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on Payment Systems and Financial Technology |
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Issue of 2019‒05‒27
34 papers chosen by |
| By: | Andrew Clark (Department of Economics, University of Reading); Alexander Mihailov (Department of Economics, University of Reading) |
| Abstract: | This paper begins by a recap on the ambition and mechanism behind Bitcoin, followed by an overview of the top 10 cryptocurrencies by market capitalization. Our focus is on their price dynamics and volatility relative to those of fiat paper money and gold, assets that have traditionally served the functions of money and international reserves. We then perform a counterfactual analysis using the Bank of England's foreign currency reserves to determine the hypothetical performance in terms of relative volatility of two alternative reserve portfolios consisting of 0.1%, 1%, or 10% holdings of either Bitcoin only, since July 2010, or of a portfolio of 50% Bitcoin and 50% Ethereum, since July 2015. Revisiting in this light the functions of money and international reserves, we expound on why private cryptocurrencies do not meet the inherent requirements for both money and international reserve assets, whereas central bank digital currencies do meet these requirements. We, finally, "scale" the magnitude and dynamics of the recent Bitcoin bubble into a historical perspective, and conclude by a discussion of areas where blockchain-based and FinTech technologies could be beneficial in international trade, payments, banking and finance. |
| Keywords: | Bitcoin, cryptocurrency, blockchain, FinTech, central bank digital currency, international reserve assets |
| JEL: | G23 E50 E59 |
| Date: | 2019–05–20 |
| URL: | http://d.repec.org/n?u=RePEc:rdg:emxxdp:em-dp2019-09&r=all |
| By: | David Allessie; Maciej Sobolewski (European Commission - JRC); Lorenzino Vaccari (European Commission - JRC) |
| Abstract: | In less than ten years from its advent in 2008, the concept of distributed ledgers has entered into mainstream research and policy agendas. Enthusiastic reception, fuelled by the success of Bitcoin and the explosion of potential use cases created high, if not hyped, expectations with respect to the transformative role of blockchain for the industry and the public sector. Growing experimentation with distributed ledgers and the emergence of the first operational implementations provide an opportunity to go beyond hype and speculation based on theoretical use cases. This report looks at the ongoing exploration of blockchain technology by governments. The analysis of a group of pioneering developments of public services shows that blockchain technology can reduce bureaucracy, increase the efficiency of administrative processes and increase the level of trust in public recordkeeping. Based on the state-of-art developments, blockchain has not yet demonstrated to be either transformative or even disruptive an innovation for governments as it is sometimes portrayed. Ongoing projects bring incremental rather than fundamental changes to the operational capacities of governments. Nevertheless some of them offer clear value for citizens. Technological and ecosystem maturity of distributed ledgers have to increase in order to unlock the transformative power of blockchain. Policy agenda should focus on non-technological barriers, such as incompatibility between blockchain-based solutions and existing legal and organizational frameworks. This principal policy goal cannot be achieved by adapting technology to legacy systems. It requires using the transformative power of blockchain to be used to create new processes, organizations, structures and standards. Hence, policy support should stimulate more experimentation with both the technology and new administrative processes that can be re-engineered for blockchain. |
| Keywords: | blockchain, public sector, distributed ledgers, digital government, public services |
| Date: | 2019–04 |
| URL: | http://d.repec.org/n?u=RePEc:ipt:iptwpa:jrc115049&r=all |
| By: | Nordin, Martin (Department of Economics, Lund University); Grenestam , Erik (Department of Economics, Lund University); Gullstrand , Joakim (Department of Economics, Lund University) |
| Abstract: | This study investigates the relationship between super-fast broadband and firms’ sales and employment level in Sweden. It is important to learn more about this recent technological change and few studies has explored the impact of super-fast broadband on firm outcomes. We use the previous roll-out of second-generation internet access to identify the effect of third-generation internet access. The early investments in optic fiber where largely core broadband network investments paving the way for later investments in third-generation broadband technology. Municipalities choosing providers who prioritized cheap technology (broadband over telephone lines, DSL) targeting the many, thus fell behind municipalities choosing providers investing in optic fiber. We find heterogeneity in the broadband effect, but the overall effect is negative. This effect may be associated with the roll-out of 4G mobile broadband in 2011; mobile broadband services are a byproduct of optic fiber because mobile broadband is transmitted from the same high capacity fiber-optic base stations. We suggest that the negative effect found is related to internet use at work and the mixing of private and work related internet use. |
| Keywords: | broadband; optic fiber; firm output; employment; regional analysis |
| JEL: | D22 J23 O30 R50 |
| Date: | 2019–05–13 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:lunewp:2019_008&r=all |
| By: | Hiroshi FUJIKI; Kiyotaka Nakashima |
| Abstract: | We examine the trends in cash usage in Japan and its substitution with noncash payment methods, such as credit cards and electronic money, using both aggregate and individual household survey data. We find that cash hoarding accounts for as much as 42% of total cash circulation in Japan. Behind this finding lies an unstable semi-log cash demand function after the late 1990s and a stable log-log cash demand function from 1995 to 2016. We also find that the extent of possible decreases in cash demand because of the substitution of cash for credit cards in day-to-day transactions is not large. Our back-of-the-envelope estimate of the possible maximum decrease in cash demand for day-to-day transactions is at most 0.4% in 2017 of the total cash in circulation in Japan. |
