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on Network Economics |
| By: | Accominotti, Olivier; Lucena-Piquero, Delio; Ugolini, Stefano |
| Abstract: | This article studies the impact of intermediaries’ disappearance on firms’ access to the sterling money market during the first globalization era of 1880-1914. We propose a new methodology to assess intermediaries’ substitutability in financial networks featuring higher-order structures (credit intermediation chains). We represent the financial network as a hyperstructure and each credit intermediation chain as a hyperedge. This approach allows us to assess how the failure of intermediaries affects network connectivity. We apply this methodology to a unique dataset documenting the network structure of the sterling money market in the year 1906. Our results reveal that the failure of individual money market actors could only cause limited damage to the network as intermediaries were highly substitutable. These findings suggest that an international financial network without highly systemic nodes can emerge even at a time of global economic integration. |
| Keywords: | bills of exchange; financial networks; hypergraphs; hyperstructures; intermediation chains; systemic risk |
| JEL: | D85 E42 F30 G20 N20 |
| Date: | 2023–08–01 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:119896 |
| By: | Gibbs, Michael (University of Chicago); Mengel, Friederike (University of Essex); Siemroth, Christoph (University of Essex) |
| Abstract: | Using data from over 28, 000 innovators within a firm, we study how network position affects innovation, measured by the quality of ideas proposed in a formal suggestion system. Network degree is associated with higher quality ideas. Bridging across structural holes is negatively related to idea quality in the short run, conditional on degree, but has positive effects in the medium run. Bridging also has positive and persisting effects on the quality of colleagues’ ideas, suggesting a positive externality from ‘brokers.’ Network size is not related to idea quality, after controlling for degree and bridging. Compared to working from the office, remote work leads to lower average network degree and bridging. This weakening of networks may explain the reduced quality of innovation during remote work found in prior literature. |
| Keywords: | working from home, network centrality, structural holes, innovation, networks, hybrid work |
| JEL: | D7 D8 O3 |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17966 |
| By: | Yunming Hui (University of Amsterdam); Inez Maria Zwetsloot (University of Amsterdam); Simon Trimborn (University of Amsterdam and Tinbergen Institute); Stevan Rudinac (University of Amsterdam) |
| Abstract: | Social network platforms like Reddit are increasingly impacting real-world economics. Meme stocks are a recent phenomena where price movements are driven by retail investors organising themselves via social networks. To study the impact of social networks on meme stocks, the first step is to analyse these networks. Going forward, predicting meme stocks' returns would require to predict dynamic interactions first. This is different from conventional link prediction, frequently applied in e.g. recommendation systems. For this task, it is essential to predict more complex interaction dynamics, such as the exact timing and interaction types like loops. These are crucial for linking the network to meme stock price movements. Dynamic graph embedding (DGE) has recently emerged as a promising approach for modeling dynamic graph-structured data. However, current negative sampling strategies, an important component of DGE, are designed for conventional dynamic link prediction and do not capture the specific patterns present in meme stock-related social networks. This limits the training and evaluation of DGE models in analysing such social networks. To overcome this drawback, we propose novel negative sampling strategies based on the analysis of real meme stock-related social networks and financial knowledge. Our experiments show that the proposed negative sampling strategy can better evaluate and train DGE models targeted at meme stock-related social networks compared to existing baselines. |
| Date: | 2025–01–24 |
| URL: | https://d.repec.org/n?u=RePEc:tin:wpaper:20250003 |
| By: | San Román, Diego |
| Abstract: | Research in anthropology and neuroscience has shown that people have a cognitive limit on the number of stable relationships they can maintain. In this spirit, we consider a network formation game in which the cost of link formation is increasing in the agent's degree. In this class of games, as opposed to commonly studied games with a fixed cost of link formation, the order in which agents form the network (order of play) determines its final structure. In particular, we find that only certain orders of play can explain the formation of circle and complete bipartite networks. We also find that there is multiplicity of equilibria only when marginal costs of link formation are intermediate. Our results show as well that some orders of play are better than others for predicting the equilibrium structure when it is not unique, and that playing last is usually harmful. |
| Keywords: | sequential network formation, pairwise stability, order of play, costs of link formation increasing in degree |
| JEL: | C72 D85 |
