Chapter 2: Bankruptcy prediction for private firms in developing economies: a
2.4 Results and discussion
2.4.1 Reasons and motives for research
As noted in the reviewed studies, it is imperative to forecast bankruptcy for private firms in developing countries due to a number of reasons. At least 4 of the 12 reviewed studies highlighted that private corporates are the dominant legal forms of firms in developing economies (see Charalambakis and Garrett, 2019; Jacoby, Li and Liu, 2019;
Slefendorfas, 2016; Charalambakis, 2014). Reviewed studies (Charalambakis and Garrett, 2019; Jacoby, Li and Liu, 2019; Charalambakis, 2014 and references therein) and other studies (Pertuz and Perez, 2021; Majukwa, 2019; Foghani, Mahadi and Omar, 2017; Hyder and Lussier, 2016; Chiwara, 2015; Organisation for Economic Co- operation and Development, 2015; Halabi and Lussier, 2014), indicated that private corporations promote economic growth and development, stimulate financial and technological innovations and reduce unemployment rates in developing economies.
Further, a number of studies confirmed a positive impact of new firms, in general, on social, economic and environmental well-being (see, for example, Neumann, 2020 and references therein).
Despite the economic importance of the privately-traded firms, from this review (see, for instance, Papana and Spyridou, 2020; Charalambakis and Garrett, 2019;
Charalambakis, 2014) it is revealed that corporate bankruptcy forecasting literature is largely devoted to publicly-held corporates in developed economies for which data is
54 extensively available, while little is known about the bankruptcy probability drivers for private corporations. Charalambakis and Garrett (2019) and Charalambakis (2014) argued that very little is well-known about the drivers of financial distress probability for privately-traded corporations in undeveloped countries. Some studies (see, for example, Falkenstein, Boral and Carty, 2000) revealed that bankruptcy forecasting models premised on data collected from publicly-traded corporations and applied to privately-owned firms will likely distort real default risk since private companies are unlike public firms. Compared to publicly-held companies, privately-traded firms are exposed to different regulatory and environmental factors. Reviewed studies (see, for example, Altman et al., 2017) and other studies (Faccio et al., 2012; Michaely and Roberts, 2012; Gao, Hartford and Li, 2012; Asker, Farre-Mensa and Ljungqvist, 2012) articulated that compared to public corporations, private corporates are smaller in size, use more leverage, depend more on bank loans and trade credit, invest more and are usually associated with raised costs of borrowing. Therefore, the correlation between accounting ratios and bankruptcy risk varies considerably across public and private firms (Falkenstein, Boral and Carty, 2000).
Further, Slefendorfas (2016) propounded that financial ratios compiled from the financial statements of corporations of different sizes cannot be compared because the prediction results may be biased. Of the reviewed studies, Altman et al. (2017) and Slefendorfas (2016), and other studies, such as Balcaen and Ooghe (2006) and Altman (1968), clearly indicated that financial information for small firms is susceptible to changes, thereby affecting the forecasting power of the designed models. By contrast, Altman et al. (2017), Slefendorfas (2016) and Altman (1968) noted that financial information for large corporates is insensitive to changes and huge corporates are associated with low bankruptcies. This indicates that small and big corporations should not be included in the same samples when creating reliable bankruptcy probability models for privately-owned firms. Using the same line of reasoning, Charalambakis and Garett (2019) and Charalambakis (2014) indicated that the covariates for bankruptcy probability for small and medium private corporations are not the same. Thus, data sets of privately-owned firms should be designed based on firm asset size when forecasting bankruptcy. Since private corporations are smaller than public corporates, it is necessary to design dependable bankruptcy forecasting models specifically for privately-owned firms (Charalambakis and Garrett, 2019). Charalambakis and Garrett (2019) also
55 postulated that these bankruptcy forecasting models help financial institutions design policies linked to the provision and cost of credit to private corporations.