| Date: | 2019–03 |
| URL: | http://d.repec.org/n?u=RePEc:tcr:wpaper:e131&r=all |
| By: | Min Shu; Wei Zhu |
| Abstract: | In the past decade, Bitcoin has become an emerging asset class well known to most people because of their extraordinary return potential in phases of extreme price growth and their unpredictable massive crashes. We apply the LPPLS confidence indicator as a diagnostic tool for identifying bubbles using the daily data of Bitcoin price in the past two years. We find that the LPPLS confidence indicator based on the daily data of Bitcoin price fails to provide effective warnings for detecting the bubbles when the Bitcoin price suffers from a large fluctuation in a short time, especially for positive bubbles. In order to diagnose the existence of bubbles and accurately predict the bubble crashes in the cryptocurrency market, this study proposes an adaptive multilevel time series detection methodology based on the LPPLS model. We adopt two levels of time series, 1 hour and 30 minutes, to demonstrate the adaptive multilevel time series detection methodology. The results show that the LPPLS confidence indicator based on the adaptive multilevel time series detection methodology have not only an outstanding performance to effectively detect the bubbles and accurately forecast the bubble crashes, but can also monitor the development and the crash of bubbles even if a bubble exists in a short time. In addition, we discover that the short-term LPPLS confidence indicator greatly affected by the extreme fluctuations of Bitcoin price can provide some useful insights into the bubble status on a shorter time scale, and the long-term LPPLS confidence indicator has a stable performance in terms of effectively monitoring the bubble status on a longer time scale. The adaptive multilevel time series detection methodology can provide real-time detection of bubbles and advanced forecast to warn of an imminent crash risk in not only the cryptocurrency market but also the other financial markets. |
| Date: | 2019–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1905.09647&r=all |
| By: | Stéphane Blemus (Université Paris1 Panthéon-Sorbonne, LabEx ReFi, Kalexius law firm, ChainTech); Dominique Guégan (Université Paris1 Panthéon-Sorbonne, Centre d'Economie de la Sorbonne, LabEx ReFi and Ca' Foscari University of Venezia) |
| Abstract: | This paper discusses the potential impacts of the so-called “initial coin offerings”, and of several developments based on distributed ledger technology (“DLT”), on corporate governance. While many academic papers focus mainly on the legal qualification of DLT and crypto-assets, and most notably in relation to the potential definition of the latter as securities/financial instruments, the authors analyze some of the use cases based on DLT technology and their potential for significant changes of the corporate governance analyses. This article studies the consequences due to the emergence of new kinds of firm stakeholders, i.e. the crypto-assets holders, on the governance of small and medium-sized enterprises (“SMEs”) as well as of publicly traded companies. Since early 2016, a new way of raising funds has rapidly emerged as a major issue for FinTech founders and financial regulators. Frequently referred to as initial coin offerings, Initial Token Offerings (“ITO”), Token Generation Events (“TGE”) or simply “token sales”, we use in our paper the terminology Initial Crypto-asset Offerings (“ICO”), as it describes more effectively than “initial coin offerings” the vast diversity of assets that could be created and which goes far beyond the payment instrument issue |
| Keywords: | ICO; Crypto-asset; Blockchain; Governance; Tokens; Fair value; Illiquid market; Kalman filter; Mark to model |
| JEL: | G12 |
| Date: | 2019–02 |
| URL: | http://d.repec.org/n?u=RePEc:mse:cesdoc:19004&r=all |
| By: | Lukasz Grzybowski (Télécom ParisTech); Maude Hasbi (Télécom ParisTech); Julienne Liang (Orange Labs [Paris] - Telecom Orange) |
| Abstract: | We estimate a mixed logit model using data on choices of broadband technologies by 94,388 subscribers to a single broadband operator in a European country on a monthly basis from January to December 2014. We find that valuation of DSL connection speed in the range between 1 and 8 MB/s is very similar. Moreover, in January 2014, the valuation of FttH connection with speed of 100 MB/s is not much different than of DSL connection with speed of 1 or 8 MB/s but it increased over time. The small initial difference in valuation of DSL and FttH connections may be because basic Internet needs of consumers such as emailing, reading news, shopping, browsing and even watching videos online could be satisfied with connection speed below 8 MB/s. We also find that consumers face significant switching costs when changing broadband tariffs, which are substantially higher when switching from DSL to FttH technology. According to counterfactual simulations based on our model estimates, switching costs between technologies are the main factor which slows down transition from DSL to FttH. |
| Keywords: | FttH,DSL,connection speed,switching costs |
| Date: | 2018–03 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-02102338&r=all |
| By: | Pierre Schweitzer (LID2MS - Laboratoire Interdisciplinaire Droit des Médias et Mutations Sociales - AMU - Aix Marseille Université, AMU - Aix Marseille Université) |
| Abstract: | This article describes the upset of the music industry after 1999, in part under the influence of increasingly efficient digital technologies allowing for the copy and sharing of music files. Then the author analyses the value proposition of streaming services such as Spotify, as well as their influence on consumer welfare and that of artists. He then concludes on the overall positive effect of this new model. |
