“Family businesses and predictability of financial strength: a Hungarian study”

The aim of this study is to examine how bankruptcy prediction models forecast finan- cial 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.


INTRODUCTION
Family businesses -their entrepreneurial values, personal leadership, and the name of the family with the intent of succession -are more oriented towards financial sustainability than non-family businesses. Those family business owners, who have sufficient management skills and know-how (as entrepreneurs) and also possess high creativity and innovation (as owner-managers), are likely to have a greater chance of survival and success (Chandler & Sági, 2018).
Family businesses struggle to survive in turbulent economic times, and thus bankruptcy prediction becomes increasingly important (Sági & Nikulin, 2017; Shkolnyk, Pisula, Loboda, & Nebaba, 2019). The bankruptcy of a family business does not only mean the end of the company's activity, but also the end of a family legacy, not to mention the knock-on effect for company stakeholders. The far-reaching effects of bankruptcy, if occurring on a large scale, can affect communities and even society as a whole, and can have far reaching constraints on governing bodies as well (Lentner & Kolozsi, 2019). The family business setup in Hungary and its development since the last decade of the previous (socialist) political regime, and mainly from the beginning of the regime change, attach great importance to this study. Family businesses have constantly been highlighted by Hungarian governmental policies as seeds of the market economy, promoting growth, not only generating substantial income for families, but also creating workplaces for others (Lentner, 2020). This study examines the financial perspectives and operational riskiness of family businesses by evaluating recent financial data and predicting their survival.
Predictive models have been developed as attempts to minimize losses or prevent bankruptcy as quickly as possible (Wu, Gaunt, & Gray, 2010). In this study, three such models are compared: the revised Altman Z-score; the IN05 Quick Test; and a new Bankruptcy Index. Quantitative data based on past and present financial data of Hungarian companies, alongside qualitative information about their size and ownership, have been compiled in the analysis.

LITERATURE REVIEW
Although many small businesses fail in the early years, this does not always apply to small businesses. Amann and Jaussaud (2012) investigated both non-family and family firms and found that, during an economic downturn, family businesses were more resilient both during and after an economic crisis when compared to non-family businesses. Moreover, they resist the downturn better, recover faster, and continue this trend of higher performance afterwards. Many studies on bankruptcies or failure in family businesses tend to focus on this resilience and reasons for survival, rather than question the likelihood of failure or financial health. As such, no empirical studies were found regarding the predicting bankruptcy in family businesses. Therefore, the following sub-section gives an overview of empirical studies that have compared predictive models, but not necessarily for family businesses.

Empirical studies on predictive modeling
Given an applicable combination of accounting ratios, bankruptcy indices can be built to predict whether a company generates added value to its owners in the long run, losing the invested capital, or stays in an unpredictable grey zone (Beaver, 1966;Edmister, 1972;Blum, 1974;Ohlson, 1980). These models are run with an approximate of 95-99 per cent of certainty (however, these model certainties are changing with time, see Altman, 2000).
The Altman Z-score tests a company's credit-strength, and this in turn indicates the likelihood of bankruptcy. It is based on five financial ra-tios (using the F-test), which are calculated using the data from a company's annual report. These five ratios provide data on profitability, leverage, liquidity, solvency and activity, which in turn can serve as indicators of potential bankruptcy with the use of a discriminate analysis. This study will use the revised model, which will be elaborated further in the Methodology section, along with the formulae for each model. Altman (1968)  In summary, there is a lack of research on the predictive modeling of bankruptcies and financial health in family businesses, especially when the focus is on the type of ownership. Existing studies can be grouped into three areas that relate to the predictive power of models for bankruptcies (Beaver, 1966

AIMS
The aim of the paper is to test on a robust sample whether family businesses work better than non-family businesses, when survival prospects (predictions for bankruptcy) are modelled, and to consider the accuracy of these models.

