IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i10p2899-d233241.html
   My bibliography  Save this article

A Hybrid Model for Addressing the Relationship between Financial Performance and Sustainable Development

Author

Listed:
  • Yanfang Zhang

    (Straits Institute of Minjiang University, Fuzhou 350108, China
    Institute of Higher Education Cooperation and Exchange across the Taiwan Strait, Minjiang University, Fuzhou 350108, China)

  • Mushang Lee

    (Department of Accounting, Chinese Culture University, Taipei 11114, Taiwan)

Abstract

Measuring financial performance has become an essential topic due to the potential decimating impacts on the corporation itself as well as to whole societies during financial turmoil. In order to provide an overarching description of the multidimensional nature for measuring a corporation’s operations, it is preferable to employ data envelopment analysis (DEA). Different from prior research that merely focuses on a singular DEA performance rank, this study extends it to multiple DEA specifications (i.e., it combines inputs and outputs in several different ways) so as to make judgments more complete and robust. We also execute fuzzy visualization technique (i.e., nonlinear fuzzy robust principal component analysis, NFRPCA) to represent the main characteristics of data so that non-specialists can have better access to the results. The analyzed result is then fed into the restricted Boltzmann machine (RBM) to establish a model to forecast a firm’s operating performance. Even a fraction of accuracy improvement can result in considerable future savings to a firm and investors. When examined using real cases, the model is a promising alternative for operating performance forecasting and can assist both internal and external market participants.

Suggested Citation

  • Yanfang Zhang & Mushang Lee, 2019. "A Hybrid Model for Addressing the Relationship between Financial Performance and Sustainable Development," Sustainability, MDPI, vol. 11(10), pages 1-15, May.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:10:p:2899-:d:233241
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/10/2899/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/11/10/2899/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Raveh, Adi, 2000. "Co-plot: A graphic display method for geometrical representations of MCDM," European Journal of Operational Research, Elsevier, vol. 125(3), pages 670-678, September.
    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. Miltiadis D. Lytras & Anna Visvizi, 2018. "Who Uses Smart City Services and What to Make of It: Toward Interdisciplinary Smart Cities Research," Sustainability, MDPI, vol. 10(6), pages 1-16, June.
    4. David Parkin & Bruce Hollingsworth, 1997. "Measuring production efficiency of acute hospitals in Scotland, 1991-94: validity issues in data envelopment analysis," Applied Economics, Taylor & Francis Journals, vol. 29(11), pages 1425-1433.
    5. Basso, Antonella & Casarin, Francesco & Funari, Stefania, 2018. "How well is the museum performing? A joint use of DEA and BSC to measure the performance of museums," Omega, Elsevier, vol. 81(C), pages 67-84.
    6. Geng, Ruibin & Bose, Indranil & Chen, Xi, 2015. "Prediction of financial distress: An empirical study of listed Chinese companies using data mining," European Journal of Operational Research, Elsevier, vol. 241(1), pages 236-247.
    7. Robert G. Eccles & Ioannis Ioannou & George Serafeim, 2014. "The Impact of Corporate Sustainability on Organizational Processes and Performance," Management Science, INFORMS, vol. 60(11), pages 2835-2857, November.
    8. Hasan Dinçer & Serhat Yüksel & Seçil Şenel, 2018. "Analyzing the Global Risks for the Financial Crisis after the Great Depression Using Comparative Hybrid Hesitant Fuzzy Decision-Making Models: Policy Recommendations for Sustainable Economic Growth," Sustainability, MDPI, vol. 10(9), pages 1-15, September.
    9. Philip R. Lane, 2012. "The European Sovereign Debt Crisis," Journal of Economic Perspectives, American Economic Association, vol. 26(3), pages 49-68, Summer.
    10. Daniel Covitz & Nellie Liang & Gustavo A. Suarez, 2013. "The Evolution of a Financial Crisis: Collapse of the Asset-Backed Commercial Paper Market," Journal of Finance, American Finance Association, vol. 68(3), pages 815-848, June.
    11. Grout, Paul A. & Zalewska, Anna, 2016. "Stock market risk in the financial crisis," International Review of Financial Analysis, Elsevier, vol. 46(C), pages 326-345.
    12. C Serrano Cinca & C Mar Molinero, 2004. "Selecting DEA specifications and ranking units via PCA," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(5), pages 521-528, May.
    13. 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.
    14. Liang, Deron & Lu, Chia-Chi & Tsai, Chih-Fong & Shih, Guan-An, 2016. "Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study," European Journal of Operational Research, Elsevier, vol. 252(2), pages 561-572.
    15. Soytas, Mehmet Ali & Denizel, Meltem & Durak Usar, Damla, 2019. "Addressing endogeneity in the causal relationship between sustainability and financial performance," International Journal of Production Economics, Elsevier, vol. 210(C), pages 56-71.
    16. Martina Ciani & Francesca Gagliardi & Samuele Riccarelli & Gianni Betti, 2018. "Fuzzy Measures of Multidimensional Poverty in the Mediterranean Area: A Focus on Financial Dimension," Sustainability, MDPI, vol. 11(1), pages 1-13, December.
    17. Miltiadis D. Lytras & Vijay Raghavan & Ernesto Damiani, 2017. "Big Data and Data Analytics Research: From Metaphors to Value Space for Collective Wisdom in Human Decision Making and Smart Machines," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 13(1), pages 1-10, January.
    18. Sagarra, Marti & Mar-Molinero, Cecilio & Agasisti, Tommaso, 2017. "Exploring the efficiency of Mexican universities: Integrating Data Envelopment Analysis and Multidimensional Scaling," Omega, Elsevier, vol. 67(C), pages 123-133.
    19. Thomas Dyllick & Kai Hockerts, 2002. "Beyond the business case for corporate sustainability," Business Strategy and the Environment, Wiley Blackwell, vol. 11(2), pages 130-141, March.
    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. Miltiadis D. Lytras & Anna Visvizi, 2021. "Artificial Intelligence and Cognitive Computing: Methods, Technologies, Systems, Applications and Policy Making," Sustainability, MDPI, vol. 13(7), pages 1-3, March.
    2. Mushang Lee & Yu-Lan Huang, 2020. "Corporate Social Responsibility and Corporate Performance: A Hybrid Text Mining Algorithm," Sustainability, MDPI, vol. 12(8), pages 1-19, April.

