Chapter 5: Determinants of corporate loan recovery rates under economic and
5.1 Introduction
151
Chapter 5: Determinants of corporate loan recovery rates
152 and unexpected losses, thus affecting the value at risk (Ingermann et al., 2016). Using the same line of reasoning, Wang et al. (2018) observed that the failure to precisely predict recovery rates in the credit risk process could result in under- or over- provisioning for the forthcoming loan losses.
Moreover, appropriate recovery rate models lead to reduced capital requirements (Ingermann et al., 2016). Also, it has emerged that accurate prediction of recovery rates can produce a competitive advantage and reduce challenges emanating from unfavourable picking owing to small variances in loan spreads (see Ingermann et al., 2016 and Gurtler and Hibbeln, 2013). Regulators need financial organisations to display evidence of recovery rates modelling to warrant that regulatory capital requirements are upheld. However, in the Basel II/III AIRB approach, there is no methodological precision regarding the models required for estimating recovery rates. This issue causes variability in the recovery rate models adopted between banks.
The majority of research papers have proposed recovery rate models for developed economies (see Wang et al., 2018; Francois, 2019; Ingermann et al., 2018; Khieu, Mullineaux and Yi, 2012), especially for corporate bonds since data for publicly-traded bonds is broadly available (Mora, 2015; Jankowitsch, Nagler and Subrahmanyam, 2014;
Yao, Crook and Andreeva, 2014). Although bank loan recovery rate literature has been multiplying of late (Ingermann et al., 2018), it is generally restricted and is even more constrained in undeveloped markets. Existent literature on bank loan recovery rates is substantially devoted to advanced economies (Ingermann et al., 2018; Khieu, Mullineaux and Yi, 2012). Further, Khieu, Mullineaux and Yi (2012) posited that the mainstream of the extant literature on loan recovery rates come from the research work of practitioners, while research articles in the academic literature focus principally on the recovery rates of bonds. This is attributed to the scarcity of recovery data for bank loans since they are regarded as private debt instruments.
Also, literature on private firm recovery rates is exceptionally limited and is significantly focused on developed markets (see, for instance, Franks, de Servigny and Davydenko, 2004). This current study strives to predict recovery rates for private non- financial firm defaulted bank loans in a developing economy, that is, Zimbabwe, under economic and financial stress. This experiment is partly motivated by Basel II that encourages financial institutions to assign loss given default to each credit facility.
Forecasting recovery rate for defaulted private corporate bank loans in an undeveloped
153 market under economic and financial stress is imperative for several reasons. Also, bank loans are significantly different to securities such as bonds and other traded corporate debts (Wang et al., 2018; Acharya, Bharath, and Srinivasan, 2007; Cantor and Varma, 2005; Franks and Torous, 1994; Gilson, 1990; Gilson, John and Lang, 1990). Private corporations are dominant firms in undeveloped markets (Charalambakis and Garrett, 2019; Slefendorfas, 2016; Organisation for Economic Co-operation and Development, 2015; Charalambakis, 2014). Bank loans granted to private firms are of high importance in Zimbabwe since a significant portion of the total bank loans is given to such type of firms. Moreover, it is not an inconsequential matter to predict recovery rates since the empirical research work on recovery rates usually presents low model forecasting ability, and there is no industry accord on which model is best suitable for modelling recovery rates.
Diverse economies or jurisdictions are characterised by distinctive settings such as rules, creditors' rights, reorganisation practices and legal systems which affect recoveries and do not allow the reapplication of recovery rate and loss given default models designed in them in different atmospheres (see Mora, 2015; Shibut and Singer, 2015; Peter, 2011; Davydenko and Franks, 2008; Bris, Welch and Zhu, 2006; Querci, 2005). In support of this, Franks, de Servigny and Davydenko (2004) computed recovery rates of firms in default whose data was gathered from 10 banks from 3 different economies. The authors revealed that due to different country-specific regimes of bankruptcy, recovery rates are substantially dissimilar among the three economies, particularly the United Kingdom (UK), Germany and France. The authors stated that the average values for the loss given default for the UK, Germany and France are 25%, 39% and 47%, respectively. Franks, de Servigny and Davydenko (2004) discovered that results were responsive to the adopted sample data and workout process regulatory structure.
Literally, the economic and regulatory settings of developed and undeveloped markets are not similar. For instance, Waqas and Md-Rus (2018) suggested that advanced economies have clearly stated bankruptcy laws and procedures, while undeveloped markets are deficient of such bankruptcy laws and procedures. Therefore, how each market allocates recovery risk premiums and the approach in which financial distress is ultimately dealt with generate diverse configurations for recovery rates across various developed and undeveloped economies. Thus, implementing recovery rate models
154 designed for advanced economies in undeveloped countries does not usually give dependable results.