| Abstract: | Cet article décrit les bouleversements intervenus dans l'industrie de la musique enregistrée après 1999, notamment sous l'influence des technologies numériques permettant la copie et la distribution de fichiers musicaux. Puis l'auteur analyse la proposition de valeur des services de streaming musical tels que Spotify ainsi que leur influence sur le bien-être du public, celui des artistes présents sur ces plateformes numériques, avant de conclure sur l'aspect globalement positif de ce nouveau modèle. |
| Keywords: | Music,Digital,Streaming Music Platforms,Culture,Recorded music industry,Musique enregistrée,Numérique -- Usages,Spotify,Streaming,Musique,Numérique |
| Date: | 2019–02 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-02120215&r=all |
| By: | Evans, Olaniyi |
| Abstract: | Trumponomics describes the economic policies of U.S. President Donald Trump and has “America-first” approach. The Trump administration risks creating a more fragmented global economy and has started the biggest global trade war. The various sides are still on tenterhooks to impose additional tariffs worth hundreds of billions of dollars. Using deadweight loss (also known as excess burden or allocative inefficiency) and Harberger's triangle, this study shows that: the trade war is devastating not just for the US and China, but for the whole world economy: (i) the prices of items that directly affect consumers’ welfare will rise; (ii) firms will face extra costs for exports; (iii) investors will become more nervous; (iv) some investors will diversify into Bitcoin and other cryptocurrencies; (v) the trade war could turn into a currency war; (vi) even developed countries could be hit by the trade war; and (vii) tariffs applied on developing countries’ exports would rise steeply. In a trade war, everyone may lose. |
| Keywords: | Trumponomics, US-China Trade War, Consumers, Stocks, Cryptocurrency, Brexit |
| JEL: | F4 O1 P0 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:93682&r=all |
| By: | Laszlo Lorincz (Institute of Economics, Centre for Economic and Regional Studies, Hungarian Academy of Sciences and Corvinus University of Budapest); Brigitta Nemeth (Institute of Economics, Centre for Economic and Regional Studies, Hungarian Academy of Sciences) |
| Abstract: | Previous studies have shown the impact of family, community, and ethnic networks on migration. Our research focuses on the role of social networks in Hungarian internal migration. We examine the factors determining out-migration rate from municipalities, and the factors influencing location choice by analysingmigration volumes on the municipality-municipality level. We measure social network effects by the migration rate of previous years, and by the intensity of user-user connections on the iWiW online social network (representing3.7million users) between two municipalities. The migration volumes and the characteristics of the municipalities are included in the analysis based on administrative data, and the distance between municipalities are indicated by the travel time. We analyselongitudinal data for the2000-2014 period, and cross-sectional models for the year 2014. Based on multilevel and fixed-effect regression models we show that both leaving and choosing municipalities is associated with network effects: the migration of previous years, and also the connections on iWiW social network influence the current migration rate, even after controlling for each other. |
| Keywords: | chain migration, internal migration, network effects, online social networks, social networks |
| JEL: | R23 |
| Date: | 2019–05 |
| URL: | http://d.repec.org/n?u=RePEc:has:discpr:1913&r=all |
| By: | Lihi Dery; Dror Hermel; Artyom Jelnov |
| Abstract: | Consider an application sold on an on-line platform, with the app paying a commission fee and, henceforth, offered for sale on the platform. The ability to sell the application depends on its customer ranking. Therefore, developers may have an incentive to promote their applications ranking in a dishonest manner. One way to do this is by faking positive customer reviews. However, the platform is able to detect dishonest behavior (cheating) with some probability and then proceeds to decide whether to ban the application. We provide an analysis and find the equilibrium behaviors of both the applications developers (cheat or not) and the platform (setting of the commission fee). We provide initial insights into how the platforms detection accuracy affects the incentives of the app developers. |
| Date: | 2019–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1905.09116&r=all |
| By: | Daron Acemoglu (MIT and CIFAR); Pascual Restrepo (Boston University) |
| Abstract: | We argue theoretically and document empirically that aging leads to greater (industrial) automation, and in particular, to more intensive use and development of robots. Using US data, we document that robots substitute for middle-aged workers (those between the ages of 36 and 55). We then show that demographic change—corresponding to an increasing ratio of older to middle-aged workers—is associated with greater adoption of robots and other automation technologies across countries and with more robotics-related activities across US commuting zones. We also provide evidence of more rapid development of automation technologies in coun- tries undergoing greater demographic change. Our directed technological change model further predicts that the induced adoption of automation technology should be more pronounced in industries that rely more on middle-aged workers and those that present greater opportunities for automation. Both of these predictions receive support from country-industry variation in the adoption of robots. Our model also implies that the productivity implications of aging are ambiguous when technology responds to demographic change, but we should expect produc- tivity to increase and labor share to decline relatively in industries that are most amenable to automation, and this is indeed the pattern we find in the data. |
| Keywords: | aging, automation, demographic change, economic growth, directed technological change, productivity, robots, tasks, technology |
