3 research outputs found

    Does Non-linearity Matter in Retail Credit Risk Modeling?

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    In this research we propose a new method for retail credit risk modeling. In order to capture possible non-linear relationships between credit risk and explanatory variables, we use a learning vector quantization (LVQ) neural network. The model was estimated on a dataset from Slovenian banking sector. The proposed model outperformed the benchmarking (LOGIT) models, which represent the standard approach in banks. The results also demonstrate that the LVQ model is better able to handle the properties of categorical variables.retail banking, credit risk, logistic regression, learning vector quantization

    The power of market mood -- Evidence from an emerging market

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    This article focuses on investor behavior and, consequently, the mood in the market. By using a self-organizing network we develop a model which tries to capture the market mood and serves as an indicator of the reasonableness of selling or purchasing securities. In this sense, the final result of this model is the same as in the model-type prediction of future stock prices, with the only exception being that one is not required to know the concrete future values of the selected security. This will indirectly support the hypothesis that psychological factors are an important (if not key) market driving force.Behavioral patterns Stock market Self-organizing map Investment decision
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