IDEAS home Printed from https://ideas.repec.org/a/hin/complx/8682124.html
   My bibliography  Save this article

A Differential Evolution-Oriented Pruning Neural Network Model for Bankruptcy Prediction

Author

Listed:
  • Yajiao Tang
  • Junkai Ji
  • Yulin Zhu
  • Shangce Gao
  • Zheng Tang
  • Yuki Todo

Abstract

Financial bankruptcy prediction is crucial for financial institutions in assessing the financial health of companies and individuals. Such work is necessary for financial institutions to establish effective prediction models to make appropriate lending decisions. In recent decades, various bankruptcy prediction models have been developed for academics and practitioners to predict the likelihood that a loan customer will go bankrupt. Among them, Artificial Neural Networks (ANNs) have been widely and effectively applied in bankruptcy prediction. Inspired by the mechanism of biological neurons, we propose an evolutionary pruning neural network (EPNN) model to conduct financial bankruptcy analysis. The EPNN possesses a dynamic dendritic structure that is trained by a global optimization learning algorithm: the Adaptive Differential Evolution algorithm with Optional External Archive (JADE). The EPNN can reduce the computational complexity by removing the superfluous and ineffective synapses and dendrites in the structure and is simultaneously able to achieve a competitive classification accuracy. After simplifying the structure, the EPNN can be entirely replaced by a logic circuit containing the comparators and the logic NOT, AND, and OR gates. This mechanism makes it feasible to apply the EPNN to bankruptcy analysis in hardware implementations. To verify the effectiveness of the EPNN, we adopt two benchmark datasets in our experiments. The experimental results reveal that the EPNN outperforms the Multilayer Perceptron (MLP) model and our previously developed preliminary pruning neural network (PNN) model in terms of accuracy, convergence speed, and Area Under the Receiver Operating Characteristics (ROC) curve (AUC). In addition, the EPNN also provides competitive and satisfactory classification performances in contrast with other commonly used classification methods.

