Credit risk evaluation modeling using evolutionary linear SVM classifiers and sliding window approach

AbstractThis paper presents a study on credit risk evaluation modeling using linear Support Vector Machines (SVM) classifiers, combined with evolutionary parameter selection using Genetic Algorithms and Particle Swarm Optimization, and sliding window approach. Discriminant analysis was applied for evaluation of financial instances and dynamic formation of bankruptcy classes. The possibilities of feature selection application were also researched by applying correlation-based feature subset evaluator. The research demonstrates a possibility to develop and apply an intelligent classifier based on original discriminant analysis method evaluation and shows that it might perform bankruptcy identification better than original model

XBRL implementation in the European union: Exploring preparers\u2019 points of view

The wide diffusion of XBRL will reach another milestone with the mandatory transition to iXBRL in the European Union. This process involves all listed companies, as ESMA requires iXBRL for companies issuing IFRS consolidated financial statements from 1st January 2020. This paper explores the points of view of preparers, key subjects in identifying the potential drawbacks of XBRL adoption since they are directly involved in the transition. We analyse letters responding to the ESMA 2015 consultation paper and interpret them in the light of previous literature on the topic, also considering the positions expressed by other respondents\u2019 categories. This study contributes to XBRL literature by shedding light on preparers\u2019 positions before the mandatory transition. Additionally, it stresses the gap existing between subjects bearing implementation costs of a communication technology and its end users. We provide insights useful to European policy-makers to improve the process of transition to XBRL. Indeed, findings remark the trade-off between the need for innovation and the necessity to reduce administrative burdens to enhance the competitiveness of European companies. Our analysis could be of interest also for policy-makers of other jurisdictions considering XBRL adoption as well as for companies and consulting firms supporting them in the transition