Investment opportunities are currently valued via metrics and algorithms formed by the economical theory. The majority of investors still values projects with the net present value (NPV) method, which takes into account the time value of money and gives solid results for simple projects with minimal requirements on mathematical skills. More complicated projects, which are in this thesis thought of as projects with a substantial degree of inner uncertainty and with an existence of further managerial decisions, can be valued by the real options analysis (ROA). This method comes from an imperfect analogy to financial option valuation and it recognizes the value of the ability to change the course of a given project.
My thesis presents a new valuation framework for projects, which are understood as stochastic decision problems. This framework incorporates the NPV and ROA methods, relaxes their assumptions and allows for decades of research in the field of stochastic decision theory (SDT) to be used. The main contributions of the new framework are: ability to incorporate multiple sources of uncertainty, usage of any distribution for uncertainty modeling, ability to conveniently incorporate Bayesian learning, ability to model user's approach to risk and ability to model any type and number of managerial actions.
The new framework significantly expands the class of projects that can be reasonably valued and can be understood as a unification of project valuation in business management.
