Family businesses and predictability of financial strength: a Hungarian study
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Author(s)Judit SágiLink to ORCID Index: https://orcid.org/0000-0003-4197-3530
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Nick ChandlerLink to ORCID Index: https://orcid.org/0000-0001-7408-0793
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Csaba LentnerLink to ORCID Index: https://orcid.org/0000-0003-2241-782X
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DOIhttp://dx.doi.org/10.21511/ppm.18(2).2020.39
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Article InfoVolume 18 2020, Issue #2, pp. 476-489
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174 Downloads
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Creative Commons Attribution 4.0 International License
The aim of this study is to examine how bankruptcy prediction models forecast financial strength for family businesses. Three predictive tests are used to study financial strength for three consecutive years (2016, 2017 and 2018) for a sample of 462,200 active Hungarian companies using the Amadeus database and expert data. Complex statistical model tests for credit assessment (bankruptcy predictions) are performed by size and ownership of the companies. It is found that the revised Altman model is impeded by a superfluous high weighting on net working capital; therefore, IN05 Quick Test predicted better chances for businesses in generating cash flows in a small emerging economy. By re-formulating the Bankruptcy Index of Karas and Režňáková and refining its coefficients, the modified Bankruptcy Index is more robust for predicting the financial health of family businesses on a cash flow basis. The test results of this modified Bankruptcy Index confirm the relative advance of family businesses in creating added value for owners. Practical implications arise from a management perspective: family businesses work better with predictability of survival in accordance with the model; therefore, their ability to adapt to financial constraints caused by crises is also more promising.
- Keywords
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JEL Classification (Paper profile tab)C53, G32
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References43
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Tables6
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Figures1
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- Figure 1. The three patterns of failure
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- Table 1. Altman Z-scores of the total of active Hungarian companies grouped by size
- Table 2. Altman Z-scores of the total of active Hungarian companies grouped by size and differentiated by ownership
- Table 3. IN05 values of the total of active Hungarian companies grouped by size
- Table 4. IN05 values of the total of active Hungarian companies grouped by size and differentiated by ownership
- Table 5. Bankruptcy Index values of the total of active Hungarian companies grouped by size
- Table 6. Bankruptcy Index values of the total of active Hungarian companies grouped by size and differentiated by ownership
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- Altman, E. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609.
- Altman, E. (2000). Predicting financial distress of companies: Revisiting the Z-score and Zeta® models. Handbook of Research Methods and Applications in Empirical Finance, 428-456.
- Altman, E., Haldeman, R., & Narayanan, P. (1977). ZetaTM analysis a new model to identify bankruptcy risk of corporations. Journal of Banking and Finance, 1(1), 29-54.
- Amann, B., & Jaussaud, J. (2012). Family and non-family business resilience in an economic downturn. Asia Pacific Business Review, 18(2), 203-223.
- Baixauli, S., & Módica-Milo, A. (2010). The bias of unhealthy SMEs in bankruptcy prediction models. Journal of Small Business and Enterprise Development, 17(1), 60-77.
- Beaver, W. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4(3), 71-111.
- Beaver, W., McNichols, M., & Rhie, J. (2005). Have financial statements become less informative? Evidence from the ability of financial ratios to predict bankruptcy. Review of Accounting Studies, 10, 93-122.
- Blum, M. (1974). Failing company discriminant analysis. Journal of Accounting Research, 12(1), 1-25.
- Bohdalová, M., & Klempaiová, N. (2017). Comparison of bankruptcy models for prediction of the financial health of the Slovak civil engineering companies. Perspectives of Business and Entrepreneurship Development in Digital Age (16th International Conference, September 20-22, 2017 Brno, Czech Republic, conference paper: 1-9).
- Chandler, N., & Sági, J. (2018). The secret sauce: a review of the characteristics that define entrepreneurs and owner-managers. Economics, Management, Innovation, 209-215.
- Cho, S., Hong, H., & Ha, B-C. (2010). A hybrid approach based on the combination of variable selection using decision trees and case-based reasoning using the Mahalanobis distance: For bankruptcy prediction. Expert Systems with Applications, 37(4). 3482-3488.
- Cimpoeru, S. (2014). Scoring Functions and Bankruptcy Prediction Models – Case Study for Romanian Companies. Procedia Economics and Finance, 10, 217-226.
- Crutzen, N. (2009) Essays on the prevention of small business failure: Taxonomy and validation of five explanatory business failure patterns (EBFPs) (594 p.). Liege: University of Liege.
- Dolejšová, M. (2015). Is it Worth Comparing Different Bankruptcy Models? Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 63(2), 525-531.
- Edmister, R. (1972). An empirical test of financial ratio analysis for small business failure prediction. Journal of Financial and Quantitative Analysis, 7(2), 1477-1493.
