4.1. Models with Firm Characteristics and Account Features
Table 3 below outlines the designed models based on firm and loan characteristics.
The experimental results of this study indicate that all covariates included in models 1(a)–(c) are significantly connected to the RRs for defaulted Zimbabwean private non-financial firm bank loans, with the total debt/total assets ratio, the EAD, the loan amount, the number of creditors, time with the bank, loan maturity period and the length of the workout process having a negative influence on the RRs, and the earnings before interest and tax/total assets ratio, the ratio of (current assets-current liabilities)/total assets, the age of the firm, the collateral value, age of loan at default and the total assets having a positive influence on the RRs.
We observe that the firm age has a positive association with the RRs, reflecting that established and mature firms are associated with high RRs while young and adolescent corporates are linked to low RRs. This is credited to the point that, compared to young and adolescent corporations, mature and established corporates have the experience, expertise and ability to effectively and efficiently deal with the recovery processes, thereby increasing the RRs. In support of this finding,
Chalupka and Kopecsni (
2009) and
Dermine and De Carvalho (
2006) posited that the firm’s age positively influences the RRs. Conversely,
Khieu et al. (
2012) reported no association between RRs and the age of the firm.
The total assets book value, which is a proxy of corporate size, has a positive sign as expected, indicating that big firms are associated with high RRs and small corporations are characterised by low RRs. This proposition agrees with the research findings of
Francois (
2019) and
Jacobs et al. (
2010).
Wang et al. (
2020) found that huge corporates are characterised by improved RRs during good times. On the one hand, large, defaulted Zimbabwean privately owned non-financial firms are associated with high RRs due to the following reasons. They can efficiently and effectively steer a default and be transformed due to several aspects linked to size such as market power and government support. Moreover, they offer less severe information asymmetry difficulties to banks. As a result, their reorganisation procedures happen more rapidly than those for smaller firms. On the other hand, small Zimbabwean private corporations are more impacted by the domestic economy conditions compared to big firms and they lack the capability to service distressed loans, leading to low RRs. Contrastingly,
Chalupka and Kopecsni (
2009) proposed that if a huge firm defaults, several creditors compete for its assets, resulting in a low RR for the bank.
Wang et al. (
2020) articulated that huge corporations may be associated with greater bankruptcy costs, leading to lesser RRs especially under bad times. Moreover,
Khieu et al. (
2012) found no substantial association between RRs and the firm size.
We discover that the loan amount has a negative sign as expected, indicating that RRs decrease as the loan amounts increase. This shows that huge loans are associated with low RRs and small loans are linked to high RRs. Given that several recovery costs are semi-fixed, this finding is not surprising. Hence, the bigger the loan, the greater the recovery costs (making it difficult for firms to repay huge loans) and the lower the RRs. This finding is supported by
Dermine and De Carvalho (
2006) and
Felsovalyi and Hurt (
1998).
Wang et al. (
2020) highlighted that the loan size is negatively related to the RRs whether the credit cycle is good or bad. Further,
Dermine and De Carvalho (
2006) posited that usually a bank defers foreclosure on huge credit facilities since some bank customers who have business relations with a client with a huge loan in default would be negatively impacted by the foreclosure and end up in default on their loans as well. The authors (
Dermine and De Carvalho 2006) promulgated that this potential “spill-over effect” leads to lower RRs when these huge credit facilities finally go in foreclosure. On the other hand, literature has indicated that financial institutions put more effort and resources into analysing the creditworthiness of borrowers with huge loans and monitoring them, resulting in higher RRs.
Acharya et al. (
2007) noted that the huge creditors’ bargaining power may escalate RRs for enormous debts. Further,
Khieu et al. (
2012) and
Thorburn (
2000) reported that there is no relationship between RRs and the size of the debt facility.
As expected, the collateral value has a significant positive sign, indicating that private firm borrowers with high-value collateral security are more easily recoverable than those with low-value collateral security. The economic import of this is that losses are less likely to happen if the collateral values are high since banks have a legitimate right to take hold of and sell particular assets pledged as security in case of default. Direct collateral realisation generates direct proceeds to the bank.
Gurtler and Hibbeln (
2013),
Khieu et al. (
2012),
Qi and Yang (
2009),
Grunert and Weber (
2009),
Chalupka and Kopecsni (
2009),
Araten et al. (
2004) and
Van de Castle and Keisman (
1999) supported the positive relationship between collateral value and RRs.
