Prospect Theory in the Automated Advisory Process

Abstract: With robo-advisors and regulation eventually changing the market conditions of thefinancial advisory industry, traditional advisors will have to adapt to a new world of asset management. Thus, it will be of interest to traditional advisors to further explore the topic of how to automatically evaluate soft aspects such as client preferences and behavior, and transform it into portfolio allocations while retaining stringency and high quality in the process. In this thesis, we show how client preferences and behavioral aspects can be translated into quantitative parameters, suitable for an asset allocation model based on prospect theory. A risk profiler, a type of questionnaire, is found to be an appropriate tool to use in this process. Further, we show that the impact of the parameters on the resulting portfolio allocations is consistent with prospect theory and the preferences of the investor. Finally, we conclude that the optimized portfolio allocation generated by the model suit the investor's preferences.