Predictions of Failure and Financial Distress: A Study on Portuguese High and Medium-High Technology Small and Mid-Sized Enterprises

• Autorzy: Yehui Tong , Zélia Serrasqueiro
• Języki publikacji: EN

Abstrakty

EN

The main purpose of this paper is to find the differences in the impacts of financial factors on business failure and financial distress. Using the traditional logistic regression method, this paper studies the ability of financial indicators to predict business failure and financial distress of small and mid-sized enterprises in Portuguese high and medium-high technology manufacturing sectors. The research results show that: (1) differences between financially healthy firms and failed firms are more obvious than differences between financially healthy firms and financially distressed firms; (2) the accurate rate of failure prediction decreases with time prolonging (from one year to three years prior to the event), whereas that of financial distress prediction maintains stable at a relatively lower level; (3) profitability is the most important indicator, which is negatively related to the probability of both business failure and financial distress; (4) debt-related and liquidity-related factors (especially indebtedness and general liquidity) are also important in predicting business failure and financial distress. This paper enriches the research literature on the predictions of both business failure and financial distress. (original abstract)

Słowa kluczowe

EN

Enterprise finance; Small business; High technique; High-tech

PL

Finanse przedsiębiorstwa; Małe i średnie przedsiębiorstwa; Wysoka technika; Nowe technologie

• Czasopismo: Journal of International Studies
• Rocznik: 2021
• Tom: 14
• Numer: nr 2
• Strony: 9--25

