NDLI: The Study of a Financial Crisis Prediction Model based on XBRL

Content Provider	IEEE Xplore Digital Library
Author	Fengyi Lin Deron Liang Shih-Jung Chiu
Copyright Year	2008
Description	Author affiliation: Nat. Taipei Univ. of Technol., Taipei (Fengyi Lin)
Abstract	Recent outbreak of corporate financial crises worldwide has brought attention to the need for a new international financial architecture which rests on crisis prediction and crisis management. Financial data have been widely used by researchers to predict financial crisis, but few studies exploit the use of non-financial indicators in corporate governance to construct financial crisis prediction model. This article introduces a prediction model based on a relatively new machine learning technique, support vector machines (SVM) with XBRL financial reporting. This study indicates that the prediction model considering both financial and non-financial information outperforms those models based on only one type of information. Two well-known prediction models, regression model and genetic algorithm, are compared with SVM. The experiment results show that the combined use of both financial and non-financial features with SVM model leads to a more accurate prediction of financial distress.
Starting Page	147
Ending Page	153
File Size	301768
Page Count	7
File Format	PDF
ISBN	9780769532639
DOI	10.1109/SNPD.2008.52
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-08-06
Publisher Place	Thailand
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Accuracy Biological system modeling Corporate governance Financial prediction Companies Predictive process Predictive models Non-financial features Genetic algorithms Business
Content Type	Text
Resource Type	Article

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