IDEAS home Printed from https://ideas.repec.org/a/spr/infsem/v18y2020i4d10.1007_s10257-018-0388-9.html
   My bibliography  Save this article

RETRACTED ARTICLE: Intelligent hybrid model for financial crisis prediction using machine learning techniques

Author

Listed:
  • J. Uthayakumar

    (Pondicherry University)

  • Noura Metawa

    (Mansoura University
    Regis University)

  • K. Shankar

    (Kalasalingam Academy of Research and Education)

  • S. K. Lakshmanaprabu

    (B. S. Abdur Rahman Crescent Institute of Science and Technology)

Abstract

Financial crisis prediction (FCP) plays a vital role in the economic phenomenon. The precise prediction of the number and possibility of failing firms acts as an index of the growth and strength of a nation’s economy. Traditionally, several methods have been presented for effective FCP. On the other hand, the classification performance and prediction accuracy and data legality is not good enough for practical applications. In addition, many of the developed methods perform well for some of the particular dataset but not adaptable to different dataset. Hence, there is a requirement to develop an efficient prediction model for better classification performance and adaptable to diverse dataset. This paper presents a cluster based classification model, comprises of two stages: improved K-means clustering and a fitness-scaling chaotic genetic ant colony algorithm (FSCGACA) based classification model. In the first stage, an improved K-means algorithm is devised to eliminate the wrongly clustered data. Then, a rule-based model is selected to design to fit the given dataset. At the end, FSCGACA is employed for seeking the optimal parameters of the rule-based model. The proposed algorithm is employed to a collection of three benchmark dataset which include qualitative bankruptcy dataset, Weislaw dataset and Polish dataset. A detailed statistical analysis of the dataset is also given. The results analysis ensured that the presented FCP model is superior to other classification model based on the different measures and also found to be more appropriate for diverse dataset.

Suggested Citation

  • J. Uthayakumar & Noura Metawa & K. Shankar & S. K. Lakshmanaprabu, 2020. "RETRACTED ARTICLE: Intelligent hybrid model for financial crisis prediction using machine learning techniques," Information Systems and e-Business Management, Springer, vol. 18(4), pages 617-645, December.
    • Handle: RePEc:spr:infsem:v:18:y:2020:i:4:d:10.1007_s10257-018-0388-9
    DOI: 10.1007/s10257-018-0388-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10257-018-0388-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10257-018-0388-9?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sumit Sarkar & Ram S. Sriram, 2001. "Bayesian Models for Early Warning of Bank Failures," Management Science, INFORMS, vol. 47(11), pages 1457-1475, November.
    2. Sun, Lili & Shenoy, Prakash P., 2007. "Using Bayesian networks for bankruptcy prediction: Some methodological issues," European Journal of Operational Research, Elsevier, vol. 180(2), pages 738-753, July.
    3. 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.
    4. West, Robert Craig, 1985. "A factor-analytic approach to bank condition," Journal of Banking & Finance, Elsevier, vol. 9(2), pages 253-266, June.
    5. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    6. Paramjeet & V. Ravi, 2011. "Bacterial foraging trained wavelet neural networks: application to bankruptcy prediction in banks," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 3(3), pages 261-280.
    7. Nigib Sharma & Nampally Arun & Vadlamani Ravi, 2013. "An ant colony optimisation and Nelder-Mead simplex hybrid algorithm for training neural networks: an application to bankruptcy prediction in banks," International Journal of Information and Decision Sciences, Inderscience Enterprises Ltd, vol. 5(2), pages 188-203.
    8. Yudong Zhang & Lenan Wu & Shuihua Wang, 2013. "UCAV Path Planning by Fitness-Scaling Adaptive Chaotic Particle Swarm Optimization," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-9, July.
    Full references (including those not matched with items on IDEAS)

