keywords: | Marketplace lending ; Online lending ; Peer-to-peer lending ; Prosper marketplace ; Disintermediation |
| JEL: | G23 G29 G20 |
| Date: | 2019–04–02 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfe:2019-22&r=all |
| By: | Fossen, Frank M. (University of Nevada, Reno); Sorgner, Alina (John Cabot University) |
| Abstract: | We investigate heterogeneous effects of new digital technologies on the individual-level employment- and wage dynamics in the U.S. labor market in the period from 2011-2018. We employ three measures that reflect different aspects of impacts of new digital technologies on occupations. The first measure, as developed by Frey and Osborne (2017), assesses the computerization risk of occupations, the second measure, developed by Felten et al. (2018), provides an estimate of recent advances in artificial intelligence (AI), and the third measure assesses the suitability of occupations for machine learning (Brynjolfsson et al., 2018), which is a subfield of AI. Our empirical analysis is based on large representative panel data, the matched monthly Current Population Survey (CPS) and its Annual Social and Economic Supplement (ASEC). The results suggest that the effects of new digital technologies on employment stability and wage growth are already observable at the individual level. High computerization risk is associated with a high likelihood of switching one's occupation or becoming non-employed, as well as a decrease in wage growth. However, advances in AI are likely to improve an individual's job stability and wage growth. We further document that the effects are heterogeneous. In particular, individuals with high levels of formal education and older workers are most affected by new digital technologies. |
| Keywords: | digitalization, artificial intelligence, machine learning, employment stability, unemployment, wage dynamics |
| JEL: | J22 J23 O33 |
| Date: | 2019–03 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp12242&r=all |
| By: | Antonio Lima; Hasan Bakhshi |
| Abstract: | Rapid technological, social and economic change is having significant impacts on the nature of jobs. In fast-changing environments it is crucial that policymakers have a clear and timely picture of the labour market. Policymakers use standardised occupational classifications, such as the Office for National Statistics’ Standard Occupational Classification (SOC) in the UK to analyse the labour market. These permit the occupational composition of the workforce to be tracked on a consistent and transparent basis over time and across industrial sectors. However, such systems are by their nature costly to maintain, slow to adapt and not very flexible. For that reason, additional tools are needed. At the same time, policymakers over the world are revisiting how active skills development policies can be used to equip workers with the capabilities needed to meet the new labour market realities. There is in parallel a desire for more granular understandings of what skills combinations are required of occupations, in part so that policymakers are better sighted on how individuals can redeploy these skills as and when employer demands change further. In this paper, we investigate the possibility of complementing traditional occupational classifications with more flexible methods centred around employers’ characterisations of the skills and knowledge requirements of occupations as presented in job advertisements. We use data science methods to classify job advertisements as STEM or non-STEM (Science, Technology, Engineering and Mathematics) and creative or non-creative, based on the content of ads in a database of UK job ads posted online belonging to Boston-based job market analytics company, Burning Glass Technologies. In doing so, we first characterise each SOC code in terms of its skill make-up; this step allows us to describe each SOC skillset as a mathematical object that can be compared with other skillsets. Then we develop a classifier that predicts the SOC code of a job based on its required skills. Finally, we develop two classifiers that decide whether a job vacancy is STEM/non-STEM and creative/non-creative, based again on its skill requirements. |
| Keywords: | labour demand, occupational classification, online job adverts, big data, machine learning, STEM, STEAM, creative economy |
| JEL: | C18 J23 J24 |
| Date: | 2018–07 |
| URL: | http://d.repec.org/n?u=RePEc:nsr:escoed:escoe-dp-2018-08&r=all |
| By: | Dorje C. Brody; Lane P. Hughston; Bernhard K. Meister |
| Abstract: | A term structure model in which the short rate is zero is developed as a candidate for a theory of cryptocurrency interest rates. The price processes of crypto discount bonds are worked out, along with expressions for the instantaneous forward rates and the prices of interest-rate derivatives. The model admits functional degrees of freedom that can be calibrated to the initial yield curve and other market data. Our analysis suggests that strict local martingales can be used for modelling the pricing kernels associated with virtual currencies based on distributed ledger technologies. |
| Date: | 2019–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1904.05472&r=all |
| By: | Ken Odajima; Daiju Aiba; Vouthy Khou |