Twórcy

autor

Yehui Tong

• Nanjing University of Finance and Economics, China

autor

Zélia Serrasqueiro

• Beira Interior University and CEFAGE Research Centre, Portugal

Bibliografia

• Achim, M.V., Borlea, S.N., & Găban, L.V. (2016). Failure prediction from the investors' view by using financial ratios. Lesson from Romania. E&M Economics and Management, 19(4), 117-133. doi: 10.15240/tul/001/2016-4-009
• Adamowicz, K., & Noga, T. (2018). Identification of financial ratios applicable in the construction of a prediction model for bankruptcy of wood industry enterprises. Folia Forestalia Polonica, Series A - Forestry, 60(1), 61-72. doi: 10.2478/ffp-2018-0006
• Agarwal, V. & Taffler, R. (2008). Comparing the performance of market-based and accounting-based bankruptcy prediction models. Journal of Banking & Finance, 32(8), 1541-1551. doi: 10.1016/j.jbankfin.2007.07.014
• Alessandrini, M., Valenza, A., Gramillano, A., Zingaretti, C., Zillmer, S., Holstein, F., Salvatori, G., Dallhammer, E., Gaupp-Berghausen, M., & Derszniak M. (2019). EU policy framework on SMEs: state of play and challenges. https://cor.europa.eu/en/engage/studies/Documents/EU-SMEs/EU-policy-SMEs.pdf
• Altman, E. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609. doi:10.1111/j.1540-6261.1968.tb00843.x
• Altman, E.I., Iwanicz-Drozdowska, M., Laitinen, E.K., & Suvas, A. (2017). Financial distress prediction in an international context: A review and empirical analysis of Altman's Z-score model. Journal of International Financial Management & Accounting, 28(2), 131-171. doi: 10.1111/jifm.12053
• Andreeva, G., Calabrese, R., & Osmetti, S.A. (2016). A comparative analysis of the UK and Italian small businesses using Generalised Extreme Value models. European Journal of Operational Research, 249(2), 506-516. doi: 10.1016/j.ejor.2015.07.062
• Andrikopoulos, P., & Khorasgani, A. (2018). Predicting unlisted SMEs' default: Incorporating market information on accounting-based models for improved accuracy. The British Accounting Review, 50(5), 559-573. doi: 10.1016/j.bar.2018.02.003
• Apergis, N., Bhattacharya, M., & Inekwe, J. (2019). Prediction of financial distress for multinational corporations: Panel estimations across countries. Applied Economics, 51(39), 4255-4269. doi: 10.1080/00036846.2019.1589646
• Audretsch, D.B., & Mahmood, T. (1995). New firm survival: New results using a hazard function. The Review of Economics and Statistics, 77(1), 97-103. doi: 10.2307/2109995
• Beaver, W.H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71-111. doi: 10.2307/2490171
• Blanco, A., Irimia, A., & Oliver, M.D. (2012). The prediction of bankruptcy of small firms in the UK using logistic regression. Análisis Financiero, 118, 32-40.
• Calabrese, R., Marra, G., & Osmetti, S.A. (2016). Bankruptcy prediction of small and medium enterprises using a flexible binary generalized extreme value model. Journal of the Operational Research Society, 67(4), 604-615. doi: 10.1057/ jors.2015.64
• Chappell, N., & Jaffe, A. (2018). Intangible investment and firm performance. Review of Industrial Organization, 52(4),509-559. doi: 10.1007/s11151-018-9629-9
• Charitou, A., Neophytou, E., & Charalambous, C. (2004). Predicting corporate failure: Empirical evidence for the UK. European Accounting Review, 13(3), 465-497. doi: 10.1080/0963818042000216811
• Cultrera, L., & Brédart, X. (2016). Bankruptcy prediction: The case of Belgian SMEs. Review of Accounting and Finance, 15(1), 101-119. doi: 10.1108/RAF-06-2014-0059
• Elston, J.A., & Audretsch, D.B. (2011). Financing the entrepreneurial decision: An empirical approach using experimental data on risk attitudes. Small Business Economics, 36(2), 209-222. doi: 10.1007/s11187-009-9210-x
• Fejér-Király, G. (2015). Bankruptcy prediction: A survey on evolution, critiques, and solutions. Acta Universitatis Sapientiae, Economics and Business, 3(1), 93-108. doi:10.1515/auseb-2015-0006
• Gepp, A., & Kumar, K. (2015). Predicting financial distress: A comparison of survival analysis and decision tree techniques. Procedia Computer Science, 54, 396 - 404. doi:10.1016/j.procs.2015.06.046
• Giacosa, E., Halili, E., Mazzoleni, A., Teodori, C., & Veneziani, M. (2016). Re-estimation of company insolvency prediction models: Survey on Italian manufacturing companies. Corporate Ownership and Control, 14(1), 159-174. doi: 10.22495/cocv14i1c1p1
• Görg, H., & Strobl, E. (2003). Multinational companies, technology spillovers and plant survival. The Scandinavian Journal of Economics, 105(4), 581-595. doi:10.1111/j.0347-0520.2003.00003.x
• Gregova, E., Valaskova, K., Adamko, P., Tumpach, M., & Jaros, J. (2020). Predicting financial distress of Slovak enterprises: Comparison of selected traditional and learning algorithms methods. Sustainability, 12, 1-17. doi: 10.3390/su12103954
• Gupta, J., Barzotto, M., & Khorasgani, A. (2018a). Does size matter in predicting SMEs failure? International Journal of Finance & Economics, 23(4), 571-605. doi: 10.1002/ijfe.1638
