Chapter 2: Bankruptcy prediction for private firms in developing economies: a
2.4 Results and discussion
2.4.2 Emerging trends
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.
58 Generally, these developments show that developing economies are associated with unique features which have significant impact on the bankruptcy risk for privately- traded corporations and are not a favourable environment for credit risk. For instance, high levels of leverage and low profitability levels expose firms in developing countries to both endogenous and exogenous liquidity shocks which raise the probability of bankruptcy. For more expositions on the developments in the most current borrowing splurge in developing economies, the interested reader is referred to Cortina, Didier and Schmukler (2018), Alfaro et al. (2017), International Monetary Fund (2015) and Bank for International Settlements (2014), among other sources.
As noted in the reviewed studies (Papana and Spyridou; 2020; Charalambakis and Garrett, 2019; Charalambakis, 2014; Eljelly and Mansour, 2001), the creation of bankruptcy forecasting models for private corporations in developing countries is a challenging task because of the scarcity of default data and information for private corporations. Limited default data and information for privately-traded corporates is predominantly credited to the fact that in most developing countries, private firms are not mandated to publicise their financial statements. Rylov, Shkurkin and Borisova (2016) indicated that corporate bankruptcy forecasting literature is limited in developing economies since they are characterised by limited historical statistical data, closed markets, institutional and legal barriers and reduced predictive power of market signals.
Thus, there is lack of dependable databases for private corporate default data and information. However, of late, as noted in reviewed studies (Papana and Spyridou, 2020; Charalambakis and Garrett, 2019; Takahashi, Taques and Basso, 2018; Altman et al., 2017; Slefendorfas, 2016; Charalabakis, 2014), it has emerged that slightly more effort has been put in private firm bankruptcy detection following the significant expansion of private firm loan business, occurrence of the 2007 – 2008 global crisis, the increase in the availability of massive databases for private firm default data and information such as ICAP, ORBIS and Capital IQ® databases, the evolution of statistical techniques that promote dynamic modelling of credit risk and the introduction of Basel II/III principles.
Further review of the literature discovers that even though in several countries private firms are not obligated by rules and regulations to produce audited financial statements and records, some private corporates do (see Range, Njeru and Waititu, 2018; Rim and Roy, 2014; Eljelly and Mansour, 2001). Thus, in some instances, private firm
59 bankruptcy risk is analysed using audited financial statements. Audited financial reports promise that there are no substantial misstatements or mistakes in the reported results (Bratten et al., 2013; Minnis, 2011). Bratten et al. (2013) and Dechow, Ge and Schrand (2010) proffered the argument that by plummeting the misrepresentations of financial reports, a reliable audit warranties dependable financial reporting. Moreover, to guarantee or augment the audit services’ quality in order to re-establish public confidence in the audit function, the audit market is exposed to continuing regulations, e.g., the 8th Directive in the European Union and the Sarbanes Oxley Act in the United States (see Widmann, Follert and Matthias Wolz, 2020, Pott, Mock and Watrin, 2009 and references therein). The link between audited financial statements and the bankruptcy risk for privately-traded corporations has not been clearly dealt with by the reviewed studies, hence the existent extensive literature is used to debate it. Several authors proposed that private firms with audited financial reports are characterised by lower bankruptcy risks than their unaudited counterparts in the eyes of the providers of finance (Cenciarelli, Greco and Allegrin, 2018; Hamzani and Achmad, 2018; Gul, Zhou and Zhu, 2013; Jahur and Quadir, 2012; Blackwell, Noland and Winters, 1998).
Cenciarelli, Greco and Allegrin (2018) suggested that huge auditing companies reduce the likelihood of bankruptcy risk because they have the resources, skills and knowledge about a particular sector and its firms and can assess internal control systems of a firm, gauge its earnings against the sector average and efficiently examine discount rates and cash flow estimates. Rim and Roy (2014) indicated that, of late, some financial institutions have been demanding audited financial statements from the private firms before granting them loans.
As noted in almost all of the reviewed studies, several bankruptcy prediction models for privately-held corporates in developing markets are based on accounting ratios.
Nevertheless, although reviewed studies did not look on the comparative forecasting ability of these accounting-based bankruptcy models, some studies highlighted that the predictive capacity of standard accounting-based bankruptcy detection models has been declining over the years (Beaver, Correia and McNichols, 2012; Beaver, McNichols and Rhie, 2005). Further, trust in the practices of auditing and firm reporting has swiftly dwindled of late due to numerous significant accounting scandals in both developed and developing economies (see, for instance, Widmann, Follert and Matthias Wolz, 2020, Pott, Mock and Watrin, 2009 and references therein). Hence, some studies such as Beaver, Correia and McNichols (2012) and Agarwal and Taffler (2008) proposed that
60 supplementary predictor variables need to be incorporated into the bankruptcy forecasting models. From the reviewed studies (Charalambakis and Garrett, 2019;
Altman et al., 2017; Charalambakis, 2014), it has been observed that, over the recent years, the predictive capacity of private firm bankruptcy models has been being enhanced by combining financial ratios with firm and loan characteristics, industry effects and macroeconomic variables as the predictor variables. Moreover, reviewed studies (Charalambakis and Garett, 2019; Takahashi, Taques and Basso, 2018;
Charalambakis, 2014) posited that to determine private firm bankruptcy probability under downturn conditions accurately, macroeconomic conditions usually captured by macroeconomic variables must be incorporated into the models. This issue is supported by other studies such as Canals-Cerda and Kerr (2015a, 2015b). Takahashi, Taques and Basso (2018) proposed that bankruptcy models that take into account periods with economic crises and times characterised by greater stability may produce highly representative results as far as bankruptcy probability is concerned. Captivatingly, since the outburst of the 2007 – 2008 universal financial and economic crisis, the forecasting of bankruptcy probability for privately-traded corporations under downturn conditions has been receiving a lot of scientific and academic research attention.
Moreover, Gupta, Gregoriou and Healy (2015) and Altman, Sabato and Wilson (2010) revealed that non-financial variables enhance the forecasting power of bankruptcy prediction models. Altman (2018) suggested that to instantly take into account shifts in the creditworthiness of corporate and individual borrowers, most recent breakthroughs in financial technology attempt to investigate the application of non-traditional measures and big data such as payable history, analysis of invoices and governance characteristics; “clicks” on adverse information episodes and data; and inputs from the social media.