| JEL: | J11 J23 J24 O33 O47 O57 |
| Date: | 2018–03 |
| URL: | http://d.repec.org/n?u=RePEc:bos:iedwpr:dp-299&r=all |
| By: | Asongu, Simplice; Odhiambo, Nicholas |
| Abstract: | This study investigates linkages between the mobile phone, information sharing offices (ISO) and financial sector development in 53 African countries for the period 2004-2011. ISO are private credit bureaus and public credit registries. The empirical evidence is based on contemporary and non-contemporary quantile regressions. Two main hypotheses are tested: mobile phones complement ISO to enhance the formal financial sector (Hypothesis 1) and mobile phones complement ISO to reduce the informal financial sector (Hypothesis 2). The hypotheses are largely confirmed. This research adds to the existing body of literature by engaging hitherto unexplored dimensions of financial sector development and investigating the role of mobile phones in information sharing for financial sector development. |
| Keywords: | Information sharing; Banking sector development; Africa |
| JEL: | G20 G29 L96 O40 O55 |
| Date: | 2019–01 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:94011&r=all |
| By: | Teodora Marinova (Sofia University “St. Kliment Ohridski”, Faculty of Economics and Business Administration) |
| Abstract: | In this paper I investigate the factors influencing the likelihood of crowdfunding projects’ success by analyzing data from the crowdfunding platform Kickstarter. The research focus is on the influence of virtual community characteristics. The results show that the probability of project success is positively influenced by a higher number of project supporters but a larger amount of comments on the project, controlled for project definition factors, is found to decrease the likelihood of project success. This is in line with previous findings of a double-edged impact of the size of the virtual innovation community and the amount of peer-to-peer interaction on the likelihood of successful innovation input by the participants. |
| Keywords: | User innovation, Crowdfunding, Virtual communities, Innovation communities. |
| JEL: | O31 O32 O33 |
| Date: | 2019–05 |
| URL: | http://d.repec.org/n?u=RePEc:sko:wpaper:bep-2019-05&r=all |
| By: | Korishchenko, Konstantin (Корищенко, Константин) (The Russian Presidential Academy of National Economy and Public Administration) |
| Abstract: | During the last 5-10 years, the topic of digitalization of the economy has gained increasing importance. One of the directions of this process is the issue and circulation of “cryptocurrency”, which was initiated in the framework of the project for the production of bitcoins. Currently, there are more than 1,500 cryptocurrencies in circulation, when assessing their total capitalization at the beginning of 2018 over $ 500 billion. Central banks and other government agencies are actively working to create a regulatory among new financial assets that claim to function as money . One of the pressing issues is the attitude of central banks to the issue of their own cryptocurrencies and possible mechanisms for the implementation of such projects. |
| Date: | 2019–04 |
| URL: | http://d.repec.org/n?u=RePEc:rnp:wpaper:041934&r=all |
| By: | de Kok, Ties (Tilburg University, School of Economics and Management) |
| Abstract: | The three essays collected in this PhD thesis concern internal and external reporting practices, narrative disclosures, recent advancements in reporting technologies, and the role of reporting in emerging markets. These essays utilize state-of-the-art empirical techniques drawn from computer science along with new data sources to study fundamental accounting questions. The first essay studies the relationship between reporting frequency and market pressure over social media in crowdfunding markets. The second essay studies the use of soft information in the context of internal bank lending decisions, in particular during a scenario of mandated changes to the location of decisions rights. The third essay studies the information retrieval process for narrative disclosures for users that vary in their financial literacy by combining innovative tracking techniques deployed on Amazon Mechanical Turk with state-of-the-art machine learning techniques. |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:tiu:tiutis:468fd12b-19c0-4c7b-a33a-6813c55ce950&r=all |
| By: | Michael Bailey; Drew M. Johnston; Theresa Kuchler; Johannes Stroebel; Arlene Wong |
| Abstract: | We study the nature of peer effects in the market for new cell phones. Our analysis builds on de-identified data from Facebook that combine information on social networks with information on users' cell phone models. To identify peer effects, we use variation in friends' new phone acquisitions resulting from random phone losses and carrier-specific contract terms. A new phone purchase by a friend has a substantial positive and long-term effect on an individual's own demand for phones of the same brand, most of which is concentrated on the particular model purchased by the friend. We provide evidence that social learning contributes substantially to the observed peer effects. While peer effects increase the overall demand for cell phones, a friend's purchase of a new phone of a particular brand can reduce individuals' own demand for phones from competing brands---in particular those running on a different operating system. We discuss the implications of these findings for the nature of firm competition. We also find that stronger peer effects are exerted by more price-sensitive individuals. This positive correlation suggests that the elasticity of aggregate demand is substantially larger than the elasticity of individual demand. Through this channel, peer effects reduce firms' markups and, in many models, contribute to higher consumer surplus and more efficient resource allocation. |
| JEL: | D40 L1 L2 M3 |
| Date: | 2019–05 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:25843&r=all |
| By: | Reaz Chowdhury; M. Arifur Rahman; M. Sohel Rahman; M. R. C. Mahdy |
