IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i18p7529-d412624.html
   My bibliography  Save this article

Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM

Author

Listed:
  • Marek Vochozka

    (Institute of Technology and Business in České Budějovice, School of Expertness and Valuation, Okružní 517/10, 370 01 České Budějovice, Czech Republic)

  • Jaromir Vrbka

    (Institute of Technology and Business in České Budějovice, School of Expertness and Valuation, Okružní 517/10, 370 01 České Budějovice, Czech Republic)

  • Petr Suler

    (Institute of Technology and Business in České Budějovice, School of Expertness and Valuation, Okružní 517/10, 370 01 České Budějovice, Czech Republic)

Abstract

There is no doubt that the issue of making a good prediction about a company’s possible failure is very important, as well as complicated. A number of models have been created for this very purpose, of which one, the long short-term memory (LSTM) model, holds a unique position in that it generates very good results. The objective of this contribution is to create a methodology for the identification of a company failure (bankruptcy) using artificial neural networks (hereinafter referred to as “NN”) with at least one long short-term memory (LSTM) layer. A bankruptcy model was created using deep learning, for which at least one layer of LSTM was used for the construction of the NN. For the purposes of this contribution, Wolfram’s Mathematica 13 (Wolfram Research, Champaign, Illinois) software was used. The research results show that LSTM NN can be used as a tool for predicting company failure. The objective of the contribution was achieved, since the model of a NN was developed, which is able to predict the future development of a company operating in the manufacturing sector in the Czech Republic. It can be applied to small, medium-sized and manufacturing companies alike, as well as used by financial institutions, investors, or auditors as an alternative for evaluating the financial health of companies in a given field. The model is flexible and can therefore be trained according to a different dataset or environment.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:18:p:7529-:d:412624
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/18/7529/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/18/7529/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zeineb Affes & Rania Hentati-Kaffel, 2019. "Forecast bankruptcy using a blend of clustering and MARS model: case of US banks," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-03045877, HAL.
    2. Zhu, Xi & Zeng, Bo & Dong, Houqi & Liu, Jiaomin, 2020. "An interval-prediction based robust optimization approach for energy-hub operation scheduling considering flexible ramping products," Energy, Elsevier, vol. 194(C).
    3. David Alaminos & Agustín del Castillo & Manuel Ángel Fernández, 2016. "A Global Model for Bankruptcy Prediction," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-18, November.
    4. Altman, Edward I. & Haldeman, Robert G. & Narayanan, P., 1977. "ZETATM analysis A new model to identify bankruptcy risk of corporations," Journal of Banking & Finance, Elsevier, vol. 1(1), pages 29-54, June.
    5. Nakajima, Makoto, 2017. "Assessing bankruptcy reform in a model with temptation and equilibrium default," Journal of Public Economics, Elsevier, vol. 145(C), pages 42-64.
    6. 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.
    7. Zeineb Affes & Rania Hentati-Kaffel, 2019. "Forecast bankruptcy using a blend of clustering and MARS model: case of US banks," Annals of Operations Research, Springer, vol. 281(1), pages 27-64, October.
    8. 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.
    9. Somu, Nivethitha & M R, Gauthama Raman & Ramamritham, Krithi, 2020. "A hybrid model for building energy consumption forecasting using long short term memory networks," Applied Energy, Elsevier, vol. 261(C).
    10. Smyl, Slawek, 2020. "A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting," International Journal of Forecasting, Elsevier, vol. 36(1), pages 75-85.
    11. Ligang Zhou & Kin Keung Lai, 2017. "AdaBoost Models for Corporate Bankruptcy Prediction with Missing Data," Computational Economics, Springer;Society for Computational Economics, vol. 50(1), pages 69-94, June.
    12. Mai, Feng & Tian, Shaonan & Lee, Chihoon & Ma, Ling, 2019. "Deep learning models for bankruptcy prediction using textual disclosures," European Journal of Operational Research, Elsevier, vol. 274(2), pages 743-758.
    13. Correa-Jullian, Camila & Cardemil, José Miguel & López Droguett, Enrique & Behzad, Masoud, 2020. "Assessment of Deep Learning techniques for Prognosis of solar thermal systems," Renewable Energy, Elsevier, vol. 145(C), pages 2178-2191.
