Improving the Support for Investment Decisions in Financial Markets Using the System Dynamics Methodology

1 Problem Setting Financial markets (FiMa) are often designated as " complex systems " (Johnson et al. 2003, p. 3; Farmer and Foley 2009, p. 685). From the perspective of system theory, this view implies two aspects: Firstly, FiMa can be interpreted as systems having structure and behavior. Secondly, such systems have high structural and behavior-al complexity (e.g. Klir 1969, pp. 50). Investors, as parts of FiMa, seek to make reasonable investment decisions. For this purpose, it is essential to comprehend the system structure, the system behavior and interdependence between both. These necessities correspond with three questions investors are concerned with in the run-up to investment decisions: What elements constitute the financial system? (question1). Why do financial markets behave in a certain way? (question2). How will security prices develop in the future? (question3) (Tab. 1, left column). Because of the high structural and behavioral complexity of FiMa, answering these questions is difficult. In order to manage this complexity, models of FiMa are constructed. A model is a mapping of the original system, which reduces the number of the original system's attributes and exhibits a subject oriented pragmatic aspect (Stachowiak 1973, p. 131). The model quality is determined by its accuracy of structure, its accuracy of behavior, its empirical validity, and its validity for application (Bossel 1994, p. 36). The questions 1 to 3 can be answered using three generic types of models of which each aims at different model objectives (Wildmann 2007, pp. 5): Description models capture the structure of the system (answer to question1), explanation models seek to