    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. Ming-Fu Hsu & Ying-Shao Hsin & Fu-Jiing Shiue, 2022. "Business analytics for corporate risk management and performance improvement," Annals of Operations Research, Springer, vol. 315(2), pages 629-669, August.
    2. Lenka Papíková & Mário Papík, 2022. "Effects of classification, feature selection, and resampling methods on bankruptcy prediction of small and medium‐sized enterprises," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(4), pages 254-281, October.
    3. Yi Cao & Xiaoquan Liu & Jia Zhai & Shan Hua, 2022. "A two‐stage Bayesian network model for corporate bankruptcy prediction," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 455-472, January.
    4. Róbert Štefko & Jarmila Horváthová & Martina Mokrišová, 2021. "The Application of Graphic Methods and the DEA in Predicting the Risk of Bankruptcy," JRFM, MDPI, vol. 14(5), pages 1-19, May.
    5. Dawen Yan & Guotai Chi & Kin Keung Lai, 2020. "Financial Distress Prediction and Feature Selection in Multiple Periods by Lassoing Unconstrained Distributed Lag Non-linear Models," Mathematics, MDPI, vol. 8(8), pages 1-27, August.
    6. Hyeongjun Kim & Hoon Cho & Doojin Ryu, 2020. "Corporate Default Predictions Using Machine Learning: Literature Review," Sustainability, MDPI, vol. 12(16), pages 1-11, August.
    7. 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.
    8. Ben Jabeur, Sami & Serret, Vanessa, 2023. "Bankruptcy prediction using fuzzy convolutional neural networks," Research in International Business and Finance, Elsevier, vol. 64(C).
    9. Alberto Tron & Maurizio Dallocchio & Salvatore Ferri & Federico Colantoni, 2023. "Corporate governance and financial distress: lessons learned from an unconventional approach," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 27(2), pages 425-456, June.
    10. Mohammad Mahdi Mousavi & Jamal Ouenniche, 2018. "Multi-criteria ranking of corporate distress prediction models: empirical evaluation and methodological contributions," Annals of Operations Research, Springer, vol. 271(2), pages 853-886, December.
    11. 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.
    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. 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.
    14. 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.
    15. Haoming Wang & Xiangdong Liu, 2021. "Undersampling bankruptcy prediction: Taiwan bankruptcy data," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-17, July.
    16. 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.
    17. Youssef Zizi & Mohamed Oudgou & Abdeslam El Moudden, 2020. "Determinants and Predictors of SMEs’ Financial Failure: A Logistic Regression Approach," Risks, MDPI, vol. 8(4), pages 1-21, October.
    18. David Alaminos & Manuel Ángel Fernández, 2019. "Why do football clubs fail financially? A financial distress prediction model for European professional football industry," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-15, December.
    19. Casado Yusta, Silvia & Nœ–ez Letamendía, Laura & Pacheco Bonrostro, Joaqu’n Antonio, 2018. "Predicting Corporate Failure: The GRASP-LOGIT Model || Predicci—n de la quiebra empresarial: el modelo GRASP-LOGIT," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 26(1), pages 294-314, Diciembre.
    20. Hyunjung Nam & Won Gyun No & Youngsu Lee, 2017. "Are Commercial Financial Databases Reliable? New Evidence from Korea," Sustainability, MDPI, vol. 9(8), pages 1-23, August.

    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:11:y:2019:i:10:p:2899-:d:233241. 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.