Suggested Citation

  • Yajiao Tang & Junkai Ji & Yulin Zhu & Shangce Gao & Zheng Tang & Yuki Todo, 2019. "A Differential Evolution-Oriented Pruning Neural Network Model for Bankruptcy Prediction," Complexity, Hindawi, vol. 2019, pages 1-21, August.
  • Handle: RePEc:hin:complx:8682124
    DOI: 10.1155/2019/8682124
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/8503/2019/8682124.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/8503/2019/8682124.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2019/8682124?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Zhang, Guoqiang & Y. Hu, Michael & Eddy Patuwo, B. & C. Indro, Daniel, 1999. "Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis," European Journal of Operational Research, Elsevier, vol. 116(1), pages 16-32, July.
    2. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    3. Kiefer, Nicholas M., 2009. "Default estimation for low-default portfolios," Journal of Empirical Finance, Elsevier, vol. 16(1), pages 164-173, January.
    4. David J. Hand, 2012. "Assessing the Performance of Classification Methods," International Statistical Review, International Statistical Institute, vol. 80(3), pages 400-414, December.
    5. Muriel Perez, 2006. "Artificial Neural Networks And Bankruptcy Forecasting : A State Of The Art," Post-Print halshs-00522129, HAL.
    6. Marta Morey & Susan K. Yee & Tory Herman & Aljoscha Nern & Enrique Blanco & S. Lawrence Zipursky, 2008. "Coordinate control of synaptic-layer specificity and rhodopsins in photoreceptor neurons," Nature, Nature, vol. 456(7223), pages 795-799, December.
    7. Christof Koch, 1997. "Computation and the single neuron," Nature, Nature, vol. 385(6613), pages 207-210, January.
    8. Hong Wang & Qingsong Xu & Lifeng Zhou, 2015. "Large Unbalanced Credit Scoring Using Lasso-Logistic Regression Ensemble," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-20, February.
    9. Hung, Ming S. & Denton, James W., 1993. "Training neural networks with the GRG2 nonlinear optimizer," European Journal of Operational Research, Elsevier, vol. 69(1), pages 83-91, August.
    10. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    11. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    12. Fabrizio Gabbiani & Holger G. Krapp & Christof Koch & Gilles Laurent, 2002. "Multiplicative computation in a visual neuron sensitive to looming," Nature, Nature, vol. 420(6913), pages 320-324, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Marek Vochozka & Jaromir Vrbka & Petr Suler, 2020. "Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM," Sustainability, MDPI, vol. 12(18), pages 1-17, September.
    2. Sabek Amine, 2023. "Unveiling the diverse efficacy of artificial neural networks and logistic regression: A comparative analysis in predicting financial distress," Croatian Review of Economic, Business and Social Statistics, Sciendo, vol. 9(1), pages 16-32, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Le, Hong Hanh & Viviani, Jean-Laurent, 2018. "Predicting bank failure: An improvement by implementing a machine-learning approach to classical financial ratios," Research in International Business and Finance, Elsevier, vol. 44(C), pages 16-25.
    2. Juraini Zainol Abidin & Nur Adiana Hiau Abdullah & Karren Lee-Hwei Khaw, 2020. "Predicting SMEs Failure: Logistic Regression vs Artificial Neural Network Models," Capital Markets Review, Malaysian Finance Association, vol. 28(2), pages 29-41.
    3. Foo See Liang & Shaak Pathak, 2019. "Understanding the Connection of Performance and Z-Scores for Manufacturing Firms in South Korea," Journal of Asian Development, Macrothink Institute, vol. 5(3), pages 37-46, November.
    4. En-Der Su & Shih-Ming Huang, 2010. "Comparing Firm Failure Predictions Between Logit, KMV, and ZPP Models: Evidence from Taiwan’s Electronics Industry," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 17(3), pages 209-239, September.
    5. Vladislav V. Afanasev & Yulia A. Tarasova, 2022. "Default Prediction for Housing and Utilities Management Firms Using Non-Financial Data," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 6, pages 91-110, December.
    6. Adler Haymans Manurung & Derwin Suhartono & Benny Hutahayan & Noptovius Halimawan, 2023. "Probability Bankruptcy Using Support Vector Regression Machines," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 13(1), pages 1-3.
    7. Andrea Bedin & Monica Billio & Michele Costola & Loriana Pelizzon, 2019. "Credit Scoring in SME Asset-Backed Securities: An Italian Case Study," JRFM, MDPI, vol. 12(2), pages 1-28, May.
    8. Su-Han Woo & Min-Su Kwon & Kum Fai Yuen, 2021. "Financial determinants of credit risk in the logistics and shipping industries," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 23(2), pages 268-290, June.
    9. Qunfeng LIAO & Seyed MEHDIAN, 2016. "Measuring Financial Distress And Predicting Corporate Bankruptcy: An Index Approach," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 17, pages 33-51, June.
    10. Youssef Zizi & Amine Jamali-Alaoui & Badreddine El Goumi & Mohamed Oudgou & Abdeslam El Moudden, 2021. "An Optimal Model of Financial Distress Prediction: A Comparative Study between Neural Networks and Logistic Regression," Risks, MDPI, vol. 9(11), pages 1-24, November.
    11. Ilyes Abid & Rim Ayadi & Khaled Guesmi & Farid Mkaouar, 2022. "A new approach to deal with variable selection in neural networks: an application to bankruptcy prediction," Annals of Operations Research, Springer, vol. 313(2), pages 605-623, June.
    12. Javed Iqbal & Furrukh Bashir & Rashid Ahmad & Hina Arshad, 2022. "Predicting Bankruptcy through Neural Network:Case of PSX Listed Companies," iRASD Journal of Management, International Research Alliance for Sustainable Development (iRASD), vol. 4(2), pages 299-315, june.
    13. Dejan JOVANOVIĆ & Mirjana TODOROVIĆ & Milka GRBIĆ, 2017. "Financial Indicators As Predictors Of Illiquidity," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 128-149, March.
    14. Şaban Çelik, 2013. "Micro Credit Risk Metrics: A Comprehensive Review," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 20(4), pages 233-272, October.
    15. Khaled Halteh & Kuldeep Kumar & Adrian Gepp, 2018. "Using Cutting-Edge Tree-Based Stochastic Models to Predict Credit Risk," Risks, MDPI, vol. 6(2), pages 1-13, May.
    16. Goriunov Dmytro & Venzhyk Katerina, 2013. "Loan Default Prediction in Ukrainian Retail Banking," EERC Working Paper Series 13/07e, EERC Research Network, Russia and CIS.
    17. Jie Sun & Mengjie Zhou & Wenguo Ai & Hui Li, 2019. "Dynamic prediction of relative financial distress based on imbalanced data stream: from the view of one industry," Risk Management, Palgrave Macmillan, vol. 21(4), pages 215-242, December.
    18. Jamal Ouenniche & Kaoru Tone, 2017. "An out-of-sample evaluation framework for DEA with application in bankruptcy prediction," Annals of Operations Research, Springer, vol. 254(1), pages 235-250, July.
    19. Zeineb Affes & Rania Hentati-Kaffel, 2016. "Forecast bankruptcy using a blend of clustering and MARS model - Case of US banks," Documents de travail du Centre d'Economie de la Sorbonne 16026, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    20. Francesco Ciampi & Valentina Cillo & Fabio Fiano, 2020. "Combining Kohonen maps and prior payment behavior for small enterprise default prediction," Small Business Economics, Springer, vol. 54(4), pages 1007-1039, April.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:complx:8682124. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.