There are several scientific studies relating to Information Systems for market surveil-lance, including the research of Mangkorntong and Rabhi (2007) in which the architec-ture for automated trading patterns based on electronically available market data is in-troduced. In assessing the performance of the financial market surveillance system, the authors note that the tested system’s main limitation is the lack of performance due to a missing API required to connect to other systems. More recent research (Diaz, Zaki, Theodoulidis, & Sampaio, 2011) presents an architecture for market monitoring where data mining techniques are utilized to detect abusive patterns in spam emails. In another study, Heping (2006) introduced a Multilevel Stochastic Dynamic Process (MSDP) framework for modelling time series for financial market analysis and surveillance, thereby focusing attention on signaling e.g., market crashes or trend accelerations. Other research (Huang, Liang, & Nguyen, 2009) tackles a visualization approach for fraud detection problems in financial markets. The system is based on pattern recognition by which an unusual pattern is matched to the similar pattern in the database. The financial
3 Research Background 16
market systems for detecting abuse, such as the Securities Observation, News Analysis and Regulation Systems (SONAR) ( Goldberg, Kirkland, Lee, Shyr, & Thakker, 2003), aim to monitor the stock market. The system applies data mining, text mining, statistical regression and rule-based detection to recognize both abuse patterns in the structured data and unusual trading following publication of the news.
In summary, to detect the various types of market manipulation, a surveillance system needs to handle traditional data (e.g., time series) as well as non-traditional data (e.g., news, blogs and twitter platforms).
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4 Study Setup
To contribute to the knowledge base and answer the research questions, this dissertation thesis followed the DSR approach as proposed by (Hevner et al., 2004) and (Vaishnavi
& Kuechler, 2008) . The detailed specific methods addressed in different papers are presented in Section B.
Design science research encompasses the development and evaluation of constructs, frameworks, models, methods, instantiations or theories by which identified business needs or real problems can be addressed (Hevner et al., 2004). The set of artifacts pre-sented in this thesis includes an analytical framework for literature review, a model that describes the relationships between components, an innovative instantiation of IT arti-facts that solves a practical problem and a deductively developed theory that enhances the knowledge base. Together, they represent a new approach to understanding abusive behavior in the financial market, thus enabling regulatory authorities to counteract crim-inal activities more effectively. Thus, is this thesis a DSR framework to guide the de-velopment of the mentioned artifacts as proposed by (Hevner et al., 2004) is incorpo-rated. Here the DRS cycles of building and evaluation closely interact with the knowledge base and the environment ensuring rigor and relevance of scientific work.
Additionally, in this thesis, the generalized process cycles as proposed by (Vaishnavi &
Kuechler, 2008) are used. The authors suggest a series of iterative rounds to conduct design-oriented research projects. The recommended cycles are as follows:
I. The initial “Awareness of the problem” aims to identify theoretical knowledge and practical user needs regarding the specific problem.
II. The “Suggestion” aims to situate the requirements and components.
III. The “Development” aims to develop artifacts.
IV. The “Evaluation” explores the functionalities and performance.
V. The “Conclusion” reflects on functionalities and performance.
Hence, the approach offers a composed general model that builds on the approaches of (Vaishnavi & Kuechler, 2008) and (Hevner et al., 2004) as presented in Figure 3.
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Figure 3: Adapted DSR Framework based on (Vaishnavi & Kuechler, 2008) and (Hevner et al., 2004)
Awareness of the Problem (Figure 4): The objective of the first cycle is to gain knowledge and to understand the problem. The result of this first cycle is paper 1.
Figure 4: Awareness of the problem cycle
This paper 1 presents a structured analysis of the existing research in the field of finan-cial decision support systems (DSS). Based on the literature reviewed, the paper pre-sents an overview of existing literature, deriving areas for future research. The literature review is structured using a model combining design theory, decision support system
Study Setup 19
and information mining components; future research can be structured likewise. The analysis from the current studies suggests three key classes: financial analysis, fraud detection and risk management. Hence, it provides an overview of the existing relevant literature and derives problem definition and problem diagnosis by providing sugges-tions for future research.
Suggestion, Development and Evaluation (Figure 5): The second cycle builds on the results of the first cycle. Further iterative steps comprise interviews with domain ex-perts. The knowledge gained in these interviews was mapped into design requirements for a qualitative multi-attribute model. To enhance the relevance of the model, several meetings with domain experts were conducted. Furthermore, this stage is also supported by the evaluation cycle. The final model experienced diverse small refinements until its completion and evaluation in paper 2. The developmental phase is accompanied by the evaluation cycle. In this phase an artifact is created to address the challenging problem of detecting fraudulent behavior in financial markets (paper 3). This paper examines a detection strategy of pump and dump manipulation to thwart fraudsters from unlawful profit techniques. Hence, an IT artifact instantiation in the form of a model-based deci-sion support system that supports decideci-sion making in the field of financial market sur-veillance is presented. This artifact utilizes a qualitative decision model to identify sit-uations in which prices of single stocks are affected by fraudsters who aggressively ad-vertise the stock. An evaluation of the implemented system based on voluminous and heterogeneous data including user-generated content data is provided.
Finally, to ensure the rigor of the results, in several iterations, the designed artifacts and evaluation are abstracted to conceive an explanatory design theory (paper 4). The ob-jective of this paper is to provide design suggestions that enable effective development of Financial Market Surveillance Decision Support Systems (FMS-DSS) for financial institutions. The study is guided by research questions aimed at determining general components and general requirements for financial market surveillance systems with the ability to detect a variety of market manipulations originating from social media usage.
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Figure 5: Cycles of Suggestion, Development and Evaluation
Conclusion: Lastly, the research project concludes with lessons learned from the pro-ject as presented in Section C.
SECTION B: Studies 21
SECTION B: Studies
SECTION B: Studies 22