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on Network Economics |
| By: | Gupta, Abhimanyu (University of Essex); Kokas, Sotirios (University of Essex); Michaelides, Alexander (Imperial College London); Minetti, Raoul (Michigan State University, Department of Economics) |
| Abstract: | A large theoretical literature emphasizes financial networks, but empirical studies remain scarce. We exploit the overlapping bank portfolio structure of US syndicated loans to construct a financial network and characterize its evolution over time. Using techniques from spatial econometrics, we find large spillovers in lending conditions from peers’ decisions during normal times: a standard deviation increase in peer lending rates can increase a bank’s lending rate by 17 basis points. However, these spillovers vanish in a large recession. We rationalize these findings through the lens of a model of syndicate lending, where banks’ reliance on private signals rises during recessions. |
| Keywords: | Financial networks; spillovers; cost of lending; syndicated loan market |
| JEL: | C31 G21 |
| Date: | 2023–03–03 |
| URL: | http://d.repec.org/n?u=RePEc:ris:msuecw:2023_001&r=net |
| By: | Filipe Campante (Johns Hopkins University); Ruben Durante (ICREA-UPF); Andrea Tesei (Queen Mary University of London) |
| Abstract: | We survey the empirical literature in economics on the impact of media technologies on social capital. Motivated by a simple model of information and collective action, we cover a range of different outcomes related to social capital, from social and political participation to interpersonal trust, in its benign and destructive manifestations. The impact of media technologies hinges on their content ("information" vs "entertainment"), their effectiveness in fostering coordination, and the networks they create, as well as individual characteristics and media consumption choices. |
| Keywords: | Social capital; media; collective action; information; coordination; participation |
| JEL: | D71 D72 D74 D83 D84 Z13 |
| Date: | 2021–09–02 |
| URL: | http://d.repec.org/n?u=RePEc:qmw:qmwecw:930&r=net |
| By: | Chang Liu; Sandra Paterlini |
| Abstract: | Stock price prediction is a crucial element in financial trading as it allows traders to make informed decisions about buying, selling, and holding stocks. Accurate predictions of future stock prices can help traders optimize their trading strategies and maximize their profits. In this paper, we introduce a neural network-based stock return prediction method, the Long Short-Term Memory Graph Convolutional Neural Network (LSTM-GCN) model, which combines the Graph Convolutional Network (GCN) and Long Short-Term Memory (LSTM) Cells. Specifically, the GCN is used to capture complex topological structures and spatial dependence from value chain data, while the LSTM captures temporal dependence and dynamic changes in stock returns data. We evaluated the LSTM-GCN model on two datasets consisting of constituents of Eurostoxx 600 and S&P 500. Our experiments demonstrate that the LSTM-GCN model can capture additional information from value chain data that are not fully reflected in price data, and the predictions outperform baseline models on both datasets. |
| Date: | 2023–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2303.09406&r=net |
| By: | Yaqub, Ohid; Coburn, Josie; Moore, Duncan A.Q. |
| Abstract: | HIV/AIDS has been a major focus for research funders. The US National Institutes of Health (NIH) alone has spent over $70bn on HIV/AIDS. Such investments ushered in antiviral drugs, helping to reverse a rapidly growing HIV/AIDS pandemic. However, the idea that research can deliver unexpected benefits beyond its targeted field, in fact, predates HIV/AIDS to at least Vannevar Bush’s influential 1945 report. Cross-disease spillovers – research investments that yield benefits beyond the target disease – remains unexplored, even though it could inform both priority-setting and calculations of returns on research investments. To this end, we took a sample of NIH’s HIV grants and examined their publications. We analyzed 118, 493 publications and found that 62% of these were spillovers. We used Medical Subject Headings (MeSH) terms assigned to publications to explore the content of these spillovers, as well as to corroborate non-spillovers. We located spillovers on a network of MeSH co-occurrence, drawn from the broader universe of medical publications, for comparison. We found that HIV spillovers were unevenly distributed across disease-space, and often in close proximity to HIV, which, when discounted, reduced our spillovers estimate to 40%. We further reviewed 1, 000 grant–publication pairs from a local sample and 1, 000 pairs from a remote sample. For local spillovers, a quarter seemed to be unexpected, on the basis of on their grant description; for remote spillovers, that proportion increased to one third. We also found that the NIH funding institutes whose remits were most closely related to HIV/AIDS were less likely to produce spillovers than others. We discuss implications for theory and policy. |
| Date: | 2023–03–20 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:gcuhn&r=net |