    14. Chang, Zihan & Zhang, Yang & Chen, Wenbo, 2019. "Electricity price prediction based on hybrid model of adam optimized LSTM neural network and wavelet transform," Energy, Elsevier, vol. 187(C).
    15. Beata Gavurova & Miroslava Packova & Maria Misankova & Lubos Smrcka, 2017. "Predictive potential and risks of selected bankruptcy prediction models in the Slovak business environment," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 18(6), pages 1156-1173, November.
    16. 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.
    17. 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.
    18. Nyitrai, Tamás & Virág, Miklós, 2019. "The effects of handling outliers on the performance of bankruptcy prediction models," Socio-Economic Planning Sciences, Elsevier, vol. 67(C), pages 34-42.
    19. Kim, A. & Yang, Y. & Lessmann, S. & Ma, T. & Sung, M.-C. & Johnson, J.E.V., 2020. "Can deep learning predict risky retail investors? A case study in financial risk behavior forecasting," European Journal of Operational Research, Elsevier, vol. 283(1), pages 217-234.
    20. Yang, Guotian & Wang, Yingnan & Li, Xinli, 2020. "Prediction of the NOx emissions from thermal power plant using long-short term memory neural network," Energy, Elsevier, vol. 192(C).
    21. Bo Wang & Liming Zhang & Hengrui Ma & Hongxia Wang & Shaohua Wan, 2019. "Parallel LSTM-Based Regional Integrated Energy System Multienergy Source-Load Information Interactive Energy Prediction," Complexity, Hindawi, vol. 2019, pages 1-13, November.
    22. Sushil Punia & Konstantinos Nikolopoulos & Surya Prakash Singh & Jitendra K. Madaan & Konstantia Litsiou, 2020. "Deep learning with long short-term memory networks and random forests for demand forecasting in multi-channel retail," International Journal of Production Research, Taylor & Francis Journals, vol. 58(16), pages 4964-4979, July.
    23. Zeineb Affes & Rania Hentati-Kaffel, 2019. "Forecast bankruptcy using a blend of clustering and MARS model: case of US banks," Post-Print hal-03045877, HAL.
    24. Tomas Kliestik & Jaromir Vrbka & Zuzana Rowland, 2018. "Bankruptcy prediction in Visegrad group countries using multiple discriminant analysis," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 13(3), pages 569-593, September.
    25. Qiao, Weibiao & Yang, Zhe, 2020. "Forecast the electricity price of U.S. using a wavelet transform-based hybrid model," Energy, Elsevier, vol. 193(C).
    26. Wei, Nan & Li, Changjun & Peng, Xiaolong & Li, Yang & Zeng, Fanhua, 2019. "Daily natural gas consumption forecasting via the application of a novel hybrid model," Applied Energy, Elsevier, vol. 250(C), pages 358-368.
    27. Jakub Horak & Jaromir Vrbka & Petr Suler, 2020. "Support Vector Machine Methods and Artificial Neural Networks Used for the Development of Bankruptcy Prediction Models and their Comparison," JRFM, MDPI, vol. 13(3), pages 1-15, March.
    28. Tian, Yong & Lai, Rucong & Li, Xiaoyu & Xiang, Lijuan & Tian, Jindong, 2020. "A combined method for state-of-charge estimation for lithium-ion batteries using a long short-term memory network and an adaptive cubature Kalman filter," Applied Energy, Elsevier, vol. 265(C).
    29. Hong, Jichao & Wang, Zhenpo & Chen, Wen & Yao, Yongtao, 2019. "Synchronous multi-parameter prediction of battery systems on electric vehicles using long short-term memory networks," Applied Energy, Elsevier, vol. 254(C).
    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. Beatriz González-Pérez & Concepción Núñez & José L. Sánchez & Gabriel Valverde & José Manuel Velasco, 2021. "Expert System to Model and Forecast Time Series of Epidemiological Counts with Applications to COVID-19," Mathematics, MDPI, vol. 9(13), pages 1-34, June.
    2. Beata Gavurova & Sylvia Jencova & Radovan Bacik & Marta Miskufova & Stanislav Letkovsky, 2022. "Artificial intelligence in predicting the bankruptcy of non-financial corporations," Oeconomia Copernicana, Institute of Economic Research, vol. 13(4), pages 1215-1251, December.
    3. Michał Kucia & Grzegorz Hajduk & Grzegorz Mazurek & Nina Kotula, 2021. "The Implementation of New Technologies in Customer Value Management—A Sustainable Development Perspective," Sustainability, MDPI, vol. 13(2), pages 1-16, January.
    4. Jakub Horak & Tomas Krulicky & Zuzana Rowland & Veronika Machova, 2020. "Creating a Comprehensive Method for the Evaluation of a Company," Sustainability, MDPI, vol. 12(21), pages 1-23, November.
    5. Mahdis sadat Jalaee & Alireza Shakibaei & Amin GhasemiNejad & Sayyed Abdolmajid Jalaee & Reza Derakhshani, 2021. "A Novel Computational Intelligence Approach for Coal Consumption Forecasting in Iran," Sustainability, MDPI, vol. 13(14), pages 1-16, July.
    6. Algirdas Justinas Staugaitis & Bernardas Vaznonis, 2022. "Financial Speculation Impact on Agricultural and Other Commodity Return Volatility: Implications for Sustainable Development and Food Security," Agriculture, MDPI, vol. 12(11), pages 1-27, November.

