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Title: A methodology for company valuation
Author: Schlueter, Oliver
ISNI:       0000 0004 2725 3614
Awarding Body: Brunel University
Current Institution: Brunel University
Date of Award: 2008
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This thesis presents an approach for company valuation by a replication portfolio of traded assets in discrete time. The model allows us to value companies with an uncertain cash flow stream without having to revert to any discount rates including premia. Modelling of asset values can be achieved in two steps: (i) Choosing a suitable stochastic process and calibrating its parameters to fit the historical asset time series behaviour, and (ii) generating a state space transition graph to implement the stochastic process dynamics in discrete time. For company valuation, a selected number of "assets" (economic, financial, and other factors) should be captured that may reasonably be assumed to influence future cash flows of the company. Each vertex of the transition graph represents a "state of the world" and is accompanied with a corresponding cash flow caused by the sales (or other company activities) at that vertex. These possible future company cash flows can be "replicated" (without the existence of the company) by investing in a self-financing portfolio of non-company assets at the beginning, and trading this portfolio as the future evolves. The minimum cost of such a self-financing portfolio equals the value of the company. A dynamic programming algorithm for this valuation problem has been derived in discrete time. Due to the fact that an exact duplication is not possible for all cases, the replication strategy will be generated by minimising the deviations in each state to approximate an exact replication. The company valuation algorithm in discrete time is based on two main ideas: The replication approach for arbitrage-free valuation as it is known for the valuation of contingency claims (Cox, Rubinstein 1985) as well as an optimisation to compute the least value replication portfolio following an approach originally established by Alexander Christofides (Christofides A. 2004). The research results derived in this thesis contribute to the further integration of some methodologies for contingent claim valuation and optimisation techniques. The derived algorithm has been applied for the valuation of companies with a high uncertainty in their expected cash flows (like start-up companies), and gives further insight in the valuation of non-traded companies. With the application of the derived company valuation algorithm, the limitations and shortcomings of determining the companies' weighted average cost of capital (WACC) can be by-passed. In a further step, the algorithm has been extended to calculate the potential value contribution of the companies' real options. Part of the contribution is the generalisation of the algorithm in the way that decision-making strategies from the capital markets, as well as strategic decisions from inside corporates can be implemented and evaluated. The optimal timing of the additional investment can be computed, and the attached additional value of the "optimal" execution of these investment options is calculated. The implementation of the algorithm is performed in C++. The extended algorithm has been applied for two high growth companies in the area of Life Sciences, confirming the applicability of the algorithm. Results will be reported for Qiagen and GPC Biotech.
Supervisor: Christofides, N.; Mitra, G. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available