Detecting similar news tickers in the area of natural gas trading: improving decision support in uncertain situations

The volatility of the natural gas market founded a need for the ability to analyze upcoming events in real time in order to manage profits and risks for participants. News ticker provide information being of utmost importance for the analysis. The research presented among this paper describes features of a software prototype supporting the analytical price prognosis tasks for gas traders. By knowing market development at the time of a certain past situation, the outcome of that situation can be used to predict the future market development of a current analyzed situation with similar content. For that, similar situations have to be detected in order to reduce uncertainty about future. Fitting into design science, we use task-technology-fit theory and technology-acceptance-model to identify information needs and to evaluate the artifact. This novel approach serves as a further step to gain a decision support with integrated structured and unstructured data.

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