Home Valuation of a Company using Time Series Analysis
Article
Licensed
Unlicensed Requires Authentication

Valuation of a Company using Time Series Analysis

  • Philipp Pohl EMAIL logo
Published/Copyright: August 26, 2016

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.

JEL Classification: C53; G32

References

Bagheri, A., P. Mohammadi, and M. Akbari. 2014. “Financial Forecasting Using ANFIS Networks with Quantum-Behaved Particle Swarm Optimization.” Expert Systems with Applications 41 (14):6235–50.10.1016/j.eswa.2014.04.003Search in Google Scholar

Brown, L., and M. Rozeff. 1979. “Univariate Time-Series Models of Quarterly Accounting Earnings per Share: A Proposed Model.” Journal of Accounting Research 17 (1):179–89.10.2307/2490312Search in Google Scholar

Chen, S. 2007. “Measuring Business Cycle Turning Points in Japan with the Markov Switching Panel Model.” Mathematics and Computers in Simulation 76 (4):263–70.10.1016/j.matcom.2006.11.003Search in Google Scholar

Chen, J., and A. Gupta. 2012. Parametric Statistical Change Point Analysis. New York: Springer.10.1007/978-0-8176-4801-5Search in Google Scholar

Clements, M. 2006. “Forecasting with Breaks.” In Handbook of Economic Forecasting, edited by G. Elliott, C. Granger, and A. Timmermann, Volume 1, 605–57. North Holland.10.1016/S1574-0706(05)01012-8Search in Google Scholar

Faroqi, A. 2014. “ARIMA Model Building and Forecasting on Imports and Exports of Pakistan.” Pakistan Journal of Statistics and Operation Research 10 (2):157–68.10.18187/pjsor.v10i2.732Search in Google Scholar

Golyandina, N., and A. Korobeynikov. 2014. “Basic Singular Spectrum Analysis and Forecasting with R.” Computational Statistics and Data Analysis 71 (3):934–54.10.1016/j.csda.2013.04.009Search in Google Scholar

Hatting, M., and D. Uys. 2014. “In-Season Retail Sales Forecasting Using Survival Models.” Orion 30 (2):59–71.10.5784/30-2-153Search in Google Scholar

Hülss, J., N. Vogel, P. Pohl, D. Ratz, and R. Küstermann. 2012. “Gaussian Distributed Shareholder Value as a Tool for Value Based Management – Business Horizon.” Journal of Business and Policy Research 7 (3):123–39.Search in Google Scholar

Hwang, S. 2010. “Cross-Validation of Short-Term Productivity Forecasting Methodologies.” Journal of Construction Engineering and Management 136 (9):1037–46.10.1061/(ASCE)CO.1943-7862.0000230Search in Google Scholar

Kim, M., and W. Kross. 2005. “The Ability of Earnings to Predict Future Operating Cash Flows Has Been Increasing – Not Decreasing.” Journal of Accounting Research 43 (5):753–80.10.1111/j.1475-679X.2005.00189.xSearch in Google Scholar

Lorek, K., and G. Willinger. 2008. “Time-Series Properties and Predictive Ability of Quarterly Cah Flows.” Advances in Accounting 24 (1):65–71.10.1016/j.adiac.2008.05.010Search in Google Scholar

Lorek, K., and G. Willinger. 2009. “New Evidence Pertaining to the Prediction of Operating Cash Flows.” Review of Quantitative Finance and Accounting 32 (1):1–15.10.1007/s11156-007-0076-1Search in Google Scholar

Lorek, K., and G. Willinger. 2011. “Multi-Step-Ahead Quarterly Cash-Flow Prediction Models.” Accounting Horizons 25 (1):71–86.10.2308/acch.2011.25.1.71Search in Google Scholar

McIntosh, W. 1990. “Forecasting Cash Flows: Evidence From the Financial Literature.” The Appraisal Journal 78 (1):221–9.Search in Google Scholar

Muhsal, B. 2013. “Change-Point Methods for Multivariate Autoregressive Models and Multiple Structural Breaks in the Mean.” Karlsruhe.Search in Google Scholar

Steffensmeier, J., J. Freeman, M. Hitt, and J. Pevehouse. 2014. Time Series Analysis for Social Sciences. New York: Cambridge University Press.10.1017/CBO9781139025287Search in Google Scholar

Vogt, A., E. Mattfeldt, G. Satzger, L. Lüders, M. Piper, O. Gehb, and W. Jones. 2014. “Analytical Support for Predicting Cost in Complex Service Delivery Environments.” IBM Journal of Research and Development 58 (4):1–10.10.1147/JRD.2014.2327301Search in Google Scholar

Yip, H., H. Fan, and Y. Chiang. 2014. “Predicting the Maintenance Cost of Construction Equipment: Comparison Between General Regression Neuronal Network and Box-Jenkins Time Series Models.” Automation in Construction 38 (2):30–8.10.1016/j.autcon.2013.10.024Search in Google Scholar

Zadeh, N., M. Sepehri, and H. Farvaresh. 2014. “Intelligent Sales Prediction for Pharmaceutical Distribution Companies: A Data Mining Based Approach.” Mathematical Problems in Engineering 31 (1):1–15.Search in Google Scholar

Published Online: 2016-8-26
Published in Print: 2017-5-24

© 2017 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 9.9.2025 from https://www.degruyterbrill.com/document/doi/10.1515/jbvela-2015-0004/html
Scroll to top button