Of the reviewed studies, Eljelly and Mansour (2001) suggested that undeveloped economies are characterised by unique economic characteristics. Papana and Spyridou, (2020) and Eljelly and Mansour (2001) indicated that simple models for predicting corporate failure, such as multiple discriminant analysis, can do a greater job in unsophisticated developing country economic atmospheres than complex corporate failure prediction models developed in advanced countries. Moreover, some studies, such as Obradovic et al. (2018), Waqas and Md-Rus (2018), Liang, Tsai and Wu (2015) and Fedorova, Gilenko and Dovzhenko (2013), highlighted that it is not easy to implement corporate default risk models created for developed countries in developing economies because these countries are dissimilar. Developing economies are usually associated with weak institutional structures such as nonexistence of rule of law and deficiency of property rights which directly affect how corporations deal with financial distress. Waqas and Md-Rus (2018) suggested that it is essential to appreciate that advanced economies have diversified economic configurations and clearly stated bankruptcy laws and procedures, while undeveloped markets are deficient of such diversified economic configurations and bankruptcy laws and procedures. Further, Senbet and Wang (2012) indicated that several developing countries have no effective reorganisation processes for distressed yet viable corporations and the costs of implementing the bankruptcy processes to solve financial distress challenges are exorbitant with regards to time and other resources. Weak institutional structures and undiversified economic configurations in developing economies affect how corporate bankruptcy is forecasted. Compared to advanced countries, developing economies are hard hit by downturn conditions due to their economic structures, thereby exposing more firms from developing markets to bankruptcy risk (Obradovic et al., 2018).
Therefore, Rylov, Shkurkin and Borisova (2016) and Slefendorfas (2016) propounded that applying corporate bankruptcy modelling techniques based on foreign data from advanced markets to developing economies does not always produce credible results because these models do not reflect the institutional, economic and financial features of developing economies.
Although from a regulatory standpoint international financial institutions require to create international bankruptcy models that can be implemented in their branches and
56 subsidiaries to regulate risk through the entire banking groups (Altman et al., 2017), it is well documented that each developing country has its unique characteristics such as financial and economic conditions, accounting legislation and practices, bankruptcy processes, taxes, debtor rights and creditor rights and how those rights are exercised, and investor protection, which affect how bankruptcy probability is analysed (Altman, 2018; Altman et al., 2017; Slefendorfas, 2016; Rim and Roy, 2014; Geiger, Raghunandan and Rama, 2005). This shows that developing countries are not completely the same, they vary a good deal from each other. In particular, procedures of bankruptcy differ significantly across developing economies on issues such as creditor rights and control in the process of bankruptcy (see, for instance, Senbet and Wang, 2012). Thus, formal bankruptcy proceedings vary across developing markets. As a result, expected and unexpected default loss functions, and firm exit, survival and restructuring are influenced in a number of different dimensions (Altman, Resti and Sironi, 2005). These differences partially emanate from the variances in legal origins of economies, which impact the law enforcement and legal rules. Davydenko and Franks (2008) highlighted that huge dissimilarities in the rights of creditors across economies incentivise financial institutions to alter their reorganisation and lending exercises in order to alleviate costly facets of the law of bankruptcy. Therefore, Slefendorfas (2016) articulated that although some bankruptcy models are implemented the world over, researchers continuously attempt to create novel models that can be implemented to firms functioning in specific economies in search of superior models. Reviewed studies (Takahashi, Taques and Basso, 2018; Altman et al., 2017 and Slefendorfas, 2016) and other studies (see, for instance, Altman, 2018) showed that bankruptcy detection models built using economy-specific data are characterised by high forecasting ability.
Reviewed studies (Papana and Spyridou, 2020; Takahashi, Taques and Basso, 2018;
Altman et al., 2017; Slefendorfas, 2016) suggested that the relationship between private firm bankruptcy risk and its determinants changes as, among other things, periods of time, technology, accounting methods, corporate strategies, economic conditions and financial situations change. Slefendorf as (2016) posited that prevalent and universally implemented bankruptcy forecasting models do not indicate shifts in today’s economic conditions, country differences and competition changes and the primary inputs (especially financial information) of such techniques are outdated. The author further proffered that many such models were designed on dissimilar firms. Changes in the relationship between corporate bankruptcy risk and its determinants lead to data
57 instability or non-stationarity problems, leading to reduced forecasting capacity and instability of the created models over time (Papana and Spyridou, 2020; Takahashi, Taques and Basso, 2018). Takahashi, Taques and Basso (2018), Brindescu-Olariu (2016), Singh and Mishra (2016), Smaranda (2014) and Timmermans (2014) showed that corporate bankruptcy detection models need to be frequently revalidated and recalibrated in direct response to changes in circumstances. Further, Singh and Mishra (2016), Timmermans, (2014), Hayden (2011), Eljelly and Mansour (2001) and Grice and Dugan (2001) highlighted that re-designing of bankruptcy models (especially using the most recent data) promises that the forecasting ability of the models does not decline. In support of this, another study, Avenhuis (2013), averred that re-designing a model with specific and larger sample produces superior forecasting ability.