Mergers and acquisitions (M&As) are mainly a mechanism used in the Latin American banking industry to carry out business consolidation. This paper focuses on the effect of M&A announcements on stocks of Latin American banks and their rivals between 2000 and 2019. We evaluate two impacts of M&A announcements: impacts on cumulative abnormal returns (CAR) and impacts on event-induced variance (EIV). We use the GARCH-based event-study method. We find that acquirers and target banks have a statistically significant CAR, however, the sign is inconclusive. Rivals of acquirers and targets are not affected by M&A announcements. In general, we observe that EIV is negative for acquirers, targets, and rivals. Finally, we estimate a multivariate GARCH model to isolate the effects of co-movements of volatility between the acquirer and the target, and we find that the results remain qualitatively equal.
This paper studies the distribution of the firm size for the Colombian economy showing evidence against the Gibrat’s law, which assumes a stable lognormal distribution. On the contrary, we propose a lognormal expansion that captures deviations from the lognormal distribution with additional terms that allow a better fit at the upper distribution tail, which is overestimated according to the lognormal distribution. As a consequence, concentration indexes should be addressed consistently with the lognormal expansion. Through a dynamic panel data approach, we also show that firm growth is persistent and highly dependent on firm characteristics, including size, age, and leverage −these results neglect Gibrat’s law for the Colombian case.
This paper proposes a semi-nonparametric (SNP) generalization of the lognormal distribution for studying firm size and providing accurate measures for economic concentration and inequality in terms of the Gini index adjusted to flexible functional forms. The empirical application for a sample of 1,772 Colombian companies from 2002 to 2015 shows that the log-SNP provides a better fit to firm size distribution, especially for the extreme quantiles, for which lognormal distribution overestimates economic concentration. In addition, dynamic panel model estimates indicate that firm characteristics, including size, age, and leverage, are determining factors in explaining firm growth, thus rejecting Gibrat's law.
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