Scenario based merger & acquisition forecasting

Khowaja Kainat, Saef Danial (), Sizov Sergej and Härdle Wolfgang Karl
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Khowaja Kainat: Humboldt-Universität zu Berlin, Berlin, Germany
Saef Danial: Humboldt-Universität zu Berlin, Berlin, Germany
Sizov Sergej: Thieme Group, Germany
Härdle Wolfgang Karl: Humboldt-Universität zu Berlin, BRC Blockchain Research Center, Berlin; Sim Kee Boon Institute, Singapore Management University, Singapore; NUS, Center of Competitiveness, Singapore; National Chiao Tung University, Prague, Czech Republic

Management & Marketing, 2024, vol. 19, issue 4, 579-600

Abstract: While there is no doubt that M&A activity in the corporate sector follows wave-like patterns, there is no uniquely accepted definition of such a “merger wave” in a time series context. Count-data time series models are often employed to measure M&A activity and merger waves are then defined as clusters of periods with an unusually high number of M&A deals retrospectively. However, the distribution of deals is usually not normal (Gaussian). More recently, different approaches that take into account the time-varying nature of M&A activity have been proposed, but still require the a-priori selection of parameters. We propose adapting the combination of the Local Parametric Approach and Multiplier Bootstrap to a count data setup in order to identify locally homogeneous intervals in the time series of M&A activity. This eliminates the need for manual parameter selection and allows for the generation of accurate forecasts without any manual input.

Keywords: mergers and acquisitions; forecasting; non stationary time series; Poisson modelling; change point detection (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:manmar:v:19:y:2024:i:4:p:579-600:n:1001

DOI: 10.2478/mmcks-2024-0026

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