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on Network Economics |
| By: | Alin Marius Andries (Alexandru Ioan Cuza University - Faculty of Economics and Business Administration); Steven Ongena (University of Zurich - Department of Banking and Finance; Swiss Finance Institute; KU Leuven; Centre for Economic Policy Research (CEPR)); Nicu Sprincean (Alexandru Ioan Cuza University of Iasi); Radu Tunaru (University of Sussex) |
| Abstract: | In this paper we gauge the degree of interconnectedness and quantify the linkages between global and other systemically important institutions, and the global financial system. We document that the two groups and the financial system become more interconnected during the global financial crisis when linkages across groups grow. In contrast, during tranquil times linkages within groups prevail. Global systemically important banks contribute most to system-wide distress, but are also most exposed. Other systemically important institutions bear more individual market risk. The two groups and the global financial system also co-vary for periods of up to 60 days. In sum, both groups perform in ways that defy any straightforward categorization. |
| Keywords: | systemic risk, interconnectedness, bank networks |
| JEL: | G21 D85 G01 |
| Date: | 2020–05 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp2040&r=all |
| By: | Pena, Paul John; Lim, Dickson |
| Abstract: | The debate within academic communities on the effectiveness of distance learning has never been as colorful and polarizing as they are today when higher education institutions (HEIs) shift instruction from the physical classroom to online platforms during a global novel coronavirus (Covid-19) pandemic. Strict social and physical distancing measures and the prolonged closure of schools aim to minimize the spread of Covid-19 carving a role for online learning as a solution to bridge the gap. Disparities in the access to high-speed internet, differences in devices used, and the environment in which both instruction and learning take place have led some to argue that the current conditions for online learning are not optimal nor inclusive. The psychological toll of living through a pandemic characterized by fear and anxiety further exacerbates learning conditions rendering attempts to bridge the gap unsurprisingly polarizing. To provide an economic basis for policies that encourage online learning amid this pandemic, we give an analytical and rational view of online learning. This brief presents the results of a theoretical exploration of learning with network externalities identifying optimal conditions that: (1) maximize returns to education, (2) grow the knowledge accumulated within a network, and (3) leverage the positive relationship between the size of the network and the wealth on knowledge accrued to learners. We provide a basis for the implementation of remote learning as a rational countermeasure to government policies that are likely to keep schools closed, supporting the argument that learning need not be quarantined, too. |
| Keywords: | coronavirus, connectivism, remote learning, human capital |
| JEL: | I1 I23 J24 O3 |
| Date: | 2020–04–29 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:100153&r=all |
| By: | Pena, Paul John; Lim, Dickson |
| Abstract: | Contemporary literature on how individuals learn in the 21st-century reveal critical differences from learning patterns in the mid-20th century–a period in which celebrated, pioneering works of Mincer, Becker and Ben-Porath on human capital were developed. Education and learning theories have evolved, but the prevailing human capital theories have not. Given continued technological progress, and the rise in available knowledge through the Internet, learning in networks is a distinct feature of the 21st-century industry. The connectivist theory of learning in the digital age is explored and substantiated. Using optimal control theory and dynamic optimisation, we define optimal conditions for knowledge generation and growth of learning networks. We find that knowledge per learner grows exponentially when the obsolescence rate of knowledge is less than the departure rate of learners from the learning network. We also find that a learning network will continue to grow as long as learners are sufficiently impatient and that technology sufficiently becoming obsolete faster. Furthermore, we show a positive relationship between the size of the network and wealth on knowledge. That is, as long as the remaining wealth on knowledge is increasing, the learning network will continue to grow over time. We present insights for policy consideration that address the necessary and sufficient conditions for sustained knowledge generation and the growth of the learning network. |
| Keywords: | human capital, learning, industry 4.0, networks |
| JEL: | J24 M53 O15 |
| Date: | 2019–06 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:100172&r=all |
| By: | Mohamed Belhaj (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique); Frédéric Deroïan (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique); Shahir Safi (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | We consider agents organized in an undirected network of local complementarities. A principal with a limited budget offers costly bilateral contracts in order to increase the sum of agents' effort. We study excess-effort linear payment schemes, i.e. contracts rewarding effort in excess to the effort made in absence of principal. The analysis provides the following main insights. First, for all contracting costs, the optimal unit returns offered to every targeted agent are positive and generically heterogeneous. This heterogeneity is due to the presence of outsiders, who create asymmetric interaction between contracting agents. Second, when contracting costs are low, it is optimal to contract with everyone and optimal unit returns are identical for all agents. Third, when contracting costs are sufficiently high, it becomes optimal to target a subset of agents, and optimal targeting can lead to NP-hard problems. In particular, when the intensity of complementarities is sufficiently low, a correspondence is established between optimal targeting and the densest k subgraph problem. Overall, the optimal targeting problem involves a trade-off between centrality and budget spending-central agents are influential, but are also more budget-consuming. These considerations can lead the principal to not target central agents. |
| Keywords: | networked synergies,aggregate effort,optimal group targeting,linear contract |
| Date: | 2020–04 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:halshs-02558397&r=all |
| By: | Jiexia Ye; Juanjuan Zhao; Kejiang Ye; Chengzhong Xu |
