A framework for business failure prediction

Irem Islek; Idris Murat Atakli; Sule Gunduz Oguducu

doi:10.1007/978-3-319-59060-8_8

A framework for business failure prediction

Irem Islek^*, Idris Murat Atakli, Sule Gunduz Oguducu

^*Corresponding author for this work

Department of Artificial Intelligence and Data Engineering

Idea Teknoloji Çözümleri

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

Business failure prediction systems help predict financial failures before they actually happen and provide an early warning for enterprises. Using machine learning techniques, instead of traditional statistical models, has brought a considerable increase in performance into the area of business failure prediction. This paper presents a frame-work for predicting business failures by using different machine learning techniques. We, also, implemented a novel model for business failure prediction based on NARX (nonlinear autoregressive network with exogenous inputs) feedback neural network to be included into this framework which is a recurrent dynamic network with feedback connections. Detailed experiments are conducted to compare the performance of these approaches. Especially, for the long-term business failure predictions, there are no other papers investigating the performance of NARX. To the best of our knowledge, this is the first time NARX algorithm is applied for long-term business failure prediction.

Original language	English
Title of host publication	Artificial Intelligence and Soft Computing - 16th International Conference, ICAISC 2017, Proceedings
Editors	Jacek M. Zurada, Lotfi A. Zadeh, Ryszard Tadeusiewicz, Leszek Rutkowski, Marcin Korytkowski, Rafal Scherer
Publisher	Springer Verlag
Pages	74-83
Number of pages	10
ISBN (Print)	9783319590592
DOIs	https://doi.org/10.1007/978-3-319-59060-8_8
Publication status	Published - 2017
Event	16th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2017 - Zakopane, Poland Duration: 11 Jun 2017 → 15 Jun 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10246 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2017
Country/Territory	Poland
City	Zakopane
Period	11/06/17 → 15/06/17

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2017.

Funding

This research was partially supported by The Scientific and Technological Research Council of Turkey (TUBITAK) under TEYDEB grant 3150156.

Funders	Funder number
TUBITAK	3150156
Türkiye Bilimsel ve Teknolojik Araştirma Kurumu

Keywords

Business failure prediction
Financial distress prediction
Machine learning
NARX

Access to Document

10.1007/978-3-319-59060-8_8

Cite this

Islek, I., Atakli, I. M., & Oguducu, S. G. (2017). A framework for business failure prediction. In J. M. Zurada, L. A. Zadeh, R. Tadeusiewicz, L. Rutkowski, M. Korytkowski, & R. Scherer (Eds.), Artificial Intelligence and Soft Computing - 16th International Conference, ICAISC 2017, Proceedings (pp. 74-83). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10246 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-59060-8_8

Islek, Irem ; Atakli, Idris Murat ; Oguducu, Sule Gunduz. / A framework for business failure prediction. Artificial Intelligence and Soft Computing - 16th International Conference, ICAISC 2017, Proceedings. editor / Jacek M. Zurada ; Lotfi A. Zadeh ; Ryszard Tadeusiewicz ; Leszek Rutkowski ; Marcin Korytkowski ; Rafal Scherer. Springer Verlag, 2017. pp. 74-83 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Business failure prediction systems help predict financial failures before they actually happen and provide an early warning for enterprises. Using machine learning techniques, instead of traditional statistical models, has brought a considerable increase in performance into the area of business failure prediction. This paper presents a frame-work for predicting business failures by using different machine learning techniques. We, also, implemented a novel model for business failure prediction based on NARX (nonlinear autoregressive network with exogenous inputs) feedback neural network to be included into this framework which is a recurrent dynamic network with feedback connections. Detailed experiments are conducted to compare the performance of these approaches. Especially, for the long-term business failure predictions, there are no other papers investigating the performance of NARX. To the best of our knowledge, this is the first time NARX algorithm is applied for long-term business failure prediction.",

keywords = "Business failure prediction, Financial distress prediction, Machine learning, NARX",

author = "Irem Islek and Atakli, {Idris Murat} and Oguducu, {Sule Gunduz}",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 16th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2017 ; Conference date: 11-06-2017 Through 15-06-2017",