    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. Leila Bateni & Farshid Asghari, 2020. "Bankruptcy Prediction Using Logit and Genetic Algorithm Models: A Comparative Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 55(1), pages 335-348, January.
    2. Sajad Abdipour & Ahmad Nasseri & Mojtaba Akbarpour & Hossein Parsian & Shahrzad Zamani, 2013. "Integrating Neural Network and Colonial Competitive Algorithm: A New Approach for Predicting Bankruptcy in Tehran Security Exchange," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 3(11), pages 1528-1539, November.
    3. Dong Zhao & Chunyu Huang & Yan Wei & Fanhua Yu & Mingjing Wang & Huiling Chen, 2017. "An Effective Computational Model for Bankruptcy Prediction Using Kernel Extreme Learning Machine Approach," Computational Economics, Springer;Society for Computational Economics, vol. 49(2), pages 325-341, February.
    4. Samir Trabelsi & Roc He & Lawrence He & Martin Kusy, 2015. "A comparison of Bayesian, Hazard, and Mixed Logit model of bankruptcy prediction," Computational Management Science, Springer, vol. 12(1), pages 81-97, January.
    5. Akkoç, Soner, 2012. "An empirical comparison of conventional techniques, neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis: The case of Turkish cred," European Journal of Operational Research, Elsevier, vol. 222(1), pages 168-178.
    6. Chiuling Lu & Ann Yang & Jui-Feng Huang, 2015. "Bankruptcy predictions for U.S. air carrier operations: a study of financial data," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 39(3), pages 574-589, July.
    7. Alam, Nurul & Gao, Junbin & Jones, Stewart, 2021. "Corporate failure prediction: An evaluation of deep learning vs discrete hazard models," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 75(C).
    8. Hui Li & Jie Sun, 2010. "Forecasting business failure in China using case-based reasoning with hybrid case respresentation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(5), pages 486-501.
    9. Vicente García & Ana I. Marqués & J. Salvador Sánchez & Humberto J. Ochoa-Domínguez, 2019. "Dissimilarity-Based Linear Models for Corporate Bankruptcy Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 53(3), pages 1019-1031, March.
    10. Matthew Smith & Francisco Alvarez, 2022. "Predicting Firm-Level Bankruptcy in the Spanish Economy Using Extreme Gradient Boosting," Computational Economics, Springer;Society for Computational Economics, vol. 59(1), pages 263-295, January.
    11. Ruey-Ching Hwang & Jhao-Siang Siao & Huimin Chung & C. Chu, 2011. "Assessing bankruptcy prediction models via information content of technical inefficiency," Journal of Productivity Analysis, Springer, vol. 36(3), pages 263-273, December.
    12. J. F. Juliá-Igual & R. Cervelló-Royo & I. Berné-Lafuente, 2017. "Market value analysis of a Chinese e-commerce holding group: a multicriteria approach," Service Business, Springer;Pan-Pacific Business Association, vol. 11(3), pages 475-490, September.
    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. 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.
    15. Lin, Fengyi & Yeh, Ching Chiang & Lee, Meng Yuan, 2013. "A Hybrid Business Failure Prediction Model Using Locally Linear Embedding And Support Vector Machines," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 82-97, March.
    16. Zeineb Affes & Rania Hentati-Kaffel, 2016. "Forecast bankruptcy using a blend of clustering and MARS model - Case of US banks," Post-Print halshs-01314553, HAL.
    17. Chih-Yang Tsai, 2017. "The impact of cost structure on supply chain cash flow risk," International Journal of Production Research, Taylor & Francis Journals, vol. 55(22), pages 6624-6637, November.
    18. Kim, Hong Sik & Sohn, So Young, 2010. "Support vector machines for default prediction of SMEs based on technology credit," European Journal of Operational Research, Elsevier, vol. 201(3), pages 838-846, March.
    19. Demyanyk, Yuliya & Hasan, Iftekhar, 2010. "Financial crises and bank failures: A review of prediction methods," Omega, Elsevier, vol. 38(5), pages 315-324, October.
    20. 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.

    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:spr:infsem:v:18:y:2020:i:4:d:10.1007_s10257-018-0388-9. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.