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Data Analytics Driven Controlling: bridging statistical modeling and managerial intuition

Author

Listed:
  • Khowaja, Kainat
  • Saef, Danial
  • Sizov, Sergej
  • Härdle, Wolfgang Karl
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 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.

Suggested Citation

  • Khowaja, Kainat & Saef, Danial & Sizov, Sergej & Härdle, Wolfgang Karl, 2020. "Data Analytics Driven Controlling: bridging statistical modeling and managerial intuition," IRTG 1792 Discussion Papers 2020-026, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    • Handle: RePEc:zbw:irtgdp:2020026
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    References listed on IDEAS

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    1. Haccou, Patsy & Meelis, Evert & van de Geer, Sara, 1987. "The likelihood ratio test for the change point problem for exponentially distributed random variables," Stochastic Processes and their Applications, Elsevier, vol. 27, pages 121-139.
    2. Inoue, Atsushi & Jin, Lu & Rossi, Barbara, 2017. "Rolling window selection for out-of-sample forecasting with time-varying parameters," Journal of Econometrics, Elsevier, vol. 196(1), pages 55-67.
    3. Härdle, Wolfgang Karl & Mihoci, Andrija & Ting, Christopher Hian-Ann, 2014. "Adaptive order flow forecasting with multiplicative error models," SFB 649 Discussion Papers 2014-035, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    4. Kutoyants, Yury A. & Spokoiny, Vladimir, 1999. "Optimal choice of observation window for Poisson observations," Statistics & Probability Letters, Elsevier, vol. 44(3), pages 291-298, September.
    5. Giraitis, Liudas & Kapetanios, George & Price, Simon, 2013. "Adaptive forecasting in the presence of recent and ongoing structural change," Journal of Econometrics, Elsevier, vol. 177(2), pages 153-170.
    6. Phillip M. Yelland & Shinji Kim & Renée Stratulate, 2010. "A Bayesian Model for Sales Forecasting at Sun Microsystems," Interfaces, INFORMS, vol. 40(2), pages 118-129, April.
    7. Andrews, Donald W K & Ploberger, Werner, 1994. "Optimal Tests When a Nuisance Parameter Is Present Only under the Alternative," Econometrica, Econometric Society, vol. 62(6), pages 1383-1414, November.
    8. Wolfgang K. Härdle & Nikolaus Hautsch & Andrija Mihoci, 2015. "Local Adaptive Multiplicative Error Models for High‐Frequency Forecasts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(4), pages 529-550, June.
    9. Vojislav Maksimovic & Gordon Phillips & Liu Yang, 2013. "Private and Public Merger Waves," Journal of Finance, American Finance Association, vol. 68(5), pages 2177-2217, October.
    10. Pesaran, M. Hashem & Pick, Andreas & Pranovich, Mikhail, 2013. "Optimal forecasts in the presence of structural breaks," Journal of Econometrics, Elsevier, vol. 177(2), pages 134-152.
    11. Francis X. Diebold, 2015. "Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective on the Use and Abuse of Diebold-Mariano Tests," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(1), pages 1-1, January.
    12. Diebold, Francis X & Gunther, Todd A & Tay, Anthony S, 1998. "Evaluating Density Forecasts with Applications to Financial Risk Management," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 863-883, November.
    13. Spokoiny, Vladimir G., 1998. "Estimation of a function with discontinuities via local polynomial fit with an adaptive window choice," SFB 373 Discussion Papers 1998,1, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    14. Boris N. Oreshkin & Nazim Réegnard & Pierre L’Ecuyer, 2016. "Rate-Based Daily Arrival Process Models with Application to Call Centers," Operations Research, INFORMS, vol. 64(2), pages 510-527, April.
    15. Harford, Jarrad, 2005. "What drives merger waves?," Journal of Financial Economics, Elsevier, vol. 77(3), pages 529-560, September.
    16. Kenneth R. Ahern & Jarrad Harford, 2014. "The Importance of Industry Links in Merger Waves," Journal of Finance, American Finance Association, vol. 69(2), pages 527-576, April.
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    • C00 - Mathematical and Quantitative Methods - - General - - - General

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