Operational definitions and assumptions
This study examines the predictive models for bankruptcy, and the precursor to this that financial health is a function of a company's credibility and financial added value to shareholders (Korom & Sági, 2005). The study assumes that declining financial health is an indicator of bankruptcy, all other things being equal. It is also assumed that growth and decline are linear processes that can thus be predicting based upon selected financial data used in company reports.
The research question relates to the behavior of family businesses. Their defining characteristics involve three key elements. Firstly, decision-making rights, on the whole, are held by either the founders, a buyer of share capital in the family business, or owned by family relatives that constitute direct heirs. Secondly, at least one family representative is formally involved in the governance of the firm. Thirdly, for limited companies, the founder, or family relatives, own 25 per cent of the decision-making rights.
The variable of company size has also been included in tests. SMEs have been defined by the European Commission as: less than 250 persons employed, a maximum annual turnover of EUR 50 million, or a balance sheet total of no more than EUR 43 million 1 ; whereas large companies are in excess of these limits. According to this sample, within SMEs, the small-size enterprises dominate among Hungarian enterprises, by 89.9 per cent out of the total. For family businesses, the ratio of small enterprises stands at 92.1 per cent. These facts underline the importance of measuring the bankruptcy risks for family businesses, considering that the living conditions of families are at stake. These criteria will be used for the sample and will be detailed further in the following section.

Methodology
Statistical models can be used to derive a selection and weighting of creditworthiness factors, and, thus, optimize accuracy when categorizing companies into solvent or insolvent. In the linear multiple discriminant analysis, the use of a weighted linear combination of indicators ensures that solvency cases are optimally classified, based the discriminant score : D 1 http://ec.europa.eu/eurostat/web/structural-business-statistics/structural-business-statistics/sme In the above equation, n refers to the number of financial indicators included in the bankruptcy prediction model, i K refers to the specific indicator value, and a stands for each indicator's coefficient within the bankruptcy prediction model.
As with discriminant analysis, regression models model the dependence of a binary variable on other independent variables. When this general definition is applied to bankruptcy prediction models, certain creditworthiness characteristics (independent variables) can be used to classify borrowers as solvent or insolvent (a dependent binary variable). The combination of nonlinear model functions and the maximum likelihood method results in the potential for regression models to calculate membership probabilities. In this way, default probabilities can be directly ascertained (OeNB, 2004).
When selecting the bankruptcy risk models, the aim was to consider only those which can be applied to family businesses and do not make restrictions concerning publicly quoted shares or the availability of market capitalization (see, for example, Scott, 1981;and Wu et al., 2010). According to the applicability of bankruptcy models to companies operating in smaller EU countries in latest years of the crisis, the highest relevance was found for the Altman Z-score and the IN05 Quick Test (see Bohdalová & Klempaiová, 2017;Dolejšová, 2015). This section details the method of using the models, or, in some cases, adapted for this study along with further information on the data sample.

Altman Z-score model
Altman pioneered the use of the multifactor discriminant analysis to predict corporate bankruptcy (Altman, 1968 where WC TA is the ratio of net working capital to total assets, RE TA is the ratio of retained earnings to total assets, EBIT TA is the ratio of earnings before interest and taxes to total assets, ED is the ratio of the accounting value of equity to total liabilities, and S TA is the ratio of sales to total assets.
In the model interpretation, companies with the Altman Z-score less than 1.23 are in danger of bankruptcy, while those above 2.9 are assumed to be financially healthy. The interval of 1.23 and 2.9 is considered a grey area concerning the company's financial soundness and survival.

IN05 Quick Test
The where TA TL is the ratio of total assets to total liabilities, EBIT I is the ratio of earnings before interest and taxes to interest, EBIT TA is the ratio of earnings before interest and taxes to total assets, OR TA is the ratio of operating revenue to total assets, and CA CL is the ratio of current assets to current liabilities.
For IN05 Quick Test values < 0.9, the company does not create value for its owners or may even destroy value, for IN05 values > 1.6, the company creates new value for its owners, and for values falling within a range of 1.6 > IN05 > 0.9, the results are inconclusive (a grey area).
The IN05 model has been designed for companies operating in one of the Central European countries, which is more relevant in the rather similar context of business environment of Hungarian companies.

