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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
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    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. 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.
    5. 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.
    6. 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.
    7. 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.
    8. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    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.
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    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

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