| Abstract: | Beyond dollarization in financial systems and business transactions, foreign currency is widely used in domestic transactions by households in several dollarized economies. Based on data from a nationally representative household survey in Cambodia, we examined the key factors that affect household preferences in relation to currency choice in those transactions where they accept money when selling some assets. We found that size of transaction is negatively correlated to household choice of local currency. In addition, we found that having a bank account mitigates the negative effect of size of transaction on local currency choice, suggesting that availability of financial services could reduce the transaction costs for households when accepting local currency. We also found that our measures of the extent of the network externalities of foreign currency are significantly correlated to household choice of foreign currency. Our findings suggest that improvement in the usability of local currency gained by reducing the transaction costs of local currency relative to foreign currency, particularly for large transactions, can have a positive impact on household use of the local currency in domestic transactions. |
| Keywords: | Dollarization, currency preference, transaction cost, household survey data |
| Date: | 2019–03 |
| URL: | http://d.repec.org/n?u=RePEc:jic:wpaper:185&r=all |
| By: | Basu, Kaushik (World Bank) |
| Abstract: | The advance of digital technology is changing the nature of markets, enhancing the capacity of corporations to extract more consumers' surplus and lower the wages paid to workers. The rise of new technology has also diminished the efficacy of traditional laws to regulate firms and corporations. This is best illustrated by antitrust laws. With the new technology, there is greater returns to scale in production, and further, it is possible to have different components of the same final good be produced by different firms in faraway places. Unlike in earlier times the n firms in one industry, say the automobile industry, would all be producing cars, now the n firms in that industry produce n different parts of the product, thereby getting enormous returns to scale. Such markets are described as vertically serrated markets and their equilibria are characterized. Traditional antitrust law does not apply to these markets because the high returns to scale are natural and not artificially induced. This compels us to look for novel ways to regulate such markets. This paper discusses, in particular, laws that compel firms to have widely dispersed share holdings. |
| Keywords: | antitrust law, share distribution, technological advance, labor demand |
| JEL: | K21 L13 O33 |
| Date: | 2019–04 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izapps:pp146&r=all |
| By: | Claudia Biancotti (Peterson Institute for International Economics); Paolo Ciocca (Consob) |
| Abstract: | Over the past few years, it has become apparent that a small number of technology companies have assembled detailed datasets on the characteristics, preferences, and behavior of billions of individuals. This concentration of data is at the root of a worrying power imbalance between dominant internet firms and the rest of society, reflecting negatively on collective security, consumer rights, and competition. Introducing data sharing mandates, or requirements for market leaders to share user data with other firms and academia, would have a positive effect on competition. As data are a key input for artificial intelligence (AI), more widely available information would help spread the benefits of AI through the economy. On the other hand, data sharing could worsen existing risks to consumer privacy and collective security. Policymakers intending to implement a data sharing mandate should carefully evaluate this tradeoff. |
| Date: | 2019–04 |
| URL: | http://d.repec.org/n?u=RePEc:iie:pbrief:pb19-3&r=all |
| By: | Amin Azari |
| Abstract: | Bitcoin is considered the most valuable currency in the world. Besides being highly valuable, its value has also experienced a steep increase, from around 1 dollar in 2010 to around 18000 in 2017. Then, in recent years, it has attracted considerable attention in a diverse set of fields, including economics and computer science. The former mainly focuses on studying how it affects the market, determining reasons behinds its price fluctuations, and predicting its future prices. The latter mainly focuses on its vulnerabilities, scalability, and other techno-crypto-economic issues. Here, we aim at revealing the usefulness of traditional autoregressive integrative moving average (ARIMA) model in predicting the future value of bitcoin by analyzing the price time series in a 3-years-long time period. On the one hand, our empirical studies reveal that this simple scheme is efficient in sub-periods in which the behavior of the time-series is almost unchanged, especially when it is used for short-term prediction, e.g. 1-day. On the other hand, when we try to train the ARIMA model to a 3-years-long period, during which the bitcoin price has experienced different behaviors, or when we try to use it for a long-term prediction, we observe that it introduces large prediction errors. Especially, the ARIMA model is unable to capture the sharp fluctuations in the price, e.g. the volatility at the end of 2017. Then, it calls for more features to be extracted and used along with the price for a more accurate prediction of the price. We have further investigated the bitcoin price prediction using an ARIMA model, trained over a large dataset, and a limited test window of the bitcoin price, with length $w$, as inputs. Our study sheds lights on the interaction of the prediction accuracy, choice of ($p,q,d$), and window size $w$. |