- Fang-Mei, T., & Yi-Chung, H. (2010). Comparing four bankruptcy prediction models: Logit, quadratic interval logit, neural and fuzzy neural networks. Expert Systems with Applications, 37(3), 1846-1853.
- Gavurova, B., Packova, M., Misankova, M., & Smrcka, L. (2017). Predictive potential and risks of selected bankruptcy prediction models in the Slovak business environment. Journal of Business Economics and Management, 18(6), 1156-1173.
- Grice, J., & Dugan, M. (2001). The limitations of bankruptcy prediction models: Some cautions for the researchers. Review of Quantitative Finance and Accounting, 17, 151-166.
- Gu, Z. (2002). Analyzing bankruptcy in the restaurant industry: A multiple discriminant model. International Journal of Hospitality Management, 21(1), 25-42.
- Karas, M., & Režňáková, M. (2014). Bankruptcy Prediction Models: Can the Prediction Power of the Models be Improved by Using Dynamic Indicators?” Procedia Economics and Finance, 12, 565-574.
- Karas, M., & Režňáková, M. (2015). Predicting Bankruptcy under Alternative Conditions: The Effect of a Change in Industry and Time Period on the Accuracy of the Model. Procedia – Social and Behavioral Sciences, 213, 397-403.
- Karas, M., Režňáková, M., Bartos, V., & Zinecker, M. (2013). Possibilities for the Application of the Altman Model within the Czech Republic, Recent Research in Law Science and Finances. Proceedings of the 4th International conference on Finance, Accounting and Law (ICFA 13): 203-208.
- Keasey, K., & Watson, R. (1991). The State of the Art of Small Firm Failure Prediction: Achievements and Prognosis. International Small Business Journal, 9, 11-28.
- Kim, S. Y. (2011). Prediction of hotel bankruptcy using support vector machine, artificial neural network, logistic regression, and multivariate discriminant analysis. The Service Industries Journal, 31(3), 441-468.
- Korom, E., & Sági, J. (2005). Measures on competitiveness in agriculture. Journal of Central European Agriculture, 6(3), 375-380.
- Kozlovskyi, S., Butyrskyi, A., Poliakov, B., Bobkova, A., Lavrov, R., & Ivanyuta, N. (2019). Management and comprehensive assessment of the probability of bankruptcy of Ukrainian enterprises based on the methods of fuzzy sets theory. Problems and Perspectives in Management, 17(3), 370-381.
- Lentner, Cs., & Kolozsi, P. P. (2019). Innovative ways of thinking concerning economic governance after the global financial crisis. Problems and Perspectives in Management, 17(3), pp. 122-131.
- Lentner, Cs. (2020). East of Europe, West of Asia (300 p.). Paris: L’Harmattan Publishing.
- Lukason, O., & Käsper, K. (2017). Failure prediction of government funded start-up firms. Investment Management and Financial Innovations, 14, 296-306.
- Matolcsy, Gy. (2016). Economic balance and growth. Budapest: Kairosz Publishing.
- Mcmillan, C., & Overall, J. (2017). Crossing the Chasm and Over the Abyss: Perspectives on Organizational Failure. Academy of Management Perspectives, 31(4), 271-287.
- Neumaier, I., & Neumaierová, I. (2005). Index IN 05. Proceedings of the Evropské finanční systémy, 143-148.
- OeNB. (2004). OeNB Guidelines on Credit Risk Management: Rating models and validation. Vienna: Oesterreichische Nationalbank.
- Ohlson, J. (1980). Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, 18(1), 109-131.
- Platt, H. & Platt, M. (1990). Development of a Class of Stable Predictive Variables: The Case of Bankruptcy Prediction. Journal of Business Finance & Accounting, 17(1), 31-51.
- Sági J., & Lentner, Cs. (2019) Post-crisis trends in household credit market behavior: evidence from Hungary (Literature review). Banks and Bank Systems, 14(3), 162-174.
- Sági, J., & Nikulin, E. E. (2017). The economic effect of Russia imposing a food embargo on the European Union with Hungary as an example. Studies in Agricultural Economics, 119(2), 85-90.
- Scott, J. (1981). The probability of bankruptcy: a comparison of empirical predictions and theoretical models. Journal of Banking & Finance, 5, 317-344.
- Shkolnyk, I., Pisula, T., Loboda, L., & Nebaba, N. (2019). Financial crisis of real sector enterprises: an integral assessment. Problems and Financial Innovations, 16(4), 366-381.
- Thanh Tung, D., & Phung, V. (2019). An application of Altman Z-score model to analyze the bankruptcy risk: cases of multidisciplinary enterprises in Vietnam. Investment Management and Financial Innovations, 16(4), 181-191.
