IDEAS home Printed from https://ideas.repec.org/a/cys/ecocyb/v50y2017i4p55-71.html
   My bibliography  Save this article

Analyzing the Profitability Performance of SMEs Using a Neural Model

Author

Listed:
  • Dumitru-Iulian NASTAC

    (Politehnica University of Bucharest)

  • Alexandru ISAIC-MANIU

    (Centre for Industrial and Service Economics, Romanian Academy)

  • Irina-Maria DRAGAN

    (The Bucharest University of Economic Studies)

Abstract

Special models of artificial neural networks (ANNs) have proven their worth in various and sometime unexpected domains. In this paper, our focus was to develop an ANN application in order to analyze the financial performance of the SMEs in Romania. For historical reasons, this sector seems to be still weakly developed in that country, both quantitative (being situated on one of the last places in the EU's entrepreneurial intensity) and qualitative, having a weak economic performance with a modest contribution to GDP. Literature shows the importance of this sector for the economies of different countries, and diverse scientific methods used for its description and analysis. One of our research purposes was the identification of those factors that condition the profitability of companies, thus providing useful directions and possible strategies for developing the SME sector. The selected information source was represented by the annual balance sheets, from about 8000 of medium-sized companies in Romania. As a means of verifying the obtained results, econometric methods were used, such as regression analysis, which could identify and validate the models that emphasize the dynamics with different influence factors. The conclusions obtained could prove their utility in both the investigation of the combining quantitative methods (ANN and regression), and in the SME sector management plan.

Suggested Citation

  • Dumitru-Iulian NASTAC & Alexandru ISAIC-MANIU & Irina-Maria DRAGAN, 2017. "Analyzing the Profitability Performance of SMEs Using a Neural Model," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 51(4), pages 55-71.
    • Handle: RePEc:cys:ecocyb:v:50:y:2017:i:4:p:55-71
    as

    Download full text from publisher

    File URL: ftp://www.eadr.ro/RePEc/cys/ecocyb_pdf/ecocyb4_2017p55-71.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Iza Lejárraga & Humberto López Rizzo & Harald Oberhofer & Susan Stone & Ben Shepherd, 2014. "Small and Medium-Sized Enterprises in Global Markets: A Differential Approach for Services?," OECD Trade Policy Papers 165, OECD Publishing.
    2. Irina M. DRAGAN & Alexandru ISAIC-MANIU, 2013. "A Barometer Of Entrepreneurial Dynamics Above The Crisis," Romanian Statistical Review, Romanian Statistical Review, vol. 61(7), pages 53-64, August.
    3. Nastac, Iulian & Bacivarov, Angelica & Costea, Adrian, 2009. "A Neuro-Classification Model for Socio-Technical Systems," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 6(3), pages 100-109, September.
    4. Tang, Zhi & Tang, Jintong, 2012. "Stakeholder–firm power difference, stakeholders' CSR orientation, and SMEs' environmental performance in China," Journal of Business Venturing, Elsevier, vol. 27(4), pages 436-455.
    5. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    6. 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.
    7. 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.
    8. Miroslav Mateev & Yanko Anastasov, 2010. "Determinants of small and medium sized fast growing enterprises in central and eastern Europe: a panel data analysis," Financial Theory and Practice, Institute of Public Finance, vol. 34(3), pages 269-295.
    9. Bera, Anil K. & Jarque, Carlos M., 1981. "Efficient tests for normality, homoscedasticity and serial independence of regression residuals : Monte Carlo Evidence," Economics Letters, Elsevier, vol. 7(4), pages 313-318.
    10. Frydman, Halina & Altman, Edward I & Kao, Duen-Li, 1985. "Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress," Journal of Finance, American Finance Association, vol. 40(1), pages 269-291, 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. Paul-Vasile Vezeteu & Dumitru Iulian Nastac, 2024. "Artificial Intelligence Integration in Business: Study of Employee Competences in Relation to the Organisational Needs," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 26(67), pages 832-832, August.

