Social Connections and Information Leakage: Evidence from Target Stock Price Run-up in Takeovers
Iftekhar Hasan, Lin Tong, An Yan
Journal of Financial Research,
im Erscheinen
Abstract
Does information leakage in a target's social networks increase its stock price prior to a merger announcement? Evidence reveals that a target with more social connections indeed experiences a higher pre-announcement price run-up. This effect does not exist during or after the merger announcement, or in windows ending two months before the announcement. It is more pronounced among targets with severe asymmetric information, and weaker when the information about the upcoming merger is publicly available prior to the announcement. It is also weaker in expedited deals such as tender offers.
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How Neighborhood Influences Shape College Choices and Academic Paths for Students: Insights from Croatia
Annika Backes, Dejan Kovač
Harvard Center for International Development,
2024
Abstract
Choosing a university and field of study is a key life decision that influences one’s lifelong earnings trajectory. Data shows that the share of individuals going to university is unequally distributed, and is lower among disadvantaged students. High-achieving students who are low income are less likely to opt for ambitious education paths, despite the high returns of education. Even among those students who decide to apply for college, the likelihood of whether they will apply to prestigious colleges or renowned study programs differs along the distribution of socioeconomic background. It does not only matter if you study, but also what and where you study, as there is a large variation in long-run outcomes, such as earnings, both between universities as well as between fields of study. Part of this mismatch can be attributed to unequal starting points for children, in terms of both institutional settings and the quality of information available within their close networks.
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IWH Research Network in Economics
IWH Research Network in Economics (IWH-ReNEc) Das Leibniz-Institut für Wirtschaftsforschung Halle führt seine Forschungsprojekte in Zusammenarbeit mit Wissenschaftlerinnen und…
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Aktuelle Konjunktur
Aktuelle Konjunktur Lokal und global: Das IWH stellt regelmäßig aktuelle Wirtschaftsdaten zur Verfügung - von der Situation der ostdeutschen Wirtschaft über die…
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Startseite
IWH-Insolvenztrend: 20-Jahres-Hoch bei Firmenpleiten Deutlich schneller als die amtliche Statistik liefert das IWH jeden Monat ein Lagebild vom bundesweiten Insolvenzgeschehen. Im…
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Archiv
Medienecho-Archiv 2021 2020 2019 2018 2017 2016 Dezember 2021 IWH: Ausblick auf Wirtschaftsjahr 2022 in Sachsen mit Bezug auf IWH-Prognose zu Ostdeutschland: "Warum Sachsens…
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Reports des European Forecasting Network (EFN)
Reports des European Forecasting Network (EFN) Das European Forecasting Network (EFN) war eine Gruppe von Konjunkturexperten verschiedener europäischer Forschungseinrichtungen…
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Courses
Courses Courses are organised in coordination with partner institutions within the Central-German Doctoral Program Economics (CGDE) network. IWH organises First-Year Courses in…
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Forecasting Economic Activity Using a Neural Network in Uncertain Times: Monte Carlo Evidence and Application to the
German GDP
Oliver Holtemöller, Boris Kozyrev
IWH Discussion Papers,
Nr. 6,
2024
Abstract
In this study, we analyzed the forecasting and nowcasting performance of a generalized regression neural network (GRNN). We provide evidence from Monte Carlo simulations for the relative forecast performance of GRNN depending on the data-generating process. We show that GRNN outperforms an autoregressive benchmark model in many practically relevant cases. Then, we applied GRNN to forecast quarterly German GDP growth by extending univariate GRNN to multivariate and mixed-frequency settings. We could distinguish between “normal” times and situations where the time-series behavior is very different from “normal” times such as during the COVID-19 recession and recovery. GRNN was superior in terms of root mean forecast errors compared to an autoregressive model and to more sophisticated approaches such as dynamic factor models if applied appropriately.
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CompNet Database
The CompNet Competitiveness Database The Competitiveness Research Network (CompNet) is a forum for high level research and policy analysis in the areas of competitiveness and…
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