| Abstract: | Stock price movement prediction is commonly accepted as a very challenging task due to the extremely volatile nature of financial markets. Previous works typically focus on understanding the temporal dependency of stock price movement based on the history of individual stock movement, but they do not take the complex relationships among involved stocks into consideration. However it is well known that an individual stock price is correlated with prices of other stocks. To address that, we propose a deep learning-based framework, which utilizes recurrent neural network (RNN) and graph convolutional network (GCN) to predict stock movement. Specifically, we first use RNN to model the temporal dependency of each related stock' price movement based on their own information of the past time slices, then we employ GCN to model the influence from involved stock based on three novel graphs which represent the shareholder relationship, industry relationship and concept relationship among stocks based on investment decisions. Experiments on two stock indexes in China market show that our model outperforms other baselines. To our best knowledge, it is the first time to incorporate multi-relationships among involved stocks into a GCN based deep learning framework for predicting stock price movement. |
| Date: | 2020–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2005.04955&r=all |
| By: | Allen, Franklin (Imperial College London); Covi, Giovanni (Bank of England); Gu, Xian (Wharton School of University of Pennsylvania.); Kowalewski, Oskar (IESEG School of Management); Montagna, Mattia (European Central Bank) |
| Abstract: | This study documents significant differences in the interbank market lending and borrowing levels across countries. We argue that the existing differences in interbank market usage can be explained by the trust of the market participants in the stability of the country’s banking sector and counterparties, proxied by the history of banking crises and failures. Specifically, banks originating from a country that has lower level of trust tend to have lower interbank borrowing. Using a proprietary dataset on bilateral exposures, we investigate the Euro Area interbank network and find the effect of trust relies on the network structure of interbank markets. Core banks acting as interbank intermediaries in the network are more significantly influenced by trust in obtaining interbank funding, while being more exposed in a community can mitigate the negative effect of low trust. Country-level institutional factors might partially substitute for the limited trust and enhance interbank activity. |
| Keywords: | Interbank market; trust; networks; centrality; community detection |
| JEL: | G01 G21 G28 |
| Date: | 2020–05–14 |
| URL: | http://d.repec.org/n?u=RePEc:boe:boeewp:0862&r=all |
| By: | Liyang Tang |
| Abstract: | This paper selects the NARX neural network as the method through literature review, and constructs specific NARX neural networks under application scenarios involving macroeconomic forecasting, national goal setting and global competitiveness assessment. Through case studies on China, US and Eurozone, this study explores how those limited & partial exogenous inputs or abundant & comprehensive exogenous inputs, a small set of most relevant exogenous inputs or a large set of exogenous inputs covering all major aspects of the macro economy, whole area related exogenous inputs or both whole area and subdivision area related exogenous inputs specifically affect the forecasting performance of NARX neural networks for specific macroeconomic indicators or indices. Through the case study on Russia this paper explores how the limited & most relevant exogenous inputs set or the abundant & comprehensive exogenous inputs set specifically influences the prediction performance of those specific NARX neural networks for national goal setting. Finally, comparative studies on the application of NARX neural networks for the forecasts of Global Competitiveness Indices (GCIs) of various economies are conducted, in order to explore whether the specific NARX neural network trained on the basis of the GCI related data of some economies can make sufficiently accurate predictions about GCIs of other economies, and whether the specific NARX neural network trained on the basis of the data of some type of economies can give more accurate predictions about GCIs of the same type of economies than those of different type of economies. Based on all of the above successful application, this paper provides policy recommendations on applying fully trained NARX neural networks that are assessed as qualified to assist or even replace the deductive and inductive abilities of the human brain in a variety of appropriate tasks. |
| Date: | 2020–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2005.08735&r=all |
| By: | Lukas Kiessling (Max Planck Institute for Research on Collective Goods, Bonn); Jonathan Norris (University of Strathclyde) |
| Abstract: | This paper studies how peers in school affect students’ mental health. Guided by a theoretical framework, we find that increasing students’ relative ranks in their cohorts by one standard deviation improves their mental health by 6% of a standard deviation conditional on own ability. These effects are more pronounced for low-ability students, persistent for at least 14 years, and carry over to economic long-run outcomes. Moreover, we document a strong asymmetry: Students who receive negative rather than positive shocks react more strongly. Our findings therefore provide evidence on how the school environment can have long-lasting consequences for the well-being of individuals. |
| Keywords: | Peer Effects, Mental Health, Depression, Rank Effects |
| JEL: | I21 I14 J24 |
| Date: | 2020–06 |
| URL: | http://d.repec.org/n?u=RePEc:mpg:wpaper:2020_12&r=all |
| By: | Mikhail Martynovich; Josef Taalbi |
| Abstract: | This study investigates how related variety in the regional employment mix affects the innovation output of a region. Departing from the idea of recombinant innovation, previous research has argued that related variety enhances regional innovation as inter-industry knowledge spillovers occur more easily between different but cognitively similar industries. This study combines a novel dataset and related variety measures based on network theory, which allows a more nuanced perspective on the relationship between related variety and regional innovation. The principal novelty of the paper lies in employing new data on product innovations commercialised by Swedish manufacturing firms between 1970 and 2013. In this respect, it allows a direct measure of regional innovation output as compared to patent measures, usually employed in similar studies. The second contribution of this paper is that we employ network-topology based measures of related variety that allow us to measure relatedness as the recombination rather than direct flow of knowledge. We argue that this measure comes closer to the notion of innovation as spurred by recombination and show that this measure is a superior predictor of innovation activity. |
| Keywords: | related variety, relatedness, innovation, network analysis |
| JEL: | L16 O31 R11 R12 |
| Date: | 2020–03 |
| URL: | http://d.repec.org/n?u=RePEc:egu:wpaper:2015&r=all |