Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

IRTG1792DP2020 026

Data Analytics Driven Controlling: bridging statistical modeling and managerial

Kainat Khowaja
Danial Saef
Sergej Sizov
Wolfgang Karl Härdle

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 robust to aberrant behaviour and
generates accurate forecasts for the evaluated business sectors. It also
provides guidance for an a-priori selection of fixed windows for forecasting.
Furthermore, it can be generalized to other business lines, e.g. for managing
supply chains, sales forecasts, or call center arrivals, thus giving managers
new ways for incorporating statistical modeling in strategic planning decisions.


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