| Abstract: | At present, cryptocurrencies have become a global phenomenon in financial sectors as it is one of the most traded financial instruments worldwide. Cryptocurrency is not only one of the most complicated and abstruse fields among financial instruments, but it is also deemed as a perplexing problem in finance due to its high volatility. This paper makes an attempt to apply machine learning techniques on the index and constituents of cryptocurrency with a goal to predict and forecast prices thereof. In particular, the purpose of this paper is to predict and forecast the close (closing) price of the cryptocurrency index 30 and nine constituents of cryptocurrencies using machine learning algorithms and models so that, it becomes easier for people to trade these currencies. We have used several machine learning techniques and algorithms and compared the models with each other to get the best output. We believe that our work will help reduce the challenges and difficulties faced by people, who invest in cryptocurrencies. Moreover, the obtained results can play a major role in cryptocurrency portfolio management and in observing the fluctuations in the prices of constituents of cryptocurrency market. We have also compared our approach with similar state of the art works from the literature, where machine learning approaches are considered for predicting and forecasting the prices of these currencies. In the sequel, we have found that our best approach presents better and competitive results than the best works from the literature thereby advancing the state of the art. Using such prediction and forecasting methods, people can easily understand the trend and it would be even easier for them to trade in a difficult and challenging financial instrument like cryptocurrency. |
| Date: | 2019–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1905.08444&r=all |
| By: | Marek Hudon; Marc Labie; Ariane Szafarz |
| Abstract: | This article is the introductory chapter of the book A Research Agenda for Financial Inclusion and Microfinance, edited by Marek Hudon, Marc Labie and Ariane Szafarz, and forthcoming in 2019 with Elgar Research Publishing. This introductive article written by the editors explains how research in microfinance and financial inclusion evolved together with field practices. It identifies the four periods in the life of the microfinance sector that match four steps in research development: genesis, childhood, adolescence, and maturity. The article discusses whether this evolution could lead to a decline. Finally, it presents the monograph, which is organized along thematic groups of chapters. The titles of the four parts of the book are: “Framing research on microfinance and financial inclusion,” “Social, environmental and financial performance,” “Targets for financial inclusion,” and “Institutional and technological design.” Each chapter is written by scholars whose expertise on financial inclusion and microfinance is recognized internationally. |
| Keywords: | Microfinance; Microcredit; Financial inclusion; Development; Social finance |
| JEL: | G21 G23 O16 G32 O19 |
| Date: | 2019–05–17 |
| URL: | http://d.repec.org/n?u=RePEc:sol:wpaper:2013/287460&r=all |
| By: | Bailey, Michael; Johnston, Drew; Kuchler, Theresa; Ströbel, Johannes; Wong, Arlene |
| Abstract: | We study the nature of peer effects in the market for new cell phones. Our analysis builds on de-identified data from Facebook that combine information on social networks with information on users' cell phone models. To identify peer effects, we use variation in friends' new phone acquisitions resulting from random phone losses and carrier-specific contract terms. A new phone purchase by a friend has a substantial positive and long-term effect on an individual's own demand for phones of the same brand, most of which is concentrated on the particular model purchased by the friend. We provide evidence that social learning contributes substantially to the observed peer effects. While peer effects increase the overall demand for cell phones, a friend's purchase of a new phone of a particular brand can reduce individuals' own demand for phones from competing brands---in particular those running on a different operating system. We discuss the implications of these findings for the nature of firm competition. We also find that stronger peer effects are exerted by more price-sensitive individuals. This positive correlation suggests that the elasticity of aggregate demand is substantially larger than the elasticity of individual demand. Through this channel, peer effects reduce firms' markups and, in many models, contribute to higher consumer surplus and more efficient resource allocation. |
| Keywords: | Demand Spillovers; peer effects; Social learning |
| JEL: | D4 L1 L2 M3 |
| Date: | 2019–05 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:13731&r=all |
| By: | Murphy, Daniel |
| Abstract: | From five-star hotels and Michelin Star restaurants, few industries signal their quality and unique selling points through the use of third-party seals like tourism. However, despite using seals and certifications in advertising being widespread, little academic research has been conducted into their effectiveness. Through the running of campaigns on Facebook’s Ad Manager for Indian airline Jet Airways, this study applies the Third-Party Seal Model to optimise campaign audiences to target the right prospects with the most effective message. Findings and a practical framework for optimal campaign delivery for the airline industry are presented. |
| Keywords: | Third-Party Seal Model; social media advertising; airline marketing; third-party seals; online advertising |
| JEL: | L83 L93 M37 |
| Date: | 2019–04–15 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:93886&r=all |
| By: | Manaa, Mehdi; Chimienti, Maria Teresa; Adachi, Mitsutoshi; Athanassiou, Phoebus; Balteanu, Irina; Calza, Alessandro; Devaney, Conall; Diaz Fernandez, Ester; Eser, Fabian; Ganoulis, Ioannis; Laot, Maxime; Philipp, Günther; Poignet, Raphael; Sauer, Stephan; Schneeberger, Doris; Stracca, Livio; Tapking, Jens; Toolin, Colm; Tyler, Carolyn; Wacket, Helmut |
