The financial stability and access to financing of SMEs in Hungary

The present study was prepared within the framework of cooperation between the Competitiveness Research Centre, operating within the Institute of Business Economics of Corvinus University of Budapest, and the National Association of Entrepreneurs, based on a commission from the latter. Th e goal of the study was to survey the self-financing capabilities and borrowing opportunities of majority Hungarian-owned small and medium-sized enterprises (SMEs), and to identify potential problems. The results of the research revealed that the high proportion of owner’s equity in the financing structure is not due to difficulties with borrowing, but because enterprises that cover their fi nancing primarily from their own resources have other financing opportunities at their disposal. Although general satisfaction with banks shows a diminishing tendency, it can still be interpreted favourably. The majority of companies have not encountered serious borrowing difficulties. With regard to the system of competitive tenders, company managers have sensed some improvement, but general satisfaction is still lacking. Although the research results suggest that the primary obstacle to growth in 2013 was not the lack of credit or external funding, it is important to emphasize that start-ups, young enterprises and micro-enterprises, which struggle the most with financing worries, were not represented in the analysed database.

Energy Efficiency Analysis of Water and Wastewater Utilities Based on the IBNET Database

A víz- és szennyvíz-szolgáltató vállalatok működési költségeinek jelentős hányadát teszi ki a villamosenergia-költség. Elemzésünk az IBNET (International Benchmarking Network for Water and Sanitation Utilities) adatbázisa alapján vizsgálja a közép-kelet-európai és a FÁK országokban működő víziközművek energiahatékonyságát. Többváltozós statisztikai elemzés segítségével tárjuk fel a különböző működési jellemzők energiahatékonyságot befolyásoló hatását. A Világbank által kezdeményezett IBNET programról bővebb információ a www.ib-net.org oldalon található, angol nyelven.

Real effective exchange rates for 178 countries: A new database

We use data on exchange rates and consumer price indices and the weighting matrix derived by Bayoumi, Lee and Jaewoo (2006) to calculate consumer price index-based REER. The main novelties of our database are that (1) it includes data for 178 countries –many more than in any other publicly available database– plus an external REER for the euro area, using a consistent methodology; (2) it includes up-to-date REER values, such as data for January 2012; and (3) it is relatively easy to calculate REER against any arbitrary group of countries. The annual database is complete for 172 countries and the euro area for 1992-2011 and data is available for six other countries for a shorter period. For several countries annual data is available for earlier years as well, eg data is available for 67 countries from 1960. The monthly database is complete for 138 countries for January 1995-January 2012, and data is also available for 15 other countries for a shorter period. The indicators calculated by us are freely downloadable and will be irregularly updated.

Application of support vector machines on the basis of the first Hungarian bankruptcy model

In our study we rely on a data mining procedure known as support vector machine (SVM) on the database of the first Hungarian bankruptcy model. The models constructed are then contrasted with the results of earlier bankruptcy models with the use of classification accuracy and the area under the ROC curve. In using the SVM technique, in addition to conventional kernel functions, we also examine the possibilities of applying the ANOVA kernel function and take a detailed look at data preparation tasks recommended in using the SVM method (handling of outliers). The results of the models assembled suggest that a significant improvement of classification accuracy can be achieved on the database of the first Hungarian bankruptcy model when using the SVM method as opposed to neural networks.