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doi = "10.1007/978-3-319-59060-8_8",

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Islek, I, Atakli, IM & Oguducu, SG 2017, A framework for business failure prediction. in JM Zurada, LA Zadeh, R Tadeusiewicz, L Rutkowski, M Korytkowski & R Scherer (eds), Artificial Intelligence and Soft Computing - 16th International Conference, ICAISC 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10246 LNAI, Springer Verlag, pp. 74-83, 16th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2017, Zakopane, Poland, 11/06/17. https://doi.org/10.1007/978-3-319-59060-8_8

A framework for business failure prediction. / Islek, Irem; Atakli, Idris Murat; Oguducu, Sule Gunduz.
Artificial Intelligence and Soft Computing - 16th International Conference, ICAISC 2017, Proceedings. ed. / Jacek M. Zurada; Lotfi A. Zadeh; Ryszard Tadeusiewicz; Leszek Rutkowski; Marcin Korytkowski; Rafal Scherer. Springer Verlag, 2017. p. 74-83 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10246 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A framework for business failure prediction

AU - Islek, Irem

AU - Atakli, Idris Murat

AU - Oguducu, Sule Gunduz

N1 - Publisher Copyright: © Springer International Publishing AG 2017.

PY - 2017

Y1 - 2017

N2 - Business failure prediction systems help predict financial failures before they actually happen and provide an early warning for enterprises. Using machine learning techniques, instead of traditional statistical models, has brought a considerable increase in performance into the area of business failure prediction. This paper presents a frame-work for predicting business failures by using different machine learning techniques. We, also, implemented a novel model for business failure prediction based on NARX (nonlinear autoregressive network with exogenous inputs) feedback neural network to be included into this framework which is a recurrent dynamic network with feedback connections. Detailed experiments are conducted to compare the performance of these approaches. Especially, for the long-term business failure predictions, there are no other papers investigating the performance of NARX. To the best of our knowledge, this is the first time NARX algorithm is applied for long-term business failure prediction.

AB - Business failure prediction systems help predict financial failures before they actually happen and provide an early warning for enterprises. Using machine learning techniques, instead of traditional statistical models, has brought a considerable increase in performance into the area of business failure prediction. This paper presents a frame-work for predicting business failures by using different machine learning techniques. We, also, implemented a novel model for business failure prediction based on NARX (nonlinear autoregressive network with exogenous inputs) feedback neural network to be included into this framework which is a recurrent dynamic network with feedback connections. Detailed experiments are conducted to compare the performance of these approaches. Especially, for the long-term business failure predictions, there are no other papers investigating the performance of NARX. To the best of our knowledge, this is the first time NARX algorithm is applied for long-term business failure prediction.

KW - Business failure prediction

KW - Financial distress prediction

KW - Machine learning

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U2 - 10.1007/978-3-319-59060-8_8

DO - 10.1007/978-3-319-59060-8_8

M3 - Conference contribution

AN - SCOPUS:85020888196

SN - 9783319590592

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 74

EP - 83

BT - Artificial Intelligence and Soft Computing - 16th International Conference, ICAISC 2017, Proceedings

A2 - Zurada, Jacek M.

A2 - Zadeh, Lotfi A.

A2 - Tadeusiewicz, Ryszard

A2 - Rutkowski, Leszek

A2 - Korytkowski, Marcin

A2 - Scherer, Rafal

PB - Springer Verlag

T2 - 16th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2017

Y2 - 11 June 2017 through 15 June 2017

ER -

Islek I, Atakli IM, Oguducu SG. A framework for business failure prediction. In Zurada JM, Zadeh LA, Tadeusiewicz R, Rutkowski L, Korytkowski M, Scherer R, editors, Artificial Intelligence and Soft Computing - 16th International Conference, ICAISC 2017, Proceedings. Springer Verlag. 2017. p. 74-83. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-59060-8_8

A framework for business failure prediction

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Keywords

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