| Date: | 2019–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1904.05315&r=all |
| By: | Cécile Fonrouge (IRG - Institut de Recherche en Gestion - UPEC UP12 - Université Paris-Est Créteil Val-de-Marne - Paris 12 - UPEM - Université Paris-Est Marne-la-Vallée); Daniela Bolzani |
| Abstract: | The flow of money from members of diasporas and their descendants back to their homelands is significant. In fact, such investments, when made in the form of loans aimed at sustaining entrepreneurship, can contribute to the economic development of the diaspora's home country. Given the increasing relevance of crowdlending as a method of entrepreneurial finance in developing countries, what are the factors that motivate diasporans to invest through online crowdlending instead of through more traditional options, and what barriers hinder them from doing so? We present a theoretical analysis that draws on the existing literature on crowdfunding and transnational entrepreneurship combined with field interviews with three founders of online diasporic platforms. We discuss the variables that must be taken into account when explaining the motivations of diasporans and the barriers hindering their engagement in online microlending. Several areas are highlighted for future theoretical and empirical research to study this largely under-researched phenomenon. |
| Keywords: | crowdlending,crowdfunding,entrepreneurial finance,microlending,entrepreneurship,migration,diasporic investment,diasporan,diaspora,transnational,diasporic finance,developing countries |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:halshs-02077559&r=all |
| By: | Skjeret, Frode (SNF); Steen, Frode (Dept. of Economics, Norwegian School of Economics and Business Administration); Wyndham, Timothy G.A. (Dept. of Economics, Norwegian School of Economics and Business Administration) |
| Abstract: | The digitisation of society has posed a challenge to news outlets. Seeking advertising revenues and facing competition for the attention of their readers, many news outlets entered the digital era with unrestricted access to their online content. More recently, news outlets have sought to restrict the amount of content available for free. We quantify the impact of introducing a paywall on the demand for news in Norway. The short-run average impact of a paywall is negative and between 3 and 4%, in the long run the effect increases to between 9 and 11%. We find heterogeneity in the response to paywalls. The largest news outlet within its market experiences larger effects than the other news outlets. After introducing a paywall, the largest news outlets face a long-run reduction in demand between 13 and 15%, as compared to the others who experience a decrease of between 8 and 11%. The timing of introducing a paywall does not seem to affect the demand response very much. |
| Keywords: | Online news; paywalls; business models; two-sided markets |
| JEL: | D40 L20 L82 |
| Date: | 2019–05–22 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:nhheco:2019_007&r=all |
| By: | Naudé, Wim (Maastricht University) |
| Abstract: | After a number of AI-winters, AI is back with a boom. There are concerns that it will disrupt society. The immediate concern is whether labor can win a 'race against the robots' and the longer-term concern is whether an artificial general intelligence (super-intelligence) can be controlled. This paper describes the nature and context of these concerns, reviews the current state of the empirical and theoretical literature in economics on the impact of AI on jobs and inequality, and discusses the challenge of AI arms races. It is concluded that despite the media hype neither massive jobs losses nor a 'Singularity' are imminent. In part, this is because current AI, based on deep learning, is expensive and difficult for (especially small) businesses to adopt, can create new jobs, and is an unlikely route to the invention of a super-intelligence. Even though AI is unlikely to have either utopian or apocalyptic impacts, it will challenge economists in coming years. The challenges include regulation of data and algorithms; the (mis-) measurement of value added; market failures, anti-competitive behaviour and abuse of market power; surveillance, censorship, cybercrime; labor market discrimination, declining job quality; and AI in emerging economies. |
| Keywords: | technology, articial intelligence, productivity, labor demand, innovation, inequality |
| JEL: | O47 O33 J24 E21 E25 |
| Date: | 2019–03 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp12218&r=all |
| By: | Bullard, James B. (Federal Reserve Bank of St. Louis) |
