Sequential Binary Investment Decisions : A Bayesian Approach

This book deals with analysis of sequential decision models in investment and portfolio theory. The optimal investment strategy is derived by using results from stochastic dynamic programming and from Bayesian statistics. The analysis is largely restricted to models with only two alternatives in order that powerful results may be obtained. In the first part a dynamic portfolio model consisting of two assets is considered. In the second part a stopping decision model is used to determine the optimal investment date of a long-lived real project. Results from discrete-time dynamic programming and from Bayesian statistics are used to derive structural properties of the optimal investment strategy, such as monotonicity results. Thereby the optimal investment strategy allows plausible economic interpretations and leads to many interesting sensitivity results