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• Network Analysis for Market Surveillance

  19-33

  Gergely Szots

  Gabor Nagy

  Views:

  166

  Our analysis has focused on the network structure of the credit default swap (CDS) market because relatively few publications have appeared on this segment of the financial market. The article puts emphasis on a proposed new supervisory tool which uses network science in market surveillance of the Hungarian financial market. Our research results are compared to those of a previously published ESMA analysis, where the writers applied network science to analyze financial market contagion risks. As a result, the article concludes that the Hungarian sovereign CDS market network structure is similar to the European one in the sense that it is highly concentrated.

  Journal of Economic Literature (JEL) codes: G14, C45

• Regional netwrok cooperation

  115-130

  Marietta Kiss

  Views:

  123

  The current study aims to reveal the regional network cooperations - found primarily in the construction industry -, in particular in the Észak-Alföld Region. The study includes three main parts: after the industry analysis of the construction industry a short summary follows about the theoretical bases of today's business network cooperations, clustering, and such relationships especially among firms operating in the construction industry, and finally it is closed by a case study revealing the relationship network of a dominant construction company of the Észak-Alföld Region. The most important finding is that in Hungary clustering in the construction industry - that has already existed in several developed economies - has not started yet, however, networking - that can be the basis for the development of a construction industry cluster - has already began, and if it continues, it further increased the advantages already experienced.

  Journal of Economic Literature (JEL) classification: L140, L850

• Data mining through a business window (Part II.)

  108-130

  Attila Harmati

  Views:

  154

  This article demonstrates the real world applications of the technology of data mining by way of a data mining project. This project was created by the author and the analyzed database was provided by a real company. The aim of the analysis was to create a classification model for this firm. To achieve this we applied logistic regression models, a decision tree and a neural network. The best model can help the company to consciously establish which customers will probably respond positively to a personal letter in a direct marketing campaign. In this way potentially favourable customers are reached more efficiently than in the case of randomized selection. This increases the efficiency of the company, and the generalization of results can confirm several advantages of data mining as used in business life.

  JEL classification: C25, C44, C45, C49, C88