    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. P. K. Viswanathan & Suresh Srinivasan & N. Hariharan, 2020. "Predicting Financial Health of Banks for Investor Guidance Using Machine Learning Algorithms," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 19(2), pages 226-261, August.
    2. Elena Gregova & Katarina Valaskova & Peter Adamko & Milos Tumpach & Jaroslav Jaros, 2020. "Predicting Financial Distress of Slovak Enterprises: Comparison of Selected Traditional and Learning Algorithms Methods," Sustainability, MDPI, vol. 12(10), pages 1-17, May.
    3. Katarina Valaskova & Dominika Gajdosikova & Jaroslav Belas, 2023. "Bankruptcy prediction in the post-pandemic period: A case study of Visegrad Group countries," Oeconomia Copernicana, Institute of Economic Research, vol. 14(1), pages 253-293, March.
    4. Roman Vavrek & Ivana Kravčáková Vozárová & Rastislav Kotulič, 2021. "Evaluating the Financial Health of Agricultural Enterprises in the Conditions of the Slovak Republic Using Bankruptcy Models," Agriculture, MDPI, vol. 11(3), pages 1-19, March.
    5. Dariusz Sala & Kostiantyn Pavlov & Olena Pavlova & Anton Demchuk & Liubomur Matiichuk & Dariusz Cichoń, 2023. "Determining of the Bankrupt Contingency as the Level Estimation Method of Western Ukraine Gas Distribution Enterprises’ Competence Capacity," Energies, MDPI, vol. 16(4), pages 1-13, February.
    6. Vadlamani Ravi & Vadlamani Madhav, 2020. "Optimizing the reliability of a bank with Logistic Regression and Particle Swarm Optimization," Papers 2004.11122, arXiv.org.
    7. 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.
    8. Yu Zhao & Huaming Du & Qing Li & Fuzhen Zhuang & Ji Liu & Gang Kou, 2022. "A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data Perspective," Papers 2211.14997, arXiv.org, revised May 2023.
    9. Sami Ben Jabeur & Nicolae Stef & Pedro Carmona, 2023. "Bankruptcy Prediction using the XGBoost Algorithm and Variable Importance Feature Engineering," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 715-741, February.
    10. Andrzej Jaki & Wojciech Ćwięk, 2020. "Bankruptcy Prediction Models Based on Value Measures," JRFM, MDPI, vol. 14(1), pages 1-14, December.
    11. Michal Pavlicko & Marek Durica & Jaroslav Mazanec, 2021. "Ensemble Model of the Financial Distress Prediction in Visegrad Group Countries," Mathematics, MDPI, vol. 9(16), pages 1-26, August.
    12. Li, Chunyu & Lou, Chenxin & Luo, Dan & Xing, Kai, 2021. "Chinese corporate distress prediction using LASSO: The role of earnings management," International Review of Financial Analysis, Elsevier, vol. 76(C).
    13. Suzan Hol, 2006. "The influence of the business cycle on bankruptcy probability," Discussion Papers 466, Statistics Norway, Research Department.
    14. Zhou, Fanyin & Fu, Lijun & Li, Zhiyong & Xu, Jiawei, 2022. "The recurrence of financial distress: A survival analysis," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1100-1115.
    15. Lin, Hsiou-Wei William & Lo, Huai-Chun & Wu, Ruei-Shian, 2016. "Modeling default prediction with earnings management," Pacific-Basin Finance Journal, Elsevier, vol. 40(PB), pages 306-322.
    16. Enrico Supino & Nicola Piras, 2022. "Le performance dei modelli di credit scoring in contesti di forte instabilit? macroeconomica: il ruolo delle Reti Neurali Artificiali," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2022(2), pages 41-61.
    17. Adriana Csikosova & Maria Janoskova & Katarina Culkova, 2020. "Application of Discriminant Analysis for Avoiding the Risk of Quarry Operation Failure," JRFM, MDPI, vol. 13(10), pages 1-14, September.
    18. Haoming Wang & Xiangdong Liu, 2021. "Undersampling bankruptcy prediction: Taiwan bankruptcy data," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-17, July.
    19. Quader, Syed Manzur, 2017. "Differential effect of liquidity constraints on firm growth," Review of Financial Economics, Elsevier, vol. 32(C), pages 20-29.
    20. Serrano-Cinca, Carlos & Gutiérrez-Nieto, Begoña & Bernate-Valbuena, Martha, 2019. "The use of accounting anomalies indicators to predict business failure," European Management Journal, Elsevier, vol. 37(3), pages 353-375.

    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:gam:jsusta:v:12:y:2020:i:18:p:7529-:d:412624. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.