By Klaus Abberger, Yuanhua Feng (auth.), Prof. Dr. Georg Bol, Prof. Dr. Gholamreza Nakhaeizadeh, Prof. Dr. Karl-Heinz Vollmer (eds.)
This ebook contains the articles of the sixth Econometric Workshop in Karlsruhe, Germany. within the first half techniques from conventional econometrics and leading edge tools from computer studying akin to neural nets are utilized to monetary matters. Neural Networks are effectively utilized to varied components akin to debtor research, forecasting and company finance. within the moment half a variety of features from Value-at-Risk are mentioned. The lawsuits describe the criminal framework, assessment the fundamentals and speak about new methods akin to shortfall measures and credits risk.
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Extra info for Risk Measurement, Econometrics and Neural Networks: Selected Articles of the 6th Econometric-Workshop in Karlsruhe, Germany
In contrast to OLTP-systems decision support systems (DSS) are usually used by only a small number of data-analysts or executives and far less frequent than OLTP-systems. e. have to consider a very large amount of data including historical information (many-records-at-a-time) spread over a multitude of data sources. Consequently, the total volume of data rises into magnitudes of giga- up to terabytes of data. In addition, these 'transactions' are not pre-defined but rather generated highly dynamic and flexible.
The error-area-value of the BPN FA21-10 is decreased by seven percent compared to the benchmark. 385% on the validation set, slightly poorer than the FA21-10. 33 The VC-information consists of credit-standing relevant features which so far have not been processed by the VC-index. The computer-aided derived features as well as the manually encoded features improve the classifiers performance, if optimized according to its error-area. 2 Business information vs. financial statement The BPNs, which are developed in the separate analysis, process the information either from the VC-information or from the financial statement.
In addition, these 'transactions' are not pre-defined but rather generated highly dynamic and flexible. Figure 1 summarizes the differences between transactions for supporting the day-to-day operations of an enterprise (OLTP) and decision support. Decision Support Characteristics OLTP high low Number of User/Frequency complex Complexity of Transactions simple stactic, predifined dynamic, flexible Kind of Transactions few many Number of Sources Gigabyte Gigabyte-Terabyte Size of Sources Actuality current-valued current-valued and historical Fig.
Risk Measurement, Econometrics and Neural Networks: Selected Articles of the 6th Econometric-Workshop in Karlsruhe, Germany by Klaus Abberger, Yuanhua Feng (auth.), Prof. Dr. Georg Bol, Prof. Dr. Gholamreza Nakhaeizadeh, Prof. Dr. Karl-Heinz Vollmer (eds.)