Novel feature selection methods to financial distress prediction

Lin, Fengyi; Liang, Deron; Yeh, Ching-Chiang; Huang, Jui‐Chieh

doi:10.1016/j.eswa.2013.09.047

Cited by 106 publications

(60 citation statements)

References 38 publications

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“…However, many studies [14,16,17,35] employed growth ratios to predict business failure in other companies. Therefore, in future study, we would include growth ratios as a financial indicator to improve the prediction performance.…”

Section: Resultsmentioning

confidence: 99%

Comparison of Single Classifier Models for Predicting Long-term Business Failure of Construction Companies Using Finance-based Definition of the Failure

Choi¹,

Son²,

Kim³

et al. 2017

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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This study compares the performance of six classifier models (ANN, KNN, C4.5, SVM, LR, and NB) for predicting the business failure of construction companies after three years from 2010 to 2012. Although previous studies have explored numerous business failure prediction models for construction companies, these models have focused on short-term failure, defined as failure occurring within one year, and have defined business failure based on companies' experiencing serious legal events, including bankruptcy, delisting, and default. However, the construction industry is typically characterized by projects with longer durations, usually exceeding one year. This implies that previous short-term models cannot predict the business failure of construction companies until the end of particular projects. Moreover, this problem is compounded by the fact that legal events can involve lengthy proceedings, which are often initiated much later than the actual moment of the business failure. Therefore, in this study, six classifier models will be used to predict the business failure of the construction companies within three years using a finance-based definition of failure. The results show that all six models' performances noticeably decrease when they predict more than one year. These results demonstrate that previous short-term prediction models with outstanding performance cannot be practical in predicting the long-term business failure of construction companies.

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Section: Resultsmentioning

confidence: 99%

Comparison of Single Classifier Models for Predicting Long-term Business Failure of Construction Companies Using Finance-based Definition of the Failure

Choi¹,

Son²,

Kim³

et al. 2017

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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show abstract

“…Ennek okait a számviteli adatok hátrányai közt találhatjuk meg. Ezek közül talán az egyik legfontosabb, hogy a számviteli kimutatásokban található információk elsősorban a múltban meghozott vezetői döntések hatásait tükrözik, így a jövőre vonatkozó előrejelző képességük kétségtelenül korlátozott (Lin et al, 2014). A számviteli információk további hát-ránya, hogy nem minden esetben nyújtanak teljes képet a vállalatok pénzügyi helyzetéről (például mérlegen kívüli tételek), ennek oka lehet az is, ha a vállalat vezetése ma-…”

Section: Cikkek Tanulmányokunclassified

Stock és flow típusú számviteli adatok alkalmazása a csődelőrejelző modellekben

Nyitrai¹

2017

Veztud

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“…In many practical applications, the dimensions of data sets (the number of attributes) are becoming higher and higher [1,8,24,31]. For these high-dimensional data, attributes irrelevant to recognition tasks may deteriorate the performance of learning algorithms, and result in the high computing cost [11,33].…”

Section: Introductionmentioning

confidence: 99%