The experimental outcomes reveal that the EAD has negative coefficients as expected, indicating that the greater the EAD, the lower the RRs. Fundamentally, the EAD indicates the proportion of the loan that is yet to be paid after default happens. It is essential to note that Zimbabwean privately-owned firms have been facing perennial viability problems for long. Consequently, huge outstanding loan balances are more challenging for the Zimbabwean private firm obligors to pay back under economic and financial stress. If a significant part of the borrowed amount is not repaid before the occurrence of default, the RR will be low and if a significant portion of the borrowed amount is reimbursed before the default, the RR will be great. This finding indicates that obligors with huge EAD expose the banks to more losses.
Bellotti and Crook (
2012),
Bastos (
2010),
Chalupka and Kopecsni (
2009) and
Felsovalyi and Hurt (
1998) supported this proposition. Nevertheless, this assertion is not consistent with the findings of
Tanoue et al. (
2017) and
Tong et al. (
2013).
Tanoue et al. (
2017) suggested that the EAD positively influences the recovery probability, with a significant EAD resulting in a high recovery probability. A possible explanation for this is that banks strengthen their recovery efforts if the EAD is greater. Moreover,
Tanoue et al. (
2017) proposed that although a significant EAD results in a high recovery probability and makes the probability of incurring a loss low, obligors with huge EAD are likely to give rise to a loss.
This current study confirms that the workout process’s length has a substantial adverse influence on the RRs, indicating that the RRs fall as the workout period escalates and the RRs rise as the workout period shortens. Generally, loan amounts increase with the workout period, especially for longer time horizons, since the costs from expenses and forgone interest increase significantly as the workout period gets longer. Moreover, write-offs increase dramatically as the workout period increases. The distressed economic and financial conditions in Zimbabwe over the observation period have led to elongated and frail workout processes, resulting in low RRs. Several studies have confirmed that extended workout process results in low RRs (
Tanoue et al. 2017;
Betz et al. 2016;
Shibut and Singer 2015;
Gurtler and Hibbeln 2013;
Khieu et al. 2012;
Calabrese and Zenga 2010;
Caselli et al. 2008). However, some authors (see, for instance,
Ingermann et al. 2016;
Querci 2005) indicated that there is no relationship between the RR and the length of the workout process.
As a profitability measure, the ratio of earnings before interest and tax/total assets is positively related to the RRs as expected. This positive sign for the ratio of earnings before interest and tax/total assets is not surprising since profitability metrics indicate the feasibility of the defaulted company’s business visions. Profitable firms can conduct successful resolution processes and hence, they are associated with superior recoveries, and unprofitable corporations find it more challenging to conduct fruitful resolution processes, leading to low recoveries. Among other authors,
Jacobs et al. (
2010) supported the positive relationship between profitability and RRs. Conversely,
Jankowitsch et al. (
2014) recognised an insignificant relationship between RRs and profitability.
The total debt/total assets ratio is a leverage indicator. Our study has a prior expectation of a negative sign for the total debt/total assets ratio’s regression coefficients. This experiment discovers an inverse relationship between the private firm loan RRs and the ratio of total debt/total assets, indicating that as the ratio increases, RRs fall. This finding is not surprising since credit comes at a cost which adversely influences the capacity of firm borrowers to reimburse their loans. Since 2016, Zimbabwe has been experiencing chronic liquidity challenges and firms have been significantly using debt. The usage of high levels of leverage by private firms has weakened their cover against liquidity shocks, leading to low RRs.
Francois (
2019),
Khieu et al. (
2012),
Carey and Gordy (
2007),
Acharya et al. (
2007) and
Varma and Cantor (
2005) are some of the authors that exposed a negative association between the RRs and leverage.
Acharya et al. (
2007) articulated that corporations with greater leverage levels are connected to a higher scattering of debt ownership, which makes it difficult to hold talks regarding restructuring, thereby leading to low RRs. However, some studies discovered a positive correlation between RRs and leverage since firms with greater leverage levels are exposed to increased monitoring by banks, thereby increasing the RRs. Further,
Wang et al. (
2020) and
Jankowitsch et al. (
2014) found that leverage is not substantially linked to RRs.
