Not logged in.
Quick Search - Contribution
Contribution Details
| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Multi-Period Behavioral Portfolio Optimization |
| Organization Unit | |
| Authors |
|
| Supervisors |
|
| Language |
|
| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Date | 2019 |
| Abstract Text | In this thesis, a new dynamic, multi-period portfolio optimization framework is proposed. Specifically, this framework is tailor-made for a hypothetical investor with respect to four key factors: a) the time horizon of the investment, b) the risk profile of the investor, c) the final and intermediate wealth goals of the investor, and d) the cashflows from the investor. In con- trast with classical single-block approaches to portfolio optimization, we propose a three-step solution that allows an investor to observe and take active part in the decision-making process. Taking the investor’s specifications into account, the proposed optimization procedure finds the optimal dynamic portfolio strategy that maximizes the probability of achieving all wealth goals cumulatively. We introduce various means of visualization of both the intermediate steps and final outputs, with the aim of better understanding the optimization process. The core of the framework is the dynamic programming algorithm proposed by Das et al. (2018a). By reviewing the theory of dynamic programming, we extend the algorithm by introducing new features in the model, such as intermediate wealth goals. |
| Export |
BibTeX
|