Social Connections and Information Leakage: Evidence from Target Stock Price Run-up in Takeovers
Iftekhar Hasan, Lin Tong, An Yan
Journal of Financial Research,
forthcoming
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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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,
No. 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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