Modified Bankruptcy Index
To examine Hungarian companies, the Bankruptcy Index of Karas and Režňáková (2015) was modified by substituting the element of the value of total as-sets in the formula to an asset-based solvency ratio (as the latter is more applicable according to, for example, Thornhill and Amit, 2003). Also, the coefficients for the indicators were refined through a blend of linear discriminant analysis and Box-Cox transformation of variables (see Zmijewski, 1984).
The modified Bankruptcy Index is as follows: where 1 X is the total assets turnover ratio (ratio of sales to total assets), 2 X is the ratio of quick assets (current assets minus inventories) to sales, and 3 X is the invert of the asset-based solvency ratio (total assets to total liabilities).
A company is evaluated as of high risk of bankruptcy if the index is lower than 7, in grey zone from 7 to 9, otherwise it is evaluated as of low risk of bankruptcy.

Data source
Data from Amadeus (Analyse Major Databases from European Sources) has been utilized in this study. The database allows its users to specify formulas for a given set of variables, and then run tests accordingly. Hungarian companies' data between 2016 and 2018 was examined. Out of the total 462,200 active companies at the end of 2018, there were 339,305 ones with family ownership (in comparison to 122,895 other companies). To select family owned companies, expert additional data from the Hungarian Chamber of Auditors was used. The employed descriptive variables were: company owners (family or non-family), and the category of each company by size.
Following the working definition of family businesses referred to earlier in this paper, the European Commission criteria were examined for each business. Only companies that met these criteria were marked and selected for analysis. The categories of companies by size were: • Very large (Operating revenue more than or equal to 100 million EUR; Total assets more than 200 million EUR; Employees more than or equal to 1,000 listed) and large companies (Operating revenue more than or equal to 10 million EUR; Total assets more than or equal to 20 million EUR; Employees more than or equal to 150); • Medium-sized companies (Operating revenue more than or equal to 1 million EUR; Total assets more than or equal to 2 million EUR; Employees more than or equal to 15; and they are not very large or large); and • Small companies (those not included in any other category).
Keasey and Watson (1991) outlined the importance of appropriateness of sample selection methods, therefore, the companies were examined by their ownership and size for all three models.

RESULTS
Three models that are tested will be listed here consecutively. For each model, the total data sam-ple is presented first, followed by the scores for family-owned and other companies grouped according to size. Tables 1 and 2 show the bankruptcy test results for Altman Z-scores.
In case of the IN05 model, three out of the five elements of the index refer directly to earnings generating capability of the companies. The overall results are also more positive for family businesses than non-family businesses in case of this test (Tables 3 and 4), especially for small and medium-sized companies.
The last sets of Tables 5 and 6 are based on the third model, the modified Bankruptcy Index. This index was developed to demonstrate the added value (in terms of operating cash flow) relative to the assets invested to the company. According to the tests, the distribution of the overall Hungarian companies by size is quite similar to the ones of the Altman Z and the IN05, except that the middle range (the grey zone) was narrowed to enhance the model predictability. In case of ownership differentiation, family businesses work better than average.       According to Altman Z-score, family businesses perform better with predictability of survival.
Accordingly, their ability to adapt to crisis financial constraints (the retreat of bank credits giving ground for the accounts suppliers' funds) is more promising. However, the results for this model indicate high bankruptcy predictions in case of Hungarian (in part, family-owned) companies. These results are supported by Cimpoeru (2014) who emphasized that the crisis had more substantial regressive effects for emerging economies in Central and Eastern Europe than elsewhere. This phenomenon is coupled with the restructuring of external financing, whereas the traditional bank credit resources have been mostly substituted by account suppliers' funds. The Altman Z-score is therefore impeded by a superfluous high weighting on net working capital (i.e., accounts suppliers).
When family businesses reached medium size (in terms of employment, asset value and operating revenue), they are predicted to be more financially stable than non-family businesses. In today's business environment, the cash-flow based indices have proved to be more reliable for measuring financial stability and added values. In this respect, IN05 predicted better chances for medium-sized family businesses in generating cash flows.
Finally, the proposed Bankruptcy Index is more focused on the companies' cash flow generating capability (added value) relative to the asset values invested. According to this measurement of relative efficiency, the advance of family businesses is even more substantial.