| Abstract: | St. Louis Fed President James Bullard welcomed community bankers, regulators and researchers to the Community Banking in the 21st Century research and policy conference. He also welcomed a third sponsor for the conference: The Federal Deposit Insurance Corp. has joined the Federal Reserve System and the Conference of State Bank Supervisors in presenting this sixth annual conference. Bullard also discussed a handful of topics related to technology, including “fintech,” artificial intelligence and innovation hubs. “The changing landscape of financial services is an important reason for this conference,” he said. |
| Date: | 2018–10–03 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedlps:322&r=all |
| By: | Bullard, James B. (Federal Reserve Bank of St. Louis) |
| Date: | 2018–07–15 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedlps:316&r=all |
| By: | Bryan Lim; Stefan Zohren; Stephen Roberts |
| Abstract: | While time series momentum is a well-studied phenomenon in finance, common strategies require the explicit definition of both a trend estimator and a position sizing rule. In this paper, we introduce Deep Momentum Networks -- a hybrid approach which injects deep learning based trading rules into the volatility scaling framework of time series momentum. The model also simultaneously learns both trend estimation and position sizing in a data-driven manner, with networks directly trained by optimising the Sharpe ratio of the signal. Backtesting on a portfolio of 88 continuous futures contracts, we demonstrate that the Sharpe-optimised LSTM improved traditional methods by more than two times in the absence of transactions costs, and continue outperforming when considering transaction costs up to 2-3 basis points. To account for more illiquid assets, we also propose a turnover regularisation term which trains the network to factor in costs at run-time. |
| Date: | 2019–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1904.04912&r=all |
| By: | Böckerman, Petri; Kortelainen, Mika; Laine, Liisa T.; Nurminen, Mikko; Saxell, Tanja |
| Abstract: | We exploit a large-scale natural experiment - the rollout of a nationwide electronic prescribing system in Finland - to study how digitization of prescriptions affects pharmaceutical use and health outcomes. We use comprehensive administrative data from patients treated with benzodiazepines, which are globally popular, effective but addictive psychotropic medications. We find no impact on benzodiazepine use on average, but among younger patients e-prescribing increases repeat prescription use. Younger patients' health outcomes do not improve but adverse outcomes, such as prescription drug abuse disorders and suicide attempts, increase dramatically. Improving access to medication through easier ordering may thus increase medication overuse. |
| Keywords: | health information technology, electronic prescribing, repeat prescriptions, inefficiency, medication overuse, Local public finance and provision of public services, H51, H75, I12, I18, |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:fer:wpaper:117&r=all |
| By: | Bullard, James B. (Federal Reserve Bank of St. Louis) |
| Abstract: | Speaking in New York, St. Louis Fed President James Bullard discussed how the current cryptocurrency wave may be driving the U.S. uniform currency system toward something more like the global non-uniform currency system, which is characterized by volatile exchange rates. He noted that societies have disliked non-uniform currency systems because the currencies trade at different values. “Cryptocurrencies may unwittingly be pushing in the wrong direction in trying to solve an important social problem, which is how best to facilitate market-based exchange,” Bullard said. |
| Date: | 2018–05–14 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedlps:307&r=all |
| By: | Marc Lassagne (Arts et Métiers ParisTech - ENSAE ParisTech - École Nationale de la Statistique et de l'Administration Économique); Laurent Dehouck (ENS Rennes - École normale supérieure - Rennes) |
| Abstract: | his article examines the risks and opportunities associated with distributed ledgers technologies. A novel methodology is developed and presented in order to diagnose and manage their impacts using a strategic risk management perspective. Possible responses to the identified challenges are suggested. |
| Abstract: | Cet article porte sur les enjeux stratégiques, les risques et les opportunités de la grappe technologique des registres numériques distribués. Il propose une nouvelle méthodologie dans une perspective de gestion stratégique des risques pour en apprécier l'impact et bâtir des réponses adaptées. |
| Date: | 2018–10–16 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-02074330&r=all |
| By: | Bullard, James B. (Federal Reserve Bank of St. Louis) |
| Date: | 2018–05–31 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedlps:311&r=all |
| By: | Pierre C. Boyer (CREST, École Polytechnique, France); Thomas Delemotte (CREST, ENSAE, France); Germain Gauthier (CREST, École Polytechnique, France); Vincent Rollet (CREST, École Polytechnique, France); Benoît Schmutz (CREST, École Polytechnique, France) |