| Date: | 2025–07–10 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:125309 |
| By: | Cameron Cornell; Lewis Mitchell; Matthew Roughan |
| Abstract: | Causal networks offer an intuitive framework to understand influence structures within time series systems. However, the presence of cycles can obscure dynamic relationships and hinder hierarchical analysis. These networks are typically identified through multivariate predictive modelling, but enforcing acyclic constraints significantly increases computational and analytical complexity. Despite recent advances, there remains a lack of simple, flexible approaches that are easily tailorable to specific problem instances. We propose an evolutionary approach to fitting acyclic vector autoregressive processes and introduces a novel hierarchical representation that directly models structural elements within a time series system. On simulated datasets, our model retains most of the predictive accuracy of unconstrained models and outperforms permutation-based alternatives. When applied to a dataset of 100 cryptocurrency return series, our method generates acyclic causal networks capturing key structural properties of the unconstrained model. The acyclic networks are approximately sub-graphs of the unconstrained networks, and most of the removed links originate from low-influence nodes. Given the high levels of feature preservation, we conclude that this cryptocurrency price system functions largely hierarchically. Our findings demonstrate a flexible, intuitive approach for identifying hierarchical causal networks in time series systems, with broad applications to fields like econometrics and social network analysis. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.12806 |
| By: | Nitsova, Silviya (University of Manchester) |
| Abstract: | State capture by extremely wealthy elites is a widespread phenomenon in developing democracies, yet the mechanisms through which it works and the impact it has on political and policy outcomes remain poorly understood. I develop a network-based approach to studying captured institutions. Focusing on the national legislature and using social network and regression analyses of unique quantitative data and original interview-based evidence on the case of Ukraine (2014-2022), I demonstrate that oligarchs seek to defend their wealth by promoting as members of parliament individuals who are linked to them via interpersonal ties. The connections between oligarchs and legislators take the form of a highly fragmented, weakly connected, and decentralized network with distinct clusters, in which oligarchs occupy central positions, and influence the adoption of policies related to oligarchs' economic interests. The study has important implications for the scholarship on money in politics, oligarchy, state capture, political connections, neopatrimonialism, legislative politics, political parties, and political representation. |
| Date: | 2025–06–23 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:k27ez_v1 |
| By: | Mahdi Kohan Sefidi |
| Abstract: | Financial crises often occur without warning, yet markets leading up to these events display increasing volatility and complex interdependencies across multiple sectors. This study proposes a novel approach to predicting market crises by combining multilayer network analysis with Long Short-Term Memory (LSTM) models, using Granger causality to capture within-layer connections and Random Forest to model interlayer relationships. Specifically, we utilize Granger causality to model the temporal dependencies between market variables within individual layers, such as asset prices, trading values, and returns. To represent the interactions between different market variables across sectors, we apply Random Forest to model the interlayer connections, capturing the spillover effects between these features. The LSTM model is then trained to predict market instability and potential crises based on the dynamic features of the multilayer network. Our results demonstrate that this integrated approach, combining Granger causality, Random Forest, and LSTM, significantly enhances the accuracy of market crisis prediction, outperforming traditional forecasting models. This methodology provides a powerful tool for financial institutions and policymakers to better monitor systemic risks and take proactive measures to mitigate financial crises. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.11019 |
| By: | Emre Yilmaz; Selin Demir; Aylin Karaca; Lila Moore (Department of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA) |
| Abstract: | This paper proposes the utilization of Dynamic Bayesian Networks for modeling Liquidity Preference-Money Supply, aiming to address the pressing need for advanced tools to analyze economic dynamics. The current research landscape lacks efficient methods to account for the intricate relationships and uncertainties inherent in monetary systems, posing significant challenges for accurate modeling and forecasting. In response, this study introduces a novel approach that leverages Dynamic Bayesian Networks to capture the complex interactions between liquidity preferences and money supply, offering a more comprehensive and adaptable framework for economic analysis. By integrating this innovative methodology, the paper advances the understanding of monetary dynamics and provides valuable insights for policymakers and researchers in the field. |