Practical implications
By analyzing and comparing the financial strength predictions of family businesses, the informative values of these models have been discovered for family business owners in general and family business owners and stakeholders in particular in Hungary. It is concluded that the strong reliance of family businesses on the entrepreneurial values, personal leadership and the name of the family implies a strong supporting element in the survival of these firms. Also, the meaningful intent of succession in family businesses drives these firms towards financial sustainability over longer periods of time.
The comparison of models in the Hungarian context complements existing studies on the creditworthiness and bankruptcy models and highlights the importance of the model choice and its associated impact on projected outcomes; as can be seen, the weightings and focuses of the indices produce very different outcomes, some of which are much more positive than others, or project a more positive outlook on certain sizes of family businesses. The findings suggest that, based on their stage in the growth cycle, they may have better predictions, e.g., greater cash flows during the growth stage rather than introduction stage means automatic strong financial health. The results may suggest this statement, however, there is space for further research in this matter.
The study has also led to a reconsideration of the concept of failure and financial health. McMillan and Overall (2017) argue that the concept of growth and decline of organizations may be less linear, which gives us an argument in favor of greater complexity in linear modeling. Figure 1 shows the three types of failures in relation to the organization's 'health'. Figure 1 shows that predictability based on purely a linear temporal process may only apply to one classical type of failure. If, for example, complex failures are caused by structural rigidities Classic growth/decline Failure in good peak performance and intelligence pathologies, there is no reason to assume that this type of failure can also occur in family businesses. Likewise, the peak performance achieved in a born global family business, followed by a subsequent slow decline, cannot be factored into predictive models, and therefore bankruptcy is predicted much earlier than it actually happens.

CONCLUSION
Given the complexity of factors leading to bankruptcy found in the literature review, there is an apparent need to apply at least two or three different bankruptcy models to properly assess the financial performance of the eligible companies. Since these models indicate probable financial stress to companies, as well as the signaling effect to lenders and the shareholders (to providers of external funds), they are crucial for predictions for family businesses.
In the context of family businesses, it is argued that factors need to be included regarding the companies' cash flow generating capability (value added) relative to the asset values invested, as reflected in their adapted Bankruptcy Index, as this measure has a strong signaling affect for external providers of funds. In case of the Index deterioration, creditors and shareholders receive a signal that warns them to take preventive steps to recover the cash flow generating capability of family businesses. These signals are crucial in connection with the current crisis of 2020, as a sudden decline in purchasing power of households alongside with their climbing indebtedness threatens the outlook for businesses. Unfortunately, the financial awareness of Hungarian private agents is rather low (Sági and Lentner, 2019), which may require policy measures to promote the sound operation of enterprises. The proposed Bankruptcy Index is more understandable in relation to signaling, as the "grey zone" is narrower than in previous models.
Finally, this study highlights the specific nature of financial health and failure of family businesses and necessitates the adaptation of existing predictive models, as done in the third model. Besides, it is concluded that at least two or three different bankruptcy models should be used and adapted to suit family businesses. All of three models' test results gave warning signals about the likelihood of failures, in case of the Bankruptcy Index for 41.6 per cent of the total number of Hungarian companies and for 35.9 per cent of Hungarian family businesses, based on financial indicators for 2018. Given that that family businesses compose the backbone of the Hungarian economy, these results are also associated with macroeconomic risks. Due to the Hungarian enterprises' reliance on external funding, the current crisis that has emerged with the pandemic will probably damage the liquidity positions of most small enterprises. (In previous years, foreign currency loan exposures have caused financial vulnerability to family businesses, which was then partially mitigated by policy interventions, see Matolcsy, 2016.) The results show that social tensions can arise from the mass insolvency of Hungarian family businesses, so that policy actions will be needed in the coming years.

RESEARCH LIMITATIONS AND FUTURE DIRECTIONS
This study provides insight into the predictive financial health of family businesses using three different models. However, since there are more predictive models to be tested, this can be seen as a potential extension of this study. Recent developments regarding how failure is seen as a less linear process may also prompt researchers to consider more complexities when developing predictive models. Furthermore, in the case of family businesses, as found in the literature, they are more resistant to failure than non-family firms (especially from a financial perspective), and this should be taken into account in predictive models as a 'familiness factor'.