    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. Suzan Hol, 2006. "The influence of the business cycle on bankruptcy probability," Discussion Papers 466, Statistics Norway, Research Department.
    2. Maria H. Kim & Graham Partington, 2015. "Dynamic forecasts of financial distress of Australian firms," Australian Journal of Management, Australian School of Business, vol. 40(1), pages 135-160, February.
    3. 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.
    4. Wolfgang K. Härdle & Rouslan A. Moro & Dorothea Schäfer, 2004. "Rating Companies with Support Vector Machines," Discussion Papers of DIW Berlin 416, DIW Berlin, German Institute for Economic Research.
    5. 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.
    6. 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.
    7. Hu, Yu-Chiang & Ansell, Jake, 2007. "Measuring retail company performance using credit scoring techniques," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1595-1606, December.
    8. Mohammad Mahdi Mousavi & Jamal Ouenniche & Kaoru Tone, 2023. "A dynamic performance evaluation of distress prediction models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 756-784, July.
    9. Jiaming Liu & Chong Wu, 2017. "Dynamic forecasting of financial distress: the hybrid use of incremental bagging and genetic algorithm—empirical study of Chinese listed corporations," Risk Management, Palgrave Macmillan, vol. 19(1), pages 32-52, February.
    10. Abdelghani Maddi, 2018. "Analyse scientométrique de la crise économique," CEPN Working Papers 2018-08, Centre d'Economie de l'Université de Paris Nord.
    11. du Jardin, Philippe, 2015. "Bankruptcy prediction using terminal failure processes," European Journal of Operational Research, Elsevier, vol. 242(1), pages 286-303.
    12. M. A. Lagesh & Maram Srikanth & Debashis Acharya, 2018. "Corporate Performance during Business Cycles: Evidence from Indian Manufacturing Firms," Global Business Review, International Management Institute, vol. 19(5), pages 1261-1274, October.
    13. Zeineb Affes & Rania Hentati-Kaffel, 2016. "Predicting US banks bankruptcy: logit versus Canonical Discriminant analysis," Post-Print halshs-01281948, HAL.
    14. Catherine Refait, 2000. "Estimation du risque de défaut par une modélisation stochastique du bilan : Application à des firmes industrielles françaises," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-03718527, HAL.
    15. Esteban Alfaro Cortés & Matías Gámez Martínez & Noelia García Rubio, 2007. "Multiclass Corporate Failure Prediction by Adaboost.M1," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 13(3), pages 301-312, August.
    16. Zeineb Affes & Rania Hentati-Kaffel, 2016. "Predicting US banks bankruptcy: logit versus Canonical Discriminant analysis," Documents de travail du Centre d'Economie de la Sorbonne 16016, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    17. du Jardin, Philippe, 2012. "The influence of variable selection methods on the accuracy of bankruptcy prediction models," MPRA Paper 44383, University Library of Munich, Germany.
    18. Virág, Miklós & Kristóf, Tamás, 2005. "Az első hazai csődmodell újraszámítása neurális hálók segítségével [Recalculation of the first Hungarian bankruptcy-prediction model using neural networks]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(2), pages 144-162.
    19. Fernando García & Francisco Guijarro & Ismael Moya, 2013. "Monitoring credit risk in the social economy sector by means of a binary goal programming model," Service Business, Springer;Pan-Pacific Business Association, vol. 7(3), pages 483-495, September.
    20. Thomas E. Mckee, 2000. "Developing a bankruptcy prediction model via rough sets theory," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 9(3), pages 159-173, September.

    More about this item

    Keywords

    SMEs; neural networks; classification; econometric models.;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • M11 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Production Management
    • P12 - Political Economy and Comparative Economic Systems - - Capitalist Economies - - - Capitalist Enterprises

    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:cys:ecocyb:v:50:y:2017:i:4:p:55-71. 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: Corina Saman (email available below). General contact details of provider: https://edirc.repec.org/data/feasero.html .

    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.