The Stock Market Reaction to Mergers and Acquisitions: Evidence from the Banking Industry

Abstract

This paper focuses on the effect of mergers and acquisitions (M&As) announcements on the 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, finding that acquirers and target banks have a statistically significant CAR and that their rivals and targets are not affected by M&A announcements. We observe that EIV is negative for acquirers, targets, and rivals. Finally, in a robustness exercise, we estimate a multivariate GARCH model, finding that the results remain qualitatively equal.

RESUMEN

Este trabajo se centra en el efecto que los anuncios de Fusiones y Adquisiciones (M&As en la sigla en inglés), tienen sobre las acciones de los bancos latinoamericanos y sus rivales entre 2000 y 2019. Evaluamos dos impactos de los anuncios M&A: los impactos sobre los retornos anormales acumulados (CAR en la sigla en inglés), y los impactos sobre la varianza inducida por eventos (EIV en la sigla en inglés). Empleamos el método de estudio de eventos basado en GARCH, encontrando que los adquirentes y los bancos objetivos tienen un CAR estadísticamente significativo y que sus rivales y objetivos no se ven afectados por los anuncios de fusiones y adquisiciones. Notamos que el EIV es negativo para los adquirentes, los objetivos y los rivales. Por último, en un ejercicio de robustez, estimamos un modelo GARCH multivariante, encontrando que los resultados siguen siendo cualitativamente iguales.

RESUMO

No presente trabalho enfocamos o efeito dos anúncios de Fusões e Aquisições (M&As na sigla em inglês) nas ações de bancos da América Latina e de seus rivais, entre 2000 e 2019. Avaliamos dois impactos de anúncios de M&A: o impacto no retorno anormal acumulado (CAR na sigla em inglês) e o impacto na variância introduzida por evento (EIV na sigla em inglês). Usando o método de estudo de eventos baseado em modelo GARCH, constatamos que os bancos alvo e os adquirentes apresentam um CAR estatisticamente significativo, e que seus rivais e alvos não são afetados pelos anúncios de M&A. Observamos que a EIV é negativa para adquirentes, alvos e rivais. Por fim, no exercício de robustez, estimamos um modelo GARCH com múltiplas variáveis e verificamos que os resultados permaneceram qualitativamente iguais.

Keywords:

• Emerging markets
• GARCH event study
• Latin America
• banking industry

Acknowledgments

The authors are grateful to acknowledge the funding received from Universidad EAFIT [project 828-000019].

Disclosure statement

The authors declare that they have no conflict of interest.

Notes

1 Thomson Reuters Eikon. (2019). [Number of mergers and acquisitions in the banking industry, 1980-2019] https://amers1.apps.cp.thomsonreuters.com/web/apps/SearchAdvanced?state=1608470666058&universe=MA&search=SearchAllMergersAcquisitions

2 For a comprehensive review of M&As in the banking industry, see Berger et al. (1999) and De Young et al. (2009).

3 Thomson Reuters Eikon. (2019). [Number of mergers and acquisitions in the banking industry, 1980-2019] https://amers1.apps.cp.thomsonreuters.com/web/apps/SearchAdvanced?state=1608470666058&universe=MA&search=SearchAllMergersAcquisitions

4 Literature related to this topic can be found in Humphery-Jenner et al. (2017), Nain and Wang (2018), and Piloff and Santomero (1998).

5 To choose the sample, we based on representative countries of the Latin American stock market. Thus, we selected those that compose the MSCI Emerging Markets Latin America Index. Between 2000 and 2019, there were 370 M&A deals in the Latin American banking industry. Of these, 257 (70%) were from banks in Brazil, Chile, Colombia, Mexico, or Peru.

6 One of the most challenging activities in this research was the news search. We identified very precisely the day where the first rumor of the transaction occurred by gleaning financial newspapers, Bloomberg news, and Thomson Reuters EIKON database and contrasting the information between them.

7 Following Cortés et al. (2015) and Savickas (2003).

8 BOVESPA in Brazil, IPSA in Chile, COLCAP in Colombia, IPC in Mexico, and IGBVL in Peru.

9 We also compared different GARCH family models based on information criteria for model selection and statistical inference when we analyzed the conditional volatility estimation. We run an EGARCH; however, the result remains qualitatively the same for this alternative specification.

10 We report only the event window results [0,0], and the results of the events window [-1,1] are qualitatively the same. However, for some individual events, the EIV coefficient for window [-1,1] is not statistically significant. When the event window widens, both the magnitude and significance weaken, and volatility might not be adequately captured. A similar effect is reported by (Elyasiani et al., 2016).

11 Peru does not have events that can be analyzed, considering the criteria that we apply.

12 We use the canonical model proposed by Bollerslev et al. (1988).