- Thornhill, S., & Amit, R. (2003). Learning about Failure: Bankruptcy, Firm Age and the Resource-Based View. Organization Science, 15, 497-509.
- Wu, S., Gaunt, C., & Gray, S. (2010). A comparison of alternative bankruptcy prediction models. Journal of Contemporary Accounting & Economics, 6, 34-45.
- Zmijewski, M. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 22, 59-82.
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Management of enterprise’s financial sustainability and improvement of its methods
Galyna Azarenkova ,
Olena Golovko ,
Kateryna Abrosimova
doi: http://dx.doi.org/10.21511/afc.02(1).2018.01
Accounting and Financial Control Volume 2, 2018-2019 Issue #1 pp. 1-14 Views: 5331 Downloads: 753 TO CITE АНОТАЦІЯThe article is dedicated to the solution of the relevant issue, concerning management of enterprise`s financial sustainability. Financial sustainability assessment is one of the most important principles of enterprise`s activity, which provides information about financial capabilities of the company at the time of its evaluation and for the future. It is a requirement for the enterprise activity, which provides a high level of competitiveness, efficiency and intensity. Thus, the restoration and strengthening of financial sustainability is a priority task for the enterprise, a basic precondition for its effective functioning. The purpose of the research is to analyze financial sustainability and to improve the methods and approaches of its evaluation. The following methods were used in this research: financial and economic analysis, economic and mathematical modeling, analysis and synthesis, comparison. The main results of the study are following: the theoretical and essential characteristics of enterprise financial sustainability has been determined; the financial status of PJSC “Turboatom” has been analyzed; the taxonomic index of financial sustainability has been calculated and the forecast of its significance has been made, the approaches to increase enterprise financial sustainability have been proposed.
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Determinants of profitability in Jordanian services companies
Omar K. Gharaibeh ,
Marie H. Bani Khaled
doi: http://dx.doi.org/10.21511/imfi.17(1).2020.24
Investment Management and Financial Innovations Volume 17, 2020 Issue #1 pp. 277-290 Views: 2408 Downloads: 415 TO CITE АНОТАЦІЯDue to the uniqueness of the services sector in terms of its characteristics and profitability, as well as the lack of studies on this sector, this study is considered to be the first to improve the knowledge of the key factors that play an important role in the profitability of the Jordanian services sector. This study investigates the effect of financial characteristics and capital structure on the profitability of all 46 services companies listed on the Amman Stock Exchange over the period 2014–2018. This study applies fixed and random effects models to panel data variables, namely, size, tangible assets, growth, business risk, debt to equity ratio and debt to assets ratio as independent variables. At the same time, profitability was measured by operating profits (earnings before interest and tax divided by total assets), return on assets (ROA), and return on equity (ROE), which acted as the dependent variables. This study reveals the first evidence that the debt to assets ratio has a negative and significant impact on the profitability of services companies in Jordan. In line with the pecking order theory, this finding suggests that more profitable services companies tend to prioritize the use of retained earnings in financing business activities rather than in financing debt. This study shows that profitability is significantly and positively affected by size and business risk, while ROA is negatively affected by business risk. It also shows that tangible assets have a negative and significant effect on profitability, while growth has a positive and significant effect on operating profits.
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Modelling of capital structure in relation to business performance maximization
Jarmila Horvathova ,
Martina Mokrisova ,
Lucia Dancisinova
doi: http://dx.doi.org/10.21511/imfi.15(2).2018.26
Investment Management and Financial Innovations Volume 15, 2018 Issue #2 pp. 292-304 Views: 2360 Downloads: 387 TO CITE АНОТАЦІЯThe aim of the article was to find out the optimal capital structure of the companies in relation to their maximum performance. To reach this aim, the data of the companies operating in the field of heat industry of the Slovak Republic were used. As the first method, a correlation matrix was applied. It was found out that there is statistically significant relationship between capital structure indicators and performance of the companies. Due to the lack of data in time series, the authors were not able to apply multiple regression model to assess the impact of these indicators on performance. Therefore, a method of modelling was used to analyze the impact of the change in capital structure on performance. Modelling was based on the principle of a gradual change in the capital structure in favor of debt. By the increase in debt, it was confirmed that there was a change in the values of selected indicators. In the course of analysis, it was confirmed that the value of EVA equity increased with the rising indebtedness by which the proposition I of the modified MM theory was supported. The performance expressed by EVA entity indicator is at its minimum when the capital structure is 90:10 in favor of equity. By increasing the debt, EVA entity rises. Based on these results, it can be stated that the performance of selected companies increases when the share of debt also rises, even when taking into account the rising financial risks.