| Abstract: | Cet article présente les résultats d'une étude sur les territoires dont sont originaires les "gilets jaunes". Dès le premier samedi de mobilisation le 17 novembre 2018, ce mouvement se démarque par son caractère local et sa couverture nationale. A partir de données inédites de la mobilisation sur Facebook, nous montrons une forte corrélation entre mobilisation online (sur Facebook) et o ine (blocages des ronds-points). Nous réalisons alors une cartographie ne et contrastée de la contestation. L'étude économétrique met en évidence le rôle de la mobilité pour expliquer les origines du mouvement, au travers notamment du passage des routes à 80km/h et des distances domicile-travail. |
| Keywords: | Gilets jaunes ; manifestation ; économie urbaine ; mobilisation réseaux sociaux. |
| JEL: | F15 J40 J60 J80 C83 |
| Date: | 2019–03–28 |
| URL: | http://d.repec.org/n?u=RePEc:crs:wpaper:2019-06&r=all |
| By: | Asongu, Simplice; Nwachukwu, Jacinta; Orim, Stella-Maris; Pyke, Chris |
| Abstract: | Purpose-The study complements the scant macroeconomic literature on the development outcomes of social media by examining the relationship between Facebook penetration and violent crime levels in a cross-section of 148 countries for the year 2012. Design/methodology/approach-The empirical evidence is based on Ordinary Least Squares (OLS), Tobit and Quantile regressions. In order to respond to policy concerns on the limited evidence on the consequences of social media in developing countries, the dataset is disaggregated into regions and income levels. The decomposition by income levels included: low income, lower middle income, upper middle income and high income. The corresponding regions include: Europe and Central Asia, East Asia and the Pacific, Middle East and North Africa, Sub-Saharan Africa and Latin America. Findings-From OLS and Tobit regressions, there is a negative relationship between Facebook penetration and crime. However, Quantile regressions reveal that the established negative relationship is noticeable exclusively in the 90th crime quantile. Further, when the dataset is decomposed into regions and income levels, the negative relationship is evident in the Middle East and North Africa (MENA) while a positive relationship is confirmed for sub-Saharan Africa. Policy implications are discussed. Originality/value- Studies on the development outcomes of social media are sparse because of a lack of reliable macroeconomic data on social media. This study primarily complemented five existing studies that have leveraged on a newly available dataset on Facebook. |
| Keywords: | Crime; Social media; ICT; Global evidence; Social networks |
| JEL: | D74 D83 K42 O30 |
| Date: | 2019–01 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:93234&r=all |
| By: | Julia Cage (Département d'économie); Nicolas Hervé (Institut national de l'audiovisuel); Marie-Luce Viaud (Institut national de l'audiovisuel) |
| Abstract: | This paper documents the extent of copying and estimates the returns to originality in online news production. We build a unique dataset combining all the online content produced by French news media during the year 2013 with new micro audience data. We develop a topic detection algorithm that identifies each news event, trace the timeline of each story, and study news propagation. We unravel new evidence on online news production. First, we document high reactivity of online media: one quarter of the news stories are reproduced online in under 4 minutes. Second, we show that this comes with extensive copying: only 33% of the online content is original. Third, we investigate the cost of copying for original news producers. Using article-level variations and media-level daily audience combined with article-level social media statistics, we find that readers partly switch to the original producers, thereby mitigating the newsgathering incentive problem raised by copying. |
| Keywords: | Internet; Information spreading; Copyright; Social media; Reputation |
| JEL: | L11 L15 L82 L86 |
| Date: | 2019–04 |
| URL: | http://d.repec.org/n?u=RePEc:spo:wpecon:info:hdl:2441/3tcpvf3sd399op9sgtn8tq5bhd&r=all |
| By: | Pierre Henry-Labord`ere (SOCIETE GENERALE) |
| Abstract: | In this paper, we introduce a primal-dual algorithm for solving (martingale) optimal transportation problems, with cost functions satisfying the twist condition, close to the one that has been used recently for training generative adversarial networks. As some additional applications, we consider anomaly detection and automatic generation of financial data. |
| Date: | 2019–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1904.04546&r=all |
| By: | Bullard, James B. (Federal Reserve Bank of St. Louis) |
| Abstract: | St. Louis Fed President James Bullard participated in a moderated conversation at Ascension Investment Management’s annual conference in St. Louis. He discussed a variety of topics, including: factors keeping real interest rates low, with the biggest one being increased demand for safe assets globally over the last 30 years; market-based inflation expectations, which remain below 2 percent on a PCE (or personal consumption expenditures price index) basis; the possibility of yield curve inversion, which he called a key near-term risk for the Fed; Fed communications, including having a press conference after every FOMC meeting beginning next year; and cryptocurrencies, which are creating drift toward a non-uniform currency—something that people have not liked historically, he said. |
