The Determinants of Corporate Growth
Corporate Growth is a concept that has been widely treated in a specific way or as part of strategy theories, in definition and in econometric models and has also been studied in many different aspects and approaches. The author describes in depth the main variables affecting corporate growth and the underlying business processes. This empirical research has focused on Sales, Profit-Cash Flow, Risk, Created Shareholder Value, Market Value and Overall Performance econometric models. These panel data models are based on the 500 Companies of the Standard & Poor’s 500. The methodology used has been very strict in identifying exogenous variables, walking through the different alternative econometric models, discussing results, and, in the end, describing the practical implications in today’s business corporate management. We basically assume that the Functions/Departments act independently in the same company, many times with different objectives, and in this situation clear processes are key to clarify the situations, roles and responsibilities. We also assume that growth implies interactions among the different functions in a company and the CEO acts to lead and coach his immediate Directors as a referee of the key conflicts through his Operating Mechanism. The objective of this PhD Dissertation is to clarify the business priorities and identify the most relevant variables in every process leading to the highest efficiency in reaching a sustainable and profitable growth. It covers the lack of academic studies on the nature and specific driving factors of corporate growth and provides a working framework for Entrepreneurs and Management leading to the Company’s success.
|Date of creation:||23 Jun 2010|
|Date of revision:|
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Web page: https://mpra.ub.uni-muenchen.de
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- Andrew Ang & Geert Bekaert, 2001. "Stock Return Predictability: Is it There?," NBER Working Papers 8207, National Bureau of Economic Research, Inc.
- Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
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