| Abstract: | This paper summarises the outcomes of the analysis of the ECB Crypto-Assets Task Force. First, it proposes a characterisation of crypto-assets in the absence of a common definition and as a basis for the consistent analysis of this phenomenon. Second, it analyses recent developments in the crypto-assets market and unfolding links with financial markets and the economy. Finally, it assesses the potential impact of crypto-assets on monetary policy, payments and market infrastructures, and financial stability. The analysis shows that, in the current market, crypto-assets’ risks or potential implications are limited and/or manageable on the basis of the existing regulatory and oversight frameworks. However, this assessment is subject to change and should not prevent the ECB from continuing to monitor crypto-assets, raise awareness and develop preparedness. JEL Classification: E42, G21, G23, O33 |
| Keywords: | characterisation, crypto-assets, crypto-assets risks, monitoring |
| Date: | 2019–05 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbops:2019223&r=all |
| By: | Zhang, Yinjunjie; Xu, Zhicheng; Palma, Marco |
| Abstract: | The rapid development and diffusion of new technologies such as automation and artificial intelligence make life more convenient. At the same time, people may develop overdependence on technology to simplify everyday tasks or to reduce the level of effort required to accomplish them. We conduct a two-phase real-effort laboratory experiment to assess how external assistance affects subsequently revealed preferences for the convenience of a lower level of effort versus monetary rewards requiring greater effort. The results suggest that men treated with external help in the first phase tend to choose more difficult options with potentially higher monetary rewards. In contrast, after being treated with external help, women exhibit a stronger propensity to utilize the convenience of an easier task and are less likely to choose a more difficult option that carries higher potential earnings. |
| Keywords: | Gender difference, Reaction to help, Real effort |
| JEL: | C91 D81 J16 |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:93899&r=all |
| By: | Daron Acemoglu (MIT); Pascual Restrepo (Boston University) |
| Abstract: | We summarize a framework for the study of the implications of automation and AI on the demand for labor, wages, and employment. Our task-based framework emphasizes the displacement effect that automation creates as machines and AI replace labor in tasks that it used to perform. This displacement effect tends to reduce the demand for labor and wages. But it is counteracted by a productivity effect, resulting from the cost savings generated by automation, which increase the demand for labor in non-automated tasks. The productivity effect is complemented by additional capital accumulation and the deepening of automation (improvements of existing machinery), both of which further increase the demand for labor. These countervailing effects are incomplete. Even when they are strong, automation in- creases output per worker more than wages and reduce the share of labor in national income. The more powerful countervailing force against automation is the creation of new labor-intensive tasks, which reinstates labor in new activities and tends to in- crease the labor share to counterbalance the impact of automation. Our framework also highlights the constraints and imperfections that slow down the adjustment of the economy and the labor market to automation and weaken the resulting produc- tivity gains from this transformation: a mismatch between the skill requirements of new technologies, and the possibility that automation is being introduced at an excessive rate, possibly at the expense of other productivity-enhancing technologies. |
| Keywords: | AI, automation, displacement effect, labor demand, inequality, productivity, tasks, technology, wages |
| JEL: | J23 J24 |
| Date: | 2018–01–04 |
| URL: | http://d.repec.org/n?u=RePEc:bos:iedwpr:dp-298&r=all |
| By: | Gian Paolo Barbetta (Università Cattolica del Sacro Cuore; Dipartimento di Economia e Finanza, Università Cattolica del Sacro Cuore); Paolo Canino; Stefano Cima |
| Abstract: | The availability of cheap wi-fi internet connections has stimulated schools to adopt Web 2.0 platforms for teaching. Using social networks and micro-blogs, teachers aim to stimulate students’ participation in school activities and their achievement. Although anecdotal evidence shows a high level of teacher satisfaction with these platforms, only a small number of studies has produced rigorous estimates of their effects on students’ achievement. We contribute to the knowledge in this field by analyzing the impact of using micro-blogs as a teaching tool on the reading and comprehension skills of students. Thanks to a large-scale randomized controlled trial, we find that using Twitter to teach literature has an overall negative effect on students’ average achievement, reducing performance on a standardized test score by about 25 to 40% of a standard deviation. The negative effect is heterogeneous with respect to some students’ characteristics. More specifically, the use of this Web 2.0 application appears to have a stronger detrimental effect on students who usually perform better. |
| Keywords: | ICT, education, literature performance, RCT. |
| JEL: | I21 |
| Date: | 2019–05 |
| URL: | http://d.repec.org/n?u=RePEc:ctc:serie1:def081&r=all |
| By: | Laudenbach, Christine; Ungeheuer, Michael; Weber, Martin |
| Abstract: | We experimentally study how presentation formats for return distributions affect investors' diversification choices. We find that sampling returns alleviates correlationneglect and constitutes an effective way to improve financial decisions. When participants get a description of probabilities for outcomes of the joint return distribution, we confirm the common finding that investors neglect the correlation between assets in their diversification choices. However, when participants sample from the joint distribution, they incorporate correlation into choices as predicted by normative theory. Results are robust across three experiments with varying expertise and experience of participants (students vs. investors), and varying return distributions (discrete, continuous). |