As a liquidity measure, the (current assets-current liabilities)/total assets ratio has a positive sign as anticipated, indicating that RRs increase as the ratio rises. That is to say, RRs are low when dealing with defaulted loans for illiquid private firms. High levels of liquidity allow private firms to meet their short-term obligations. Zimbabwe has been witnessing a severe liquidity squeeze in its currency system since 2016, resulting in low RRs as private firms fail to honour their obligations. In support of this,
Francois (
2019),
Jankowitsch et al. (
2014), and
Varma and Cantor (
2005) discovered a positive relationship between RRs and liquidity.
The number of creditors, which is a proxy for the private corporates’ capital structures, is negatively correlated with the RRs as expected, indicating that as the number of creditors increases, the RRs fall. Most Zimbabwean private corporations are often undercapitalised. Hence, they usually employ debt sourced from a number of creditors to finance their working capital needs and growth. Due to incessant viability challenges mainly caused by the existence of distressed financial and economic conditions, the majority of these private corporations have failed to pay-off their outstanding debts pooled from a number of creditors, leading to low RRs. Moreover, a challenging resolution process characterises private firms with several creditors as creditors compete for the corporations’ assets, resulting in low RRs. In agreement with this assertion,
Chalupka and Kopecsni (
2009) discovered that a borrower with several loans is associated with a low RR.
In this study, we find that the time with the bank is negatively associated with the RRs, indicating that as the time with the bank increases, RRs fall, and as the time with the bank shortens, RRs increase. This sounds unreasonable considering that usually firm borrowers with long-term relationships with their banks enjoy a lot of benefits provided by their banks which include low interest rates and efficient monitoring. A possible explanation for this is that banks may not put extra effort and resources into monitoring and analysing the creditworthiness of borrowers with long-term relationships, leading to low RRs. Moreover, banks may be highly conflicted when dealing with defaulted customers associated with long-term relationships. Therefore, banks face risks for being reproached of abusing conflicts of interests or deserting their anticipated responsibilities as relationship banks, leading to low RRs. Our finding of the negative relationship between the time with the bank and RRs agrees with
Zhang and Thomas (
2012).
The age of loan at default is positively associated with the RRs, demonstrating that as the age of loan at default increases, RRs increase, and as the age of loan at default falls, RRs fall. If the loan defaults closer to its maturity date, a firm borrower can easily complete the debt repayments, therefore increasing the RR. The age of loan at default apprehend the propensity for loans associated with inferior quality to default sooner (
Johnston-Ross and Shibut 2015). In support of our finding,
Johnston-Ross and Shibut (
2015) exposed that an upsurge in the age of the loan at default is related to an LGD decrease, indicating that an increase in the age of the loan at default lead to an increase in RR.
The study results reveal that the loan maturity period is negatively related to RRs, indicating that as the loan maturity period increases, the RRs drop, and as the loan maturity period shortens, the RRs surge up. This is not surprising since uncertainty increases with the maturity period of loans, which may increase the cost of long-term loans, among other things. For instance,
European Commission (
2005) articulated that interest rates for short-term loans are lesser than those for long-term loans due to lower uncertainty. In support of our findings,
Zhang and Thomas (
2012) and
Kosak and Poljsak (
2010) discovered an adverse relationship between loan term and RRs.
In terms of the prediction performance, model 1(c) is better than models 1(a) and 1(b) considering MAE, RMSE, R2 and α estimates. Model 1(c) has the lowest MAE and RMSE values followed by model 1(b) and then model 1(a). Moreover, model 1(c) is associated with the highest R2 and α estimates followed by model 1(b) and then model 1(a). Models 1(a)–(c) are capable of explaining 33.48%, 38.70% and 42.80%, respectively, of the variance in RRs. This indicates that models for the two sub-samples perform better than the model for the whole sample.
4.2. Models with Firm Features, Account Characteristics and Macroeconomic Variables
Table 4 below presents the developed models premised on firm features, loan characteristics and macroeconomic factors.
