In many economic applications it is desirable to make future predictions about the financial status of a company. The focus of predictions is mainly if a company will default or not. A support vector machine (SVM) is one learning method which uses historical data to establish a classification rule called a score or an SVM. Companies with scores above zero belong to one group and the rest to another group.Estimation of the probability of default (PD) values can be calculated from the scores provided by an SVM. The transformation used in this paper is a combination of weighting ranks and of smoothing the results using the PAV algorithm. The conversion is then monotone.This discussion paper is based on the Creditreform database from 1997 to 2002. The indicator variables were converted to financial ratios; it transpired out that eight of the 25 were useful for the training of the SVM. The results showed that those ratios belong to activity, profitability, liquidity and leverage.Finally, we conclude that SVMs are capable of extracting the necessary information from financial balance sheets and then to predict the future solvency or insolvent of a company. Banks in particular will benefit from these results by allowing them to be more aware of their risk when lending money. * The financial support from the Deutsche Forschungsgemeinschaft via SFB 649 'Ökonomisches Risiko", Humboldt-Universität zu Berlin is gratefully acknowledged. †

This study analyses credit default risk for firms in the Asian and Pacific region by applying two methodologies: a Support Vector Machine (SVM) and a logistic regression (Logit). Among different financial ratios suggested as predictors of default, leverage ratios and the company size display a higher discriminating power compared to others. An analysis of the dependencies between PD and financial ratios is provided along with a comparison with Europe (Germany). With respect to forecasting accuracy the SVM has a lower model risk than the Logit on average and displays a more robust performance. This result holds true across different years.

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Spatial mobility is an important means of tackling regional disparities and matching problems in education and labor markets, but it is also a source of individual social inequality as it is associated with higher socio-economic resources and returns; however, there is a paucity of research on the prevalence and predictors of spatial mobility among youth entering vocational education and training (VET). We examine the importance of (a) individual occupational orientations, (b) regional opportunity structures, and (c) social ties for the spatial mobility of youth in this early transition phase using longitudinal data from the German NEPS, which we combined with administrative geospatial data of German districts (NUTS-3). Our results show widespread spatial mobility among students entering the VET system: 16% are mobile within and 22% between regional labor markets. Multinomial logistic regression models show that, in addition to young people’s occupational orientations (status aspirations; search duration) and social ties to friends, regional opportunity structures (general unattractiveness; person-environment mismatch) are crucial for youths’ spatial mobility. This underscores the importance of spatial mobility given regional disparities to promote youths’ access to VET and reduce regional mismatches in the VET market.