• Gupta, J., Gregoriou, A., & Ebrahimi, T. (2018b). Empirical comparison of hazard models in predicting SMEs failure. Quantitative Finance, 18(3), 437-466. doi:10.1080/14697688.2017.1307514
• Hill, N.T., Perry, S.E., & Andes, S. (1996). Evaluating firms in financial distress: An event history analysis. Journal of Applied Business Research, 12(3), 60-71.
• Jackson, R.H.G., & Wood, A. (2013). The performance of insolvency prediction and credit risk models in the UK: A comparative study. The British Accounting Review, 45(3), 183-202. doi: 10.1016/j.bar.2013.06.009
• Kubíčková, D., & Nulíček, V. (2016). Predictors of financial distress and bankruptcy model construction. International Journal of Management Science and Business Administration, 2(6), 34-41. doi: 10.18775/ijmsba.1849-5664-5419.2014.26.1003
• Malakauskas, A., & Lakstutiene, A. (2021). Financial distress prediction for small and medium enterprises using machine learning techniques. Inzinerine Ekonomika-Engineering Economics, 32(1), 4-14. doi: 10.5755/j01.ee.32.1.27382
• Mata, J., & Portugal, P. (1994). Life duration of new firms. The Journal of Industrial Economics, 42(3), 227-245. doi: 10.2307/2950567
• Mselmi, N., Lahiani, A., & Hamza, T. (2017). Financial distress prediction: The case of French small and medium********sized firms. International Review of Financial Analysis, 50, 67-80. doi: 10.1016/j.irfa.2017.02.004
• Muñoz-Izquierdo, N., Laitinen, E.K., Camacho-Miñano, M.-del-M., & Pascual-Ezama, D. (2020). Does audit report information improve financial distress prediction over Altman's traditional Z-Score model? Journal of International Financial Management & Accounting, 31(1), 65-97. doi: 10.1111/jifm.12110
• Ohlson, J.A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109-131. doi: 10.2307/2490395
• Oliveira, M.D.N.T., Ferreira, F.A.F., Ilander, G.O.P.-B., & Jalali, M.S. (2017). Integrating cognitive mapping and MCDA for bankruptcy prediction in small- and medium-sized enterprises. Journal of the Operational Research Society, 68(9), 985-997. doi: 10.1057/s41274-016-0166-3
• Omelka, J., Beranová, M., & Tabas, J. (2013). Comparison of the models of financial distress prediction. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 61(7), 2587-2592. doi: 10.11118/actaun201361072587
• Pacheco, L. (2015). SMEs probability of default: The case of the hospitality sector. Tourism & Management Studies, 11(1), 153-159.
• Pérez, S.E., Llopis, A.S., & Llopis, J.A.S. (2004). The determinants of survival of Spanish manufacturing firms. Review of Industrial Organization, 25(3), 251-273. doi:10.1007/s11151-004-1972-3
• Pervan, I., Pervan, M., & Kuvek, T. 2018. Firm failure prediction: The financial distress model vs traditional models. Croatian Operational Research Review, 9(2), 269-279. doi: 10.17535/crorr.2018.0021
• Pinheiro, T.G. (2019). Industry in Portugal: a sector in motion. Retrieved from: https://www.caixabankresearch.com/en/industry-portugal-sector-motion
• Platt, H.D., & Platt, M.B. (2002). Predicting corporate financial distress: Reflections on choice-based sample bias. Journal of Economics and Finance, 26(2), 184-199. doi:10.1007/BF02755985
• Pozzoli, M., & Paolone, F. (2016). An overlook at bankruptcy prediction in Italy in 2016: An application of the Altman's model on failed Italian manufacturing companies in the 2016-first quarter. International Journal of Accounting and Financial Reporting, 6(2), 293-309. doi:10.5296/ijafr.v6i2.10339
• Quintiliani, A. (2017). The costs of SME's financial distress: A cross-country analysis. Rivista Piccola Impresa/Small Business, 3, 71-92. doi: 10.14596/pisb.284
• Šarlija, N., & Jeger, M. (2011). Comparing financial distress prediction models before and during recession. Croatian Operational Research Review, 2(1), 133-142.
• Succurro, M., & Mannarino, L. (2014). The impact of financial structure on firms' probability of bankruptcy: A comparison across Western Europe convergence regions. Regional and Sectoral Economic Studies, 14(1), 81-94.
• Tascón, M.T., & Castaño, F. J. (2017). Selection of variables in small business failure analysis: Mean selection vs. median selection. Revista de Metodos Cuantitativos para la Economia y la Empresa, 24, 54-88.
• Vavřina, J., Hampel, D., & Janová, J. (2013). New approaches for the financial distress classification in agribusiness. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 61(4), 1177-1182. doi:10.11118/actaun201361041177
• Yazdanfar, D., & Nilsson, M. (2008). The bankruptcy determinants of Swedish SMEs. Institute for Small Business & Entrepreneurship, 5-7(2008), 1-14.
• Žiković, I.T. (2018). Challenges in predicting financial distress in emerging economies: The case of Croatia. Eastern European Economics, 56(1), 1-27. doi:10.1080/00128775.2017.1387059
• Zmijewski, M.E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 22, 59-82. doi: 10.2307/2490859.

Identyfikatory

DOI

10.14254/2071-8330.2021/14-2/1