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Valuation of a Company using Time Series Analysis

Author

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  • Pohl Philipp

    (Department of Business, Cooperative State University Karlsruhe, Erzbergerstrasse 121, 76133Karlsruhe, Germany)

Abstract

In this paper we present an approach to value-based management of companies using time series analysis. We present a technique for projecting cash flows in order to calculate the company value using time series analysis. We consider a new, indirect approach and a direct approach of projecting cash flows. We analyse both models from the perspective of value-based management. Finally, company value is calculated for both models, as a point estimate and as a distribution function respectively. As shown in the article, the distribution function of corporate value is a normal distribution function. On this basis, it is possible to apply all instruments of value-at-risk analysis.

Suggested Citation

  • Pohl Philipp, 2017. "Valuation of a Company using Time Series Analysis," Journal of Business Valuation and Economic Loss Analysis, De Gruyter, vol. 12(1), pages 1-39, February.
    • Handle: RePEc:bpj:jbvela:v:12:y:2017:i:1:p:1-39:n:3
    DOI: 10.1515/jbvela-2015-0004
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    References listed on IDEAS

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    1. Myungsun Kim & William Kross, 2005. "The Ability of Earnings to Predict Future Operating Cash Flows Has Been Increasing—Not Decreasing," Journal of Accounting Research, Wiley Blackwell, vol. 43(5), pages 753-780, December.
    2. Chen, Shyh-Wei, 2007. "Measuring business cycle turning points in Japan with the Markov Switching Panel model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 76(4), pages 263-270.
    3. Brown, Ld & Rozeff, Ms, 1979. "Univariate Time-Series Models Of Quarterly Accounting Earnings Per Share - Proposed Model," Journal of Accounting Research, Wiley Blackwell, vol. 17(1), pages 179-189.
    4. Golyandina, Nina & Korobeynikov, Anton, 2014. "Basic Singular Spectrum Analysis and forecasting with R," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 934-954.
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    More about this item

    Keywords

    time series analysis; value-at-risk analysis; value-based management;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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