TL;DR
This paper introduces a novel statistical method for adaptive window selection in count data, improving the prediction of M&A events and supporting strategic planning with data analytics.
Contribution
It presents an automatic procedure for detecting intensity changes in count data, enhancing forecasting accuracy and managerial decision-making in various business contexts.
Findings
Robust to aberrant behavior in data
Accurately forecasts M&A events in tested sectors
Guides selection of fixed windows for forecasting
Abstract
Strategic planning in a corporate environment is often based on experience and intuition, although internal data is usually available and can be a valuable source of information. Predicting merger & acquisition (M&A) events is at the heart of strategic management, yet not sufficiently motivated by data analytics driven controlling. One of the main obstacles in using e.g. count data time series for M&A seems to be the fact that the intensity of M&A is time varying at least in certain business sectors, e.g. communications. We propose a new automatic procedure to bridge this obstacle using novel statistical methods. The proposed approach allows for a selection of adaptive windows in count data sets by detecting significant changes in the intensity of events. We test the efficacy of the proposed method on a simulated count data set and put it into action on various M&A data sets. It is…
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