| Keywords: | Correlation Neglect; Diversification; Fintech; Investment Decisions; Risk Taking |
| JEL: | C91 G02 G11 |
| Date: | 2019–05 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:13737&r=all |
| By: | Sudiksha Joshi |
| Abstract: | In this research paper, I have performed time series analysis and forecasted the monthly value of housing starts for the year 2019 using several econometric methods - ARIMA(X), VARX, (G)ARCH and machine learning algorithms - artificial neural networks, ridge regression, K-Nearest Neighbors, and support vector regression, and created an ensemble model. The ensemble model stacks the predictions from various individual models, and gives a weighted average of all predictions. The analyses suggest that the ensemble model has performed the best among all the models as the prediction errors are the lowest, while the econometric models have higher error rates. |
| Date: | 2019–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1905.07848&r=all |
| By: | Gogas, Periklis (Democritus University of Thrace, Department of Economics); Papadimitriou, Theophilos (Democritus University of Thrace, Department of Economics); Plakandaras, Vasilios (Democritus University of Thrace, Department of Economics); Gupta, Rangan (University of Pretoria) |
| Abstract: | The difficulty in modelling inflation and the significance in discovering the underlying data generating process of inflation is expressed in an ample literature regarding inflation forecasting. In this paper we evaluate nonlinear machine learning and econometric methodologies in forecasting the U.S. inflation based on autoregressive and structural models of the term structure. We employ two nonlinear methodologies: the econometric Least Absolute Shrinkage and Selection Operator (LASSO) and the machine learning Support Vector Regression (SVR) method. The SVR has never been used before in inflation forecasting considering the term–spread as a regressor. In doing so, we use a long monthly dataset spanning the period 1871:1–2015:3 that covers the entire history of inflation in the U.S. economy. For comparison reasons we also use OLS regression models as benchmark. In order to evaluate the contribution of the term-spread in inflation forecasting in different time periods, we measure the out-of-sample forecasting performance of all models using rolling window regressions. Considering various forecasting horizons, the empirical evidence suggests that the structural models do not outperform the autoregressive ones, regardless of the model’s method. Thus we conclude that the term-spread models are not more accurate than autoregressive ones in inflation forecasting. |
| Keywords: | U.S. Inflation; forecasting; Support Vector Regression; LASSO |
| JEL: | C22 C45 |
| Date: | 2019–05–15 |
| URL: | http://d.repec.org/n?u=RePEc:ris:duthrp:2016_003&r=all |
| By: | Heinisch, Dominik; Koenig, Johannes; Otto, Anne (Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany]) |
| Abstract: | "Only scarce information is available on doctorate recipients' career outcomes in Germany (BuWiN 2013). With the current information base, graduate students cannot make an informed decision whether to start a doctorate (Benderly 2018, Blank 2017). Administrative labour market data could provide the necessary information, is however incomplete in this respect. In this paper, we describe the record linkage of two datasets to close this information gap: data on doctorate recipients collected in the catalogue of the German National Library (DNB), and the German labour market biographies (IEB) from the German Institute of Employment Research. We use a machine learning based methodology, which 1) improves the record linkage of datasets without unique identifiers, and 2) evaluates the quality of the record linkage. The machine learning algorithms are trained on a synthetic training and evaluation dataset. In an exemplary analysis we compare the employment status of female and male doctorate recipients in Germany." (Author's abstract, IAB-Doku) ((en)) |
| JEL: | C81 E24 I20 |
| Date: | 2019–05–21 |
| URL: | http://d.repec.org/n?u=RePEc:iab:iabdpa:201913&r=all |
| By: | Christopher Kath; Florian Ziel |
| Abstract: | We discuss a concept denoted as Conformal Prediction (CP) in this paper. While initially stemming from the world of machine learning, it was never applied or analyzed in the context of short-term electricity price forecasting. Therefore, we elaborate the aspects that render Conformal Prediction worthwhile to know and explain why its simple yet very efficient idea has worked in other fields of application and why its characteristics are promising for short-term power applications as well. We compare its performance with different state-of-the-art electricity price forecasting models such as quantile regression averaging (QRA) in an empirical out-of-sample study for three short-term electricity time series. We combine Conformal Prediction with various underlying point forecast models to demonstrate its versatility and behavior under changing conditions. Our findings suggest that Conformal Prediction yields sharp and reliable prediction intervals in short-term power markets. We further inspect the effect each of Conformal Prediction's model components has and provide a path-based guideline on how to find the best CP model for each market. |
| Date: | 2019–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1905.07886&r=all |
| By: | Samuel Asante Gyamerah; Philip Ngare; Dennis Ikpe |
