General Model for Automated Diagnosis of Business Performance
AbstractIn this paper, we describe an extension of the methodology for explanation generation in financial knowledge-based systems, offering the possibility to automatically generate explanations and diagnostics to support business decision tasks. The central goal is the identification of specific knowledge structures and reasoning methods required to construct computerized explanations from financial data and business models. A multi-step look-ahead algorithm is proposed that deals with so-called calling-out effects, which are a common phenomenon in financial data sets. The extended methodology was tested on a case-study conducted for Statistics Netherlands involving the comparison of financial figures of firms in the Dutch retail branch. The analyses are performed with a diagnostic software application which implements our theory of explanation. Comparison of results of the classic explanation methodology with the results of the extended methodology shows significant improvements in the analyses when cancelling-out effects are present in the data.
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Bibliographic InfoPaper provided by Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam. in its series Research Paper with number ERS-2005-058-LIS.
Date of creation: 14 Oct 2005
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finance; decision support systems; artificial intelligence; explanation; production statistics;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2005-10-29 (All new papers)
- NEP-BEC-2005-10-29 (Business Economics)
- NEP-CMP-2005-10-29 (Computational Economics)
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