The empirical findings reveal that all explanatory factors included in models 2(a)–(c) are considerably correlated with the RRs for defaulted Zimbabwean privately owned non-financial firm bank loans, with the length of the workout process, the EAD, the total debt/total assets ratio, number of creditors, time with the bank and the interest rate having a negative influence on the RRs, and the total assets, the collateral value, the (current assets-current liabilities)/total assets ratio, the ratio of earnings before interest and tax/total assets, firm age, the inflation rate and the real GDP growth rate having a positive influence on the RRs. We observe that the signs for the estimated coefficients for the total debt/total assets ratio, the total assets, the EAD, the collateral value, the ratio of (current assets-current liabilities)/total assets, the earnings before interest and tax/total assets ratio, number of creditors, firm age and the length of the workout process are similar to those in models 1(a)–(c).
As expected, the interest rate has a negative sign. This indicates that the RRs decline as the interest rate rises and the RRs rise as the interest rate falls. Generally, higher interest rates make it more challenging for a firm borrower to repay its outstanding loan balances. Higher interest rates lower the RRs at the default time since the claim on the obligor continues to increase after default due to interest accruals. This proclamation is supported by the research results of
Nakayiza (
2013) and
Kosak and Poljsak (
2010). Using the same line of reasoning,
Bellotti and Crook (
2012) employed the United Kingdom retail banks’ base interest rates and concluded that the interest rates at the default time and RRs are negatively related.
In particular, the real GDP growth rate enters models 2(a)–(c) with a positive sign as expected, indicating that as the real GDP growth rate increases, RRs rise. This discovered relationship is not surprising since a surge in the real GDP growth rate indicates that the economy is performing well and is moving in the right direction. The existent literature revealed that RRs are lesser in times of recessions and greater during expansions (see, for instance,
Hanson and Schuermann 2004;
Frye 2000). Under stressed economic and financial conditions, debtors are less likely to reimburse their debts, which adversely impacts RRs. Moreover, workout periods are lengthened during times of distress than during normal times (
Shibut and Singer 2015), resulting in low RRs. In the same vein,
Park and Bang (
2014),
Han and Jang (
2013) and
Khieu et al. (
2012) discovered a substantial positive relationship between the RR and the real GDP growth. However,
Wang et al. (
2020) indicated that there is no association between the RRs and the annual GDP growth while
Ingermann et al. (
2016) observed that there is no significant association between RR and the real GDP growth.
The experimental results show that the rate of inflation and the RRs are positively related, indicating that as the rate of inflation rises, RRs increase and as the inflation rate falls, RRs decrease. This is not surprising since inflation benefits borrowers by reducing the real value of loans, thereby making it easier for them to pay their loans. The observation period under review has been associated with a deflation which led to a decrease in economic activity, an upsurge in unemployment rates, a drop in investment, a rise in debt’s real value and stifled economic growth and development (see, for example,
Mahonde 2016).
Masiyandima et al. (
2018) argued that the emergence of the American dollar as the primary currency in Zimbabwe led to negative and low rates of inflation which impacted negatively on the country’s growth. Deflation exacerbated the recession in Zimbabwe and resulted in a deflationary spiral. Since Zimbabwean private firms use a lot of debt, an upsurge in the debts’ real value due to deflation led to low RRs.
Model 2(c) is better than models 2(a) and 2(b) in terms of performance considering MAE, RMSE, R2 and α values. Model 2(c) is associated with the lowest MAE and RMSE values followed by model 2(b) and then model 2(a). In addition, model 2(c) is associated with the highest R2 and α values followed by model 2(b) and then model 2(a). Models 2(a)–(c) can describe 42.49%, 46.10% and 50.60%, respectively, of the variance in RRs. This shows that models for the two sub-samples perform better than the model for the whole sample.
Comparing RR models for each respective category, i.e., whole sample, sub-sample 1 and sub-sample 2, we observe that models with firm features, loan characteristics and macroeconomic variables describes RRs better than models with firm features and macroeconomic variables. Therefore, our study concludes that incorporating macroeconomic variables into the RR models gives a better model fit, resulting in substantial enhancement of the models’ explanatory abilities.
Although the model fit is weak across all the developed models as indicated by R
2, such values are usual in RR modelling. The results found in this experiment are comparable to the results discovered in other studies such as
Hocht et al. (
2022),
Ingermann et al. (
2016) and
Zhang and Thomas (
2012). For example, using the training sample,
Zhang and Thomas (
2012) created a RR linear regression model and got an MSE of 0.1650, MAE of 0.3663, α of 0.3183 and R
2 of 0.1066.