| Abstract: | The effects of weather on agriculture in recent years have become a major concern across the globe. Hence, the need for an effective weather risk management tool (weather derivatives) for agricultural stakeholders. However, most of these stakeholders are unwilling to pay for the price of weather derivatives (WD) because of product-design and geographical basis risks in the pricing models of WD. Using machine learning ensemble technique for crop yield forecasting and feature importance, the major major weather variable (average temperature) that affects crop yields are empirically determined. This variable (average temperature) is used as the underlying index for WD to eliminate product-design basis risks. A model with time-varying speed of mean reversion, seasonal mean, local volatility that depends on the average temperature and time for the contract period is proposed. Based on this model, pricing models for futures, options on futures, and basket futures for cumulative average temperature and growing degree-days are presented. Pricing futures on baskets reduces geographical basis risk as buyer's have the opportunity to select the most appropriate weather stations with their desired weight preference. With these pricing models, agricultural stakeholders can hedge their crops against the perils of weather. |
| Date: | 2019–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1905.07546&r=all |
| By: | Gawlitza, Joshua; Sturm, Timo; Spohrer, Kai; Henzler, Thomas; Akin, Ibrahim; Schönberg, Stefan; Borggrefe, Martin; Haubenreisser, Holger; Trinkmann, Frederik |
| Abstract: | Introduction: Quantitative computed tomography (qCT) is an emergent technique for diagnostics and research in patients with chronic obstructive pulmonary disease (COPD). qCT parameters demonstrate a correlation with pulmonary function tests and symptoms. However, qCT only provides anatomical, not functional, information. We evaluated five distinct, partial-machine learning-based mathematical models to predict lung function parameters from qCT values in comparison with pulmonary function tests. Methods: 75 patients with diagnosed COPD underwent body plethysmography and a dose-optimized qCT examination on a third-generation, dual-source CT with inspiration and expiration. Delta values (inspiration—expiration) were calculated afterwards. Four parameters were quantified: mean lung density, lung volume low-attenuated volume, and full width at half maximum. Five models were evaluated for best prediction: average prediction, median prediction, k-nearest neighbours (kNN), gradient boosting, and multilayer perceptron. Results: The lowest mean relative error (MRE) was calculated for the kNN model with 16%. Similar low MREs were found for polynomial regression as well as gradient boosting-based prediction. Other models led to higher MREs and thereby worse predictive performance. Beyond the sole MRE, distinct differences in prediction performance, dependent on the initial dataset (expiration, inspiration, delta), were found. Conclusion: Different, partially machine learning-based models allow the prediction of lung function values from static qCT parameters within a reasonable margin of error. Therefore, qCT parameters may contain more information than we currently utilize and can potentially augment standard functional lung testing. |
| Date: | 2019–03–21 |
| URL: | http://d.repec.org/n?u=RePEc:dar:wpaper:113226&r=all |
| By: | Ludovic Gouden\`ege; Andrea Molent; Antonino Zanette |
| Abstract: | In this paper we modify the Gaussian Process Regression Monte Carlo (GPR-MC) method introduced by Gouden\`ege et al. proposing two efficient techniques which allow one to compute the price of American basket options. In particular, we consider basket of assets that follow a Black-Scholes dynamics. The proposed techniques, called GPR Tree (GRP-Tree) and GPR Exact Integration (GPR-EI), are both based on Machine Learning, exploited together with binomial trees or with a closed formula for integration. Moreover, these two methods solve the backward dynamic programming problem considering a Bermudan approximation of the American option. On the exercise dates, the value of the option is first computed as the maximum between the exercise value and the continuation value and then approximated by means of Gaussian Process Regression. Both the two methods derive from the GPR-MC method and they mainly differ in the method used to approximate the continuation value: a single step of binomial tree or integration according to the probability density of the process. Numerical results show that these two methods are accurate and reliable and improve the results of the GPR-MC method in handling American options on very large baskets of assets. |
| Date: | 2019–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1905.09474&r=all |
| By: | Sevak Alaverdyan; Anna Zaharieva |
| Abstract: | In this study we investigate the link between the job search channels that workers use to find employment and the probability of occupational mismatch in the new job. Our specific focus is on differences between native and immigrant workers. We use data from the German Socio-Economic Panel (SOEP) over the period 2000-2014. First, we document that referral hiring via social networks is the most frequent single channel of generating jobs in Germany; in relative terms referrals are used more frequently by immigrant workers compared to natives. Second, our data reveals that referral hiring is associated with the highest rate of occupational mismatch among all channels in Germany. We combine these findings and use them to develop a theoretical search and matching model with two ethnic groups of workers (natives and immigrants), two search channels (formal and referral hiring) and two occupations. When modeling social networks we take into account ethnic and professional homophily in the link formation. Our model predicts that immigrant workers face stronger risk of unemployment and often rely on recommendations from their friends and relatives as a channel of last resort. Furthermore, higher rates of referral hiring produce more frequent occupational mismatch of the immigrant population compared to natives. We test this prediction empirically and confirm that more intensive network hiring contributes significantly to higher rates of occupational mismatch among immigrants. Finally, we document that the gaps in the incidence of referrals and mismatch rates are reduced among second generation immigrants indicating some degree of integration in the German labour market. |
| Keywords: | job search, referrals, social networks, occupational mismatch, immigration |
| JEL: | J23 J31 J38 J64 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:diw:diwsop:diw_sp1033&r=all |