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on Network Economics |
| By: | Nihar Shah |
| Abstract: | In many online domains, Sybil networks -- or cases where a single user assumes multiple identities -- is a pervasive feature. This complicates experiments, as off-the-shelf regression estimators at least assume known network topologies (if not fully independent observations) when Sybil network topologies in practice are often unknown. The literature has exclusively focused on techniques to detect Sybil networks, leading many experimenters to subsequently exclude suspected networks entirely before estimating treatment effects. I present a more efficient solution in the presence of these suspected Sybil networks: a weighted regression framework that applies weights based on the probabilities that sets of observations are controlled by single actors. I show in the paper that the MSE-minimizing solution is to set the weight matrix equal to the inverse of the expected network topology. I demonstrate the methodology on simulated data, and then I apply the technique to a competition with suspected Sybil networks run on the Sui blockchain and show reductions in the standard error of the estimate by 6 - 24%. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.17426 |
| By: | Massimiliano Fessina; Giulio Cimini; Tiziano Squartini; Pablo Astudillo-Est\'evez; Stefan Thurner; Diego Garlaschelli |
| Abstract: | Production networks constitute the backbone of every economic system. They are inherently fragile as several recent crises clearly highlighted. Estimating the system-wide consequences of local disruptions (systemic risk) requires detailed information on the supply chain networks (SCN) at the firm-level, as systemic risk is associated with specific mesoscopic patterns. However, such information is usually not available and realistic estimates must be inferred from available sector-level data such as input-output tables and firm-level aggregate output data. Here we explore the ability of several maximum-entropy algorithms to infer realizations of SCNs characterized by a realistic level of systemic risk. We are in the unique position to test them against the actual Ecuadorian production network at the firm-level. Concretely, we compare various properties, including the Economic Systemic Risk Index, of the Ecuadorian production network with those from four inference models. We find that the most realistic systemic risk content at the firm-level is retrieved by the model that incorporates information about firm-specific input disaggregated by sector, indicating the importance of correctly accounting for firms' heterogeneous input profiles across sectors. Our results clearly demonstrate the minimal amount of empirical information at the sector level that is necessary to statistically generate synthetic SCNs that encode realistic firm-specific systemic risk. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.02467 |
| By: | Ixandra Achitouv |
| Abstract: | Applying a network analysis to stock return correlations, we study the dynamical properties of the network and how they correlate with the market return, finding meaningful variables that partially capture the complex dynamical processes of stock interactions and the market structure. We then use the individual properties of stocks within the network along with the global ones, to find correlations with the future returns of individual S&P 500 stocks. Applying these properties as input variables for forecasting, we find a 50% improvement on the R2score in the prediction of stock returns on long time scales (per year), and 3% on short time scales (2 days), relative to baseline models without network variables. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.11759 |
| By: | Beatrice Federica Paolella; Tanya Araújo |
| Abstract: | The European Union Emissions Trading Scheme (EU ETS) has been instrumental in mitigating carbon dioxide (CO2) emissions across Europe since its initiation on January 1, 2005. CO2 has emerged as a traded commodity in the EU ETS, governed by market fundamentals similar to those in other global commodity markets. The interplay of supply and demand, driven by the allocation of allowances, plays thus a crucial role. Here, using real data, we developed networks of EU ETS to model exchanges of allowances between EU countries. Our results provide new insights into the topological structure of trading from 2005-2020. Combining the results from centrality measures, clustering and modularity, the EU ETS networks can be seen in the transition from a structure with few clusters to a structure characterized by numerous clusters organized around new nodes with acquired centrality. |
| Keywords: | Emissions Trading Scheme, Network Analysis, CO2 Trading, Allocation of Allowences. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:ise:remwps:wp03372024 |
| By: | Celso Brunetti; Matthew Carl; Jacob Gerszten; Chiara Scotti; Chaehee Shin |
| Abstract: | Does interconnectedness improve market quality? Yes.We develop an alternative network structure, the assets network: assets are connected if they are held by the same investors. We use several large datasets to build the assets network for the corporate bond market. Through careful identification strategies based on the COVID-19 shock and “fallen angels, ” we find that interconnectedness improves market quality especially during stress periods. Our findings contribute to the debate on the role of interconnectedness in financial markets and show that highly interconnected corporate bonds allow for risk sharing and require a lower compensation for risk. |
| Keywords: | Financial stability; Interconnectedness; Institutional investors; Big data |
| JEL: | C13 C55 C58 G10 |
| Date: | 2024–08–16 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:2024-66 |
| By: | Boris Nikolov (University of Lausanne; Swiss Finance Institute; European Corporate Governance Institute (ECGI)); Norman Schuerhoff (Swiss Finance Institute - HEC Lausanne); Zepeng Wang (University of Lausanne and Swiss Finance Institute) |
| Abstract: | Corporate financial leverage within competition networks is determined by both direct and indirect competitors. Using data on firms’ self reported competitors, we identify eleven stable competition communities within the U.S. economy, where firms are grouped into communities based on competitive interactions both within and across industries. We find a strong complementarity between a firm’s leverage and that of its community members, consistent with strategic interactions with both immediate peers and chain effects from the propagation of shocks affecting indirect peers. To achieve identification, we employ a granular instrumental variable approach. Our results highlight that firms’ financial strategies are shaped not only by direct competition but also by the broader competitive environment. |
| Keywords: | capital structure, strategic competition, financial complementarity, competitor networks |
| JEL: | G31 G32 L13 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp2445 |
| By: | FUJITANI, Ryosuke; ITO, Akitoshi; IWATA, Kiyonori |
| Abstract: | This study examines how sub-communities of institutional investors (‘cliques’) play a governance role through their coordinated engagement via-à-vis Japanese firms. Based on the five-percent ownership threshold for defining a link in the investor network, we identify several cliques among institutional investors investing in Japanese firms. We show that the largest clique of each firm votes on behalf of shareholder’s value at shareholder meetings. We also find that institutional investors in the same clique vote in the same direction more frequently than institutions which do not belong to the same clique. These findings suggest that institutional investors coordinate their voting behaviors to enhance value-increasing managerial decisions. |
| Date: | 2024–08–19 |
| URL: | https://d.repec.org/n?u=RePEc:hit:hmicwp:253 |