In this paper we propose a two-stage methodology to classify the non-banking financial institutions (NFIs) based on their financial performance. The first stage of the methodology consists of grouping the companies in similar financial performance classes (e.g.: good, average, poor performance classes). We optimise the allocation of the observations within the performance clusters by applying an enhanced version of an observation re-allocation procedure proposed in our previous work. Next, based on the result of the grouping phase, we construct a performance class variable by attaching a performance label to each data row. Then, in the second phase of our methodology, we propose a feed-forward neural-network classification model that maps the input space to the newly-constructed performance class variable. This model allows us to forecast the performance of new companies as data become available.

An integrated two-stage methodology for optimising the accuracy of performance classification models / Ferrara, Massimiliano; Costea, A; Serban, F. - In: TECHNOLOGICAL AND ECONOMIC DEVELOPMENT OF ECONOMY. - ISSN 2029-4913. - 23:1(2017), pp. 111-139. [:10.3846/20294913.2016.1213196]

An integrated two-stage methodology for optimising the accuracy of performance classification models

FERRARA, Massimiliano
Supervision
;
2017-01-01

Abstract

In this paper we propose a two-stage methodology to classify the non-banking financial institutions (NFIs) based on their financial performance. The first stage of the methodology consists of grouping the companies in similar financial performance classes (e.g.: good, average, poor performance classes). We optimise the allocation of the observations within the performance clusters by applying an enhanced version of an observation re-allocation procedure proposed in our previous work. Next, based on the result of the grouping phase, we construct a performance class variable by attaching a performance label to each data row. Then, in the second phase of our methodology, we propose a feed-forward neural-network classification model that maps the input space to the newly-constructed performance class variable. This model allows us to forecast the performance of new companies as data become available.
2017
knowledge-based systems, uncertainty modelling, applications of fuzzy sets, classification, artificial intelligence, performance evaluation, non-banking financial institutions
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12318/5729
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