The expansion of advanced modeling tools, such as neural, evolutionary, fuzzy and hybrid systems, has led to a systematic attempt for their applicability in the challenging stock market field. Today, the ensuing results are admittedly far better than those accomplished by models based on linear or typical nonlinear mathematical approximators; yet, the related trading risk remains at significantly high levels. In quest of innovative approaches, one interesting research direction appears to be the complete analysis and exploitation of various interrelated quantitative and mostly qualitative agents affecting stock market behavior. Based on this criterion, fuzzy cognitive maps (FCMs) constitute a powerful modeling tool for the development of a stock market forecasting system as they are structured as networks of cause-effect relationships between diverse factors. The subject of this study is aligned with the aforementioned remark; firstly, the recognition of crucial stock market, business and economic agents is attempted, secondly an FCM-based stock market model is designed, and ultimately the feasibility and effectiveness of the real world application is evaluated.