TY - GEN
T1 - The study of a financial crisis prediction model based on XBRL
AU - Lin, Fengyi
AU - Liang, Deron
AU - Chin, Shih Jung
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
KW - Corporate governance
KW - Financial prediction
KW - Non-financial features
KW - Predictive process
KW - Support vector machines
UR - https://www.scopus.com/pages/publications/57749198060
U2 - 10.1109/SNPD.2008.52
DO - 10.1109/SNPD.2008.52
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AN - SCOPUS:57749198060
SN - 9780769532639
T3 - Proc. 9th ACIS Int. Conf. Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2008 and 2nd Int. Workshop on Advanced Internet Technology and Applications
SP - 147
EP - 153
BT - Proc. 9th ACIS Int. Conf. Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2008 and 2nd Int. Workshop on Advanced Internet Technology and Applications
T2 - 9th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2008 in conjunction with 2nd International Workshop on Advanced Internet Technology and Applications, AITA 2008
Y2 - 6 August 2008 through 8 August 2008
ER -