Scenario based merger & acquisition forecasting
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...
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| Format: | Article |
| Language: | English |
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Sciendo
2024-12-01
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| Series: | Management şi Marketing |
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| Online Access: | https://doi.org/10.2478/mmcks-2024-0026 |
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| _version_ | 1841536570232930304 |
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| author | Khowaja Kainat Saef Danial Sizov Sergej Härdle Wolfgang Karl |
| author_facet | Khowaja Kainat Saef Danial Sizov Sergej Härdle Wolfgang Karl |
| author_sort | Khowaja Kainat |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7bb328e06091494294832c6c7af3d520 |
| institution | Kabale University |
| issn | 2069-8887 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Management şi Marketing |
| spelling | doaj-art-7bb328e06091494294832c6c7af3d5202025-01-14T14:22:49ZengSciendoManagement şi Marketing2069-88872024-12-0119457960010.2478/mmcks-2024-0026Scenario based merger & acquisition forecastingKhowaja Kainat0Saef Danial1Sizov Sergej2Härdle Wolfgang Karl3Humboldt-Universität zu Berlin, Berlin, GermanyHumboldt-Universität zu Berlin, Berlin, GermanyThieme Group, GermanyHumboldt-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 RepublicWhile 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.https://doi.org/10.2478/mmcks-2024-0026mergers and acquisitionsforecastingnon stationary time seriespoisson modellingchange point detection |
| spellingShingle | Khowaja Kainat Saef Danial Sizov Sergej Härdle Wolfgang Karl Scenario based merger & acquisition forecasting Management şi Marketing mergers and acquisitions forecasting non stationary time series poisson modelling change point detection |
| title | Scenario based merger & acquisition forecasting |
| title_full | Scenario based merger & acquisition forecasting |
| title_fullStr | Scenario based merger & acquisition forecasting |
| title_full_unstemmed | Scenario based merger & acquisition forecasting |
| title_short | Scenario based merger & acquisition forecasting |
| title_sort | scenario based merger acquisition forecasting |
| topic | mergers and acquisitions forecasting non stationary time series poisson modelling change point detection |
| url | https://doi.org/10.2478/mmcks-2024-0026 |
| work_keys_str_mv | AT khowajakainat scenariobasedmergeracquisitionforecasting AT saefdanial scenariobasedmergeracquisitionforecasting AT sizovsergej scenariobasedmergeracquisitionforecasting AT hardlewolfgangkarl scenariobasedmergeracquisitionforecasting |