Supporting Financial Market Surveillance: An IT Artifact Evaluation

In this paper, an IT artifact instantiation (i.e. software prototype) to support decision making in the field of financial market surveillance, is presented and evaluated. This artifact utilizes a qualitative multi-attribute model to identify situations in which prices of single stocks are affected by fraudsters who aggressively advertise the stock. A quantitative evaluation of the instantiated IT artifact, based on voluminous and heterogeneous data including data from social media, is provided. The empirical results indicate that the developed IT artifact instantiation can provide support for identifying such malicious situations. Given this evidence, it can be shown that the developed solution is able to utilize massive and heterogeneous data, including user-generated content from financial blogs and news platforms, to provide practical decision support in the field of market surveillance

State of the Art of Financial Decision Support Systems based on Problem, Requirement, Component and Evaluation Categories

Financial decision support has become an important information systems research topic and is also of highest interest to practitioners. Two rapidly emerging trends, the increasing amount of available data and the evolution of data mining methods, pose challenges for researchers. Thus, a review of existing research with the goal to guide future research efforts in this domain is timely. To structure our literature review and future research in this area, we propose a framework in the paper that integrates elements of decision support systems, design theory, and information mining. The framework is then applied in the paper. Our analysis reveals that the focus of existing research can be grouped into three major domain categories. More research is needed in two of the categories for which we found only very few IS studies, despite the high relevance of these topics due to increased turbulences in worldwide financial markets. Furthermore, we discuss the opportunities to make stronger use of heterogeneous data and of combined data mining techniques and to build upon the rich set of available evaluation methods

Hot Stock or Not? A Qualitative Multi-Attribute Model to Detect Financial Market Manipulation

The emergence of online financial information channels, such as web portals and financial blogs, eases the challenge process for scammers of publishing fraudulent contents in order to manipulate share prices. To maintain market integrity, financial market surveillance authorities monitor these different information channels to detect suspicious behaviour. However, as the available amount of online information increases, analyses become more costly and time-consuming. In order to support related decisions, we have developed a model to identify fraudulent situations. Based on interviews with domain experts, we first identified the factors determining suspicious situations and then applied a qualitative multi-attribute modelling technique. Thereby, our resulting model builds upon valuable knowledge of domain experts and provides means to address the challenge of information based market manipulation