Perspectives

slightly positive number. Again this is due to the possible use of abandon options which can terminate projected loss-making projects. As a fuzzy valuation per definition cannot estimate projects to have a negative value as analyzed in part 4 it is necessary to settle on a minimum threshold value which the valuation should exceed in order to offset the unavoidable costs related to the termination of a project as discussed in section 6.7. In the clean real option valuation performed in section 6.5 the fuzzy approach again demonstrates its ability to value the value of managerial flexibility. The binomial tree setting shows how the diverse development of the potential sales can be better captured and valued with a fuzzy approach. The results obtained for all three valuation perspectives are considered to be robust as the sensitivity analysis in section 6.6 showed an unequivocal result when changing the key input variables.

To answer our primary research question our findings in part 5 confirm that the use of fuzzy numbers to perform a real option valuation of a biotech project is well applicable compared with the traditional valuation methods because it captures the real option thinking without the normal difficulties of implementing it for practitioners. It can be relatively easily applied to different levels of valuations and is therefore applicable for practitioners both in companies with advanced valuation approaches and companies with more simple valuation practices. For practitioners with a simple valuation approach the fuzzy event tree is recommended as it combines the inclusion of a risk perspective as well as the value of managerial flexibility with an uncomplicated

implementation. For more advanced practitioners the fuzzy binomial tree valuation could be interesting to apply as it stresses the managerial value stemming from the possible excessive development in the underlying asset.

Reflections on the value of real option analysis

A key assumption behind option analysis is that the economic agent, i.e. the project manager, acts rationally. This is most definitely true in the case of financial options where the agent exercises the option if it is in the money and otherwise does not. However, with real options it could be considered doubtful whether the agent acts rationally at all times. An example could be the selection between two projects that have the same expected return but different volatilities.

To optimize the profits of the company the manager should choose the high risk project as in theory it will have a higher NPV. But the manager could be biased to choose the low risk project as it will have a higher likelihood of success and thus secure his own job. Or choose a project that requires his particular skills over a project where another project manager would be needed even though that project would be of higher value to the company. Such irrational entrenchment behavior is a classic principal-agent problem where the main hypothesis is that the manager makes the investment that makes him valuable (Shleifer & Vishny, 1989, p.125). Also, on an ongoing project assessment level the question of the rational agent can be raised. A project manager has often worked several years on a project and is therefore very attached to it which could imply a reluctance to let go of the project if for instance negative market information occurs.

As discussed in our analysis a part of the value that real option analysis brings to the valuation is the assessment of which future scenarios and possibilities that may arise. When outlining the project the management gains important strategic insights in how to act under different

circumstances which is highly valuable with an ever-changing project. It could be interesting if companies operating in uncertain environments focused on creating an organisation that is prepared for change and strives at catching the opportunities that present themselves in the constantly changing surroundings. Being best at capturing and taking advantage of such possibilities could be a competitive edge that would be hard to imitate because – to ensure its success - it would be embedded throughout the organisation.

The application of real option analysis implies that companies in general will take on riskier projects as they have a higher expected value. As companies have to maximize profit on behalf of the shareholders they should not worry about diversifying and reducing the unsystematic risk

strategy for the companies does not imply that it is also the optimal strategy for the society.

Higher risk will lead to more failures and consequently defaults thereby causing uncertainty for stakeholders such as the employees. It would result in a more insecure job market which puts a bigger burden on society as they have to pay for the consequences. Especially in the wake of the financial crisis where excessive risk-taking was a major source to the turmoil would it be

unfortunate to signal higher risk aversion to the public.

Further Research

It would be interesting to present the fuzzy valuation approach to the companies in our survey (and others too) to find out how many of the practitioners would be interested in applying this method and how many would be capable of actually applying it to their valuations. This knowledge would be very helpful in determining the necessary requirements such as software programs or education that would facilitate a wider implementation.

Applying the method from an investor’s point of view would be interesting to determine how well it works in putting a price on a company’s different projects and thereby the company as a whole compared to the standard methods applied. This could be achieved by valuing a number of companies by the different valuation methods and 12 months later determine which method had been most accurate when comparing the forecasted stock prices with the actual stock prices.

For advanced users of fuzzy valuations it could be interesting to consider the application of fuzzy numbers on other elements of the valuation. Applying fuzzy numbers to input variables in the cash flow forecast could be valuable as many of these cannot be considered crisp numbers which they are defined as in standard valuations. An example of such an input variable is the success rates for each phase used in the risk-adjusted DCF valuation which could be portrayed more accurate with a fuzzy number. Thus the entire valuation could be fuzzified to represent a pure possibilistic approach and possibly an improved result. But it would also complicate the valuation process further and as such is not in the scope of this thesis due to the goal of

applicability. Also the possible mathematical constraints for substituting probabilistic numbers with possibilistic numbers could be interesting to investigate.

9 Literature list