| Date: | 2019–04–01 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedlps:315&r=all |
| By: | Hali Edison (Williams College); Hector Carcel (Bank of Lithuania) |
| Abstract: | This paper applies Latent Dirichlet Allocation (LDA), a machine learning algorithm, to analyze the transcripts of the U.S. Federal Open Market Committee (FOMC) covering the period 2003 – 2012, including 45,346 passages. The goal is to detect the evolution of the different topics discussed by the members of the FOMC. The results of this exercise show that discussions on economic modelling were dominant during the Global Financial Crisis (GFC), with an increase in discussion of the banking system in the years following the GFC. Discussions on communication gained relevance toward the end of the sample as the Federal Reserve adopted a more transparent approach. The paper suggests that LDA analysis could be further exploited by researchers at central banks and institutions to identify topic priorities in relevant documents such as FOMC transcripts. |
| Keywords: | FOMC, Text data analysis, Transcripts, Latent Dirichlet Allocation |
| JEL: | E52 E58 D78 |
| Date: | 2019–04–05 |
| URL: | http://d.repec.org/n?u=RePEc:lie:dpaper:11&r=all |
| By: | Pauline Affeldt; Tomaso Duso; Florian Szücs |
| Abstract: | We study the evolution of the EC’s merger decision procedure over the first 25 years of European competition policy. Using a novel dataset constructed at the level of the relevant markets and containing all merger cases over the 1990-2014 period, we evaluate how consistently arguments related to structural market parameters were applied over time. Using non-parametric machine learning techniques, we find that the importance of market shares and concentration measures has declined while the importance of barriers to entry and the risk of foreclosure has increased in the EC’s merger assessment following the 2004 merger policy reform. |
| Keywords: | Merger policy, DG competition, causal forests |
| JEL: | K21 L40 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:diw:diwwpp:dp1797&r=all |
| By: | Adamantios Ntakaris; Giorgio Mirone; Juho Kanniainen; Moncef Gabbouj; Alexandros Iosifidis |
| Abstract: | Mid-price movement prediction based on limit order book (LOB) data is a challenging task due to the complexity and dynamics of the LOB. So far, there have been very limited attempts for extracting relevant features based on LOB data. In this paper, we address this problem by designing a new set of handcrafted features and performing an extensive experimental evaluation on both liquid and illiquid stocks. More specifically, we implement a new set of econometrical features that capture statistical properties of the underlying securities for the task of mid-price prediction. Moreover, we develop a new experimental protocol for online learning that treats the task as a multi-objective optimization problem and predicts i) the direction of the next price movement and ii) the number of order book events that occur until the change takes place. In order to predict the mid-price movement, the features are fed into nine different deep learning models based on multi-layer perceptrons (MLP), convolutional neural networks (CNN) and long short-term memory (LSTM) neural networks. The performance of the proposed method is then evaluated on liquid and illiquid stocks, which are based on TotalView-ITCH US and Nordic stocks, respectively. For some stocks, results suggest that the correct choice of a feature set and a model can lead to the successful prediction of how long it takes to have a stock price movement. |
| Date: | 2019–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1904.05384&r=all |
| By: | Storm, Hugo; Heckelei, Thomas; Baylis, Kathy; Mittenzwei, Klaus |
| Abstract: | Farm subsidies are commonly motivated by their promise to help keep families in agriculture and reduce farm structural change. Many of these subsidies are designed to be targeted to smaller farms, and include production caps or more generous funding for smaller levels of activity. Agricultural economists have long studied how such subsidies affect production choices, and resulting farm structure. Traditional econometric models are typically restricted to detecting average effects of subsidies on certain farm types or regions and cannot easily incorporate complex subsidy design or the multi-output, heterogeneous nature of many farming activities. Programming approaches may help address the broad scope of agricultural production but have less empirical measures for behavioral and technological parameters. This paper uses a recurrent neural network and detailed panel data to estimate the effect of subsidies on the structure of Norwegian farming. Specifically, we use the model to determine how the varying marginal subsidies have affected the distribution of Norwegian farms and their range of agricultural activities. We use the predictive capacity of this flexible, multi-output machine learning model to identify the effects of agricultural subsidies on farm activity and structure, as well as their detailed distributional effects. |
| Keywords: | Agricultural and Food Policy, Farm Management, Land Economics/Use, Research Methods/ Statistical Methods |
| Date: | 2019–04–10 |
| URL: | http://d.repec.org/n?u=RePEc:ags:ubfred:287343&r=all |