Chapter 3: Corporate default risk modelling under distressed economic and
3.1 Introduction
In developing countries, financial institutions often struggle to develop the formal credit evaluation procedures that are widely used in advanced economies. Challenges include data scarcity, imperfect and shallow financial markets, insufficient technical capacity and lack of extensive penetration by both local and external rating firms (see, for example, Ozili, 2019). Not surprisingly, the use of data-intensive dynamic default forecasting models in its infancy in several developing economies due to limited data.
Dynamic default prediction models emphasise the importance of duration effects.
The bulk of research has focused on the default detection models for public firms in developed economies due to the wider availability of reliable and comprehensive data (see, for instance, Charalambakis; Garrett, 2016 and Shumway, 2001). Research work on the drivers of default probability for privately-held companies is generally limited, and that for such companies in developing economies is even more limited. The literature on private firm default prediction modelling is chiefly focused on developed nations (see, for instance, Bauweraerts, 2016; Falkenstein, Boral and Carty, 2000).
However, it has emerged that applying models constructed for developed economies to emerging markets does not always lead to granting of appropriate credit (Ashraf, Felix and Serrasqueiro, 2019; Rylov, Shkurkin and Borisova, 2016), because the economic structures of these countries are significantly different (Rylov, Shkurkin and Borisova, 2016; Fedorova, Gilenko and Dovzhenko, 2013). In the same vein, Altman (2018), Takahashi, Taques and Basso (2018) and Slefendorfas (2016) indicated that each economy has its unique features, and thus models developed specifically for individual countries outperform universal models.
The study contributes to the discourse of default risk modelling by analysing the drivers of default probability for private firms under distressed economic and financial conditions in a developing economy (Zimbabwe). Credit default prediction for private firms in developing economies is crucial for a number of reasons and has acted as motivation for several academic research papers (see, for example, Charalambakis and Garrett, 2019; Slefendorfas, 2016; Charalambakis, 2014). Private corporations are dominant firms in developing economies (Charalambakis and Garrett, 2019;
69 Slefendorfas, 2016; Organisation for Economic Co-operation and Development, 2015;
Charalambakis, 2014), perhaps because developing economies are associated with underdeveloped local equity markets, which limits the use of price-based information (such as distance to default) in default prediction. Also, private firms promote economic growth and development and financial and technological innovations, and they reduce unemployment rates (Charalambakis and Garrett, 2019; Hyder and Lussier, 2016;
Majoni, Matunhu and Chaderopa, 2016; Chiwara, 2015; Organisation for Economic Co- operation and Development, 2015; Halabi and Lussier, 2014; Wymenga et al., 2012).
Given firms’ economic contribution, Bauweraerts (2016) and Eckbo, Thorburn and Wang (2014) announced that their failure is associated with high social and economic costs.
Privately-traded corporates are unlike publicly owned companies; they are smaller in size, use more leverage, depend more on bank loans and trade credit, invest more and are associated with high costs of borrowing (Asker, Farre-Mensa and Ljungqvist, 2012;
Gao, Hartford and Li, 2012; Michaely and Roberts, 2012). Charalambakis and Garrett (2019) reported that the non-stock-market variables that drive default probability for private and public firms are not the same. In the same vein, Falkenstein, Boral and Carty (2000) observed that the correlation between default risk and financial variables varies considerably across listed and private corporates. Moreover, public and private firms are affected by different regulatory and environmental variables. Falkenstein, Boral and Carty (2000) suggested that the importance of this observation is that default prediction techniques based on data gathered from public firms and applied to private corporates will likely distort real default risk.
Gathering default information and data for private firms is challenging since their stocks are not bought and sold on stock exchanges (Charalambakis and Garrett, 2019). This indicates that the records and financial statements of corporate borrowers accessed from banks are the primary sources of default data and information for private companies.
Therefore, predicting the probability of default for private firms is vital because it helps banks generate policies related to the supply of credit to and the cost of credit to private firms (Charalambakis and Garrett, 2019). Moreover, evidence on the predictive performance of private firm default prediction models sheds more light on the capability of financial ratios to predict firm default.
The default probability is influenced by general macroeconomic conditions. Obradovic et al. (2018) and Canals-Cerda and Kerr (2015a) revealed that economic downturns are
70 associated with high default frequencies. Several authors (see, for example, Charalambakis and Garrett, 2019) have suggested that the inclusion of macroeconomic factors improves the forecasting results of private firm default prediction models.
However, the dilemma is that there is lack of industry consensus on which macroeconomic factors have the most substantial impact on private firm default risk under downturn conditions, resulting in the introduction of diverse probability of default prediction methodologies.
This study proposes stepwise logistic regression models based on diverse combinations of loan and firm characteristics, financial ratios and macroeconomic factors, and uses the stepwise selection of some threshold criteria to predict default for Zimbabwean privately-owned non-financial firms under downturn conditions twelve months in advance. The study focuses on a twelve-month period because it permits financial institutions to take corrective action to avoid forecasted defaults and ensures that timely data is incorporated into the rating techniques (Hayden 2011; Basel Committee on Banking Supervision 1999). The primary focus of this analysis is on the economic interpretation of the estimated coefficients for the predictor variables incorporated into the designed models. To fit the models, the study adopts a unique cross-sectional data set of defaulted and non-defaulted private firm loan accounts accessed from an anonymised major Zimbabwean commercial bank over the observation period from 2010 to 2018. Geographically, the data set is an accurate representation of the Zimbabwean market.