| Keywords: | Dynamic Bayesian Networks Liquidity Preference Money Supply Economic Analysis Monetary Dynamics, Dynamic Bayesian Networks, Liquidity Preference, Money Supply, Economic Analysis, Monetary Dynamics |
| Date: | 2025–02–23 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05086735 |
| By: | Macchiati, Valentina; Cappiello, Lorenzo; Giuzio, Margherita; Ianiro, Annalaura; Lillo, Fabrizio |
| Abstract: | We propose a novel framework to assess systemic risk stemming from the inadequate liquidity preparedness of non-bank financial institutions (NBFIs) to derivative margin calls. Unlike banks, NBFIs may struggle to source liquidity and meet margin calls during periods of significant asset price fluctuations, potentially triggering asset fire sales and amplifying market volatility. We develop a set of indicators and statistical methods to assess liquidity preparedness and examine risk transmission through common asset holdings and counterparty exposures. Applying our framework to euro area NBFIs during the Covid-19 turmoil and the 2022–2023 monetary tightening, we observe an increase in distressed entities, which, in turn, seem to exhibit more liquidity-driven selling behaviours than their non-distressed peers. Network analysis suggests that certain counterparties of distressed entities appear particularly vulnerable to margin call-induced liquidity shocks. Our framework offers policymakers valuable tools to enhance the monitoring and resilience of the NBFI sector. JEL Classification: C02, E52, G01, G11, G23 |
| Keywords: | derivative margin calls, financial stability, liquidity risk, network analysis, non-bank financial institutions |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20253074 |
| By: | Goghie, Alexandru-Stefan |
| Abstract: | This paper explores the symbiotic relationship between the Cayman Islands and the centrality of the United States (US) in global financial networks, using their connection as a test case for a broader theory of how infrastructural power of states is achieved through transnational and networked strategies. The legal and financial infrastructure of the Cayman Islands is extensively used by US financial institutions. This infrastructure supports the development of a significantly US-centric fund industry, facilitating substantial investments into US capital markets. Additionally, it serves as a global conduit, channelling funds from regions such as Asia and Latin America into US markets, streamlining the process by which foreign investors acquire US securities, and supporting the development of complex USD-denominated financial products. This dynamic enhances the depth, liquidity, and complexity of US capital markets, thereby reinforcing US centrality in global financial networks and bolstering its geopolitical power through financial diplomacy, economic sanctions, regulatory influence, and control over critical financial infrastructure. The relationship underscores the infrastructural power of the Cayman Islands, whose financial and legal framework is essential for sustaining and amplifying US centrality. Consequently, this paper aims to integrate the transnational perspective on infrastructural power within the International Political Economy (IPE) and Geopolitics literature, demonstrating how the Cayman Islands function as a multifaceted networked site that strengthens, projects, and sustains US state power on a global scale. |
| Date: | 2024–10–06 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:acnrb_v1 |
| By: | Antero Alves Pereira Neto (Universidade Federal de Uberlândia); Carlos Bianchi (Universidad de la República (Uruguay). Facultad de Ciencias Económicas y de Administración. Instituto de Economía); Marisa dos Reis Azevedo Botelho (Universidade Federal de Uberlândia) |
| Abstract: | With the global emergency triggered by the oil crisis and the climate conferences initiated in the 1970s, many countries around the world found themselves compelled to seek alternatives to oil. This led, particularly in the transport sector—one of the largest consumers of oil and emitters of pollutants—to developments aimed at enabling plant-based biofuels, fleet electrification, and the use of alternative fuels, such as hydrogen. Hydrogen, which can be produced through various methods, from oil transformation to molecular water splitting, emerges as a key prospect for achieving the full decarbonization of the global economy. However, the challenges of making it widespread encounter barriers that remain difficult to overcome. Using the methodology of social network analysis, this study aims to map the main trajectory of patents involved in consolidating the processes for hydrogen production through electrolysis, specifically for applications in the transport sector—a sustainable method with potential for widespread adoption due to its high energy efficiency. The results reveal the prevalence of patents that combine electrolytic transformation with internal combustion systems reliant on fossil fuels, an outcome unexpected from a sustainability standpoint. These findings underscore the need to identify a secondary trajectory with clearer advancements toward sustainability. This research aligns with Sustainable Development Goals (SDGs) 7, 13, and 11. |
| Keywords: | Hydrogen, Electrolysis, Hydrogen Economy, Sustainability |
| JEL: | O25 O14 Q58 |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:ulr:wpaper:dt-17-24 |