Zimbabwe provides an exciting and challenging case in analysing default risk for private firms in developing countries. Over the past two decades Zimbabwe has experienced severe and prolonged distressed economic and financial conditions that have contributed to substantial deindustrialisation and informalisation of the economy.
In order to stabilise the economy, the country phased out the Zimbabwean dollar in 2009 and adopted a basket of currencies that included the euro, South African rand, British pound, Botswana pula, and United States (US) dollar. However, the US dollar emerged as the presentation and functional currency of firms. Masiyandima et al. (2018) posited that the emergence of the US dollar as the main currency resulted in negative and low rates of inflation, which negatively impacted the country's growth. The economy witnessed 28 successive months of deflation from October 2014 to January 2017 (Masiyandima et al., 2018). World Bank Group (2020a) indicated that the real
71 gross domestic product (GDP) growth rate fell from more than 10% per year in 2010 - 2012 to an average of 2.5% between 2013 and 2018, while public debt and budget balance were on average 43.68% of GDP and -4.07% of GDP, respectively, during the observation period. The downturn conditions witnessed in Zimbabwe are rarely found in developed countries or even in some developing nations.
On April 17, 2008, the Zimbabwean government enacted the Indigenisation and Economic Empowerment Act into law to empower indigenous people. Section 3(1) of the Act obliges foreign-owned commercial businesses with an asset value of at least USD 500 000 to cede 51% or more of their shares to indigenous Zimbabweans. Hence, most private firms are owned by the indigenous Zimbabweans, most of whom have deficient or unfitting industry experience and limited managerial skills. The implementation of the Act led to a fall in investor confidence, a reduction in aggregate demand for local goods and services, a decline in firm performance, and deterioration in economic growth, resulting in the failure of several private firms. To avoid massive closures of local firms, the Zimbabwean government launched “Buy Zimbabwe” and
“Make Local Buy Local” campaigns promoting home-grown goods and services, and banned certain imports. The resulting increased demand for local goods and services has exposed several private firms to episodes of rapid growth. Given that Zimbabwean private firms are often undercapitalised, rapid growth has been financed through debt.
Various local owners have failed to cope with the management challenges that come with rapid growth, leading to high default probabilities. Further, due to incessant viability problems, most private firms that previously exported have either ceased to export, as a way of downsizing, or gone out of business entirely.
Many Zimbabwean private firms have found it difficult to access formal credit from financial institutions because of their high indebtedness and questionable creditworthiness, resulting in firms substituting bank credit with trade credit. Generally, compared to Zimbabwean public firms, Zimbabwean private firms use more leverage, depend more on bank loans and trade credit, are associated with high borrowing costs, invest more, and are smaller in size. Being a smaller size implies limited management skills and less diversification, which indicates more vulnerability to idiosyncratic shocks. In resolving corporate financial distress issues, there is a clear legal structure in Zimbabwe. Distressed companies can apply to the courts either for liquidation or to be placed under judicial management. The Insolvency Act guides liquidation and judicial
72 management. Hence, the Insolvency Act indirectly influences the default probability for private non-financial firms in a number of diverse dimensions.
The Reserve Bank of Zimbabwe (RBZ) is the principal institution that regulates and supervises the Zimbabwean banking sector, which is dominated by commercial banks in terms of total deposits, total assets and total loans and advances (Reserve Bank of Zimbabwe, 2018). Ownership of commercial banks is spread between the government, foreigners and local individuals and corporates. Defaults are regarded as in-house information of the financial institutions and are indicated only for the customers of the financial institutions. In order to align themselves with international regulatory standards, Zimbabwean banks are implementing Basel II guidelines, spearheaded by the RBZ. However, several banks, mainly local and government-owned banks, do not have sufficient technical capacity to implement Basel II/III, mainly due to the economic challenges that have bedevilled the economy.
This study shows that accounting information is useful in differentiating between defaulted and non-defaulted private firms under distressed financial and economic conditions. The experiment offers evidence indicating that a model that incorporates five accounting ratios combined with one firm characteristic and two macroeconomic variables best explains the likelihood that a Zimbabwean private firm will default. This model has an in-sample classification rate of 98.40%. In particular, the study finds a negative effect on default probability using the earnings before interest and tax/total assets ratio, the ratio of (current assets-current liabilities)/total assets, the age of the firm, the real GDP growth rate and the inflation rate, and a positive effect on default probability using the bank debt/total assets, earnings before interest and tax/total liabilities and accounts receivable/net sales ratios. The financial ratios are important because they denote some of the most imperative credit risk factors, namely, profitability, leverage, activity and liquidity (see, for instance, Charalambakis and Garrett, 2019 and Hayden, 2011). The research work also shows that the forecasting results of default prediction models are improved by incorporating macroeconomic variables. This finding is consistent with the discovery of Sheikh and Yahya (2015) and Hill, Perry and Andes (2011), who posited that the forecasting results of bankruptcy prediction models are improved by including macroeconomic variables.
The rest of the chapter is organised as follows. In section 3.2, a brief overview of the methodology is outlined. Section 3.3 is allocated to sample and data discussion, and
73 section 3.4 states experimental results. Finally, section 3.5 presents conclusions and potential directions for future research.