The recent 2007 – 2008 global financial and economic crisis has highlighted the recovery rates' multifaceted and stochastic nature in the event of default (Jankowitsch, Nagler and Subrahmanyam, 2014). Basically, recovery rates are driven by endogenous factors (for example, firm and account characteristics) and exogenous factors (for example, macroeconomic variables). Interestingly, the incorporation of macroeconomic factors reduces uncertainty in forecasting recovery rates since they capture the effects of distressed economic and financial conditions and follows Basel II recommendation to predict the “downturn loss given default” (Bellotti and Crook, 2012). Although distressed economic and financial conditions are associated with low recovery rates, it is not crystal clear which variables may describe recovery rates in a better way under downturn conditions. Betz, Kellner and Rosch (2018) declared that the values of loss given default are based on recovery rates generated throughout different economic situations in the process of resolution and hence, it is a difficult task to select appropriate macroeconomic factors.
This analysis provides answers to the main questions below:
(i) What are the critical predictor variables of recovery rates for defaulted private non- financial firm bank loans under downturn conditions in a developing country?
(ii) Does the incorporation of macroeconomic variables result in improved recovery rate models?
(iii) How well do the designed models perform in predicting recovery rates?
In this study, these questions are explored by suggesting stepwise Ordinary Least Squares (OLS) regression models founded on diverse combinations of firm characteristics, loan features and macroeconomic factors in order to predict workout recovery rates for defaulted private non-financial firm bank loans in the context of economic and financial stress in a developing economy 12 months in advance. The study's principal focus is on the identification and economic interpretation of the predicted coefficients for the drivers included in the designed models. To fit the models, the study adopts a unique cross-sectional real-life data set of defaulted private firm bank loans accessed from a major anonymised Zimbabwean commercial bank over the
155 sample period 2010 - 2018. Geographically, the data set is an accurate picture of the Zimbabwean market. The author believes that this research project is the first piece of research work to analyse recovery rates for Zimbabwean privately-owned firm’s defaulted bank loans.
Zimbabwe provides an exciting and challenging example in examining recovery rates for defaulted private firm bank loans in developing countries under economic and financial stress. Zimbabwe has been experiencing severe and extended downturn conditions over the past two decades which have promoted deindustrialisation and informalisation of the economy. In 2009, the nation phased out the Zimbabwean dollar and embraced a number of currencies which included the British pound, Botswana pula, euro, South African rand and American dollar to stabilise the economy. Nevertheless, the American dollar materialised as the presentation and functional currency of corporations. The advent of the American dollar as the chief currency caused negative and low inflation rates, which adversely affected the nation's growth (Masiyandima et al., 2018). Masiyandima et al. (2018) indicated that the country observed 28 uninterrupted months of negative inflation from October 2014 - January 2017. During the period under review, that is, 2010 -2018, the real gross domestic product (GDP) growth rate has fallen from more than 10% per annum in the period 2010 - 2012 to an average of 2.5% from 2013 - 2018 (World Bank Group, 2020a). The distressed economic and financial conditions observed in Zimbabwe are hardly experienced in advanced or even in other undeveloped markets. Therefore, the results of this examination can be compared to, but must not be expected to be similar to the results of other research work in recovery modelling.
In Zimbabwe there is a clear legal structure in resolving corporate financial distress issues. Distressed corporations can be liquidated or placed under judicial management.
The purpose of judicial management is to offer sustainable firms which are in financial difficulties a more even opportunity to rehabilitate themselves and be returned to profitability. When a firm displays financial difficulties by failing to meet obligations when they fall due, an application for judicial management can be made to the courts by a corporation itself or creditors or shareholders or certain officials. The courts have discretion on judicial management. Also, creditors or distressed firms themselves or judicial managers can apply to the courts for the liquidation of the distressed firms.
After the borrower's default on payment, a liquidation proceeding can be initiated by filing a petition with the courts. The courts then evaluate the petition and, if the balance
156 sheet of the firm shows that it is insolvent, the courts will support liquidation. The liquidation of firms and their placement under judicial management are guided by the Insolvency Act.
The judicial procedures for dealing with corporate bankruptcy affect the estimation of recovery rates. For instance, insolvency reforms in Zimbabwe permit an insolvency representative or a debtor to get new financing upon insolvency proceedings instigation.
Further, World Bank Group (2020b) indicated that the costs related to the liquidation proceeding in Zimbabwe amount to about 22% of the worth of the estate of the borrower. These costs include attorney fees, court or government agency fees, fees of accountants, insolvency representative fees, etc.
The experimental results show that the firm size, the earnings before interest and tax/total assets ratio, the exposure at default, the length of the workout process, the total debt/total assets ratio, the ratio of (current assets – current liabilities)/total assets, the inflation rate, the interest rate, the collateral value and the real GDP growth rate are all significant determinants of recovery rates for Zimbabwean private non-financial corporate bank loans. In particular, the research project discovers a negative effect on the recovery rates, of the exposure at default, the length of the workout process, the total debt/total assets ratio and the interest rate on the recovery rates, and also a positive impact of the firm size, the ratio of earnings before interest and tax/total assets, the (current assets – current liabilities)/total assets ratio, the collateral value, the inflation rate and the real GDP growth rate. The included financial ratios are vital since they denote some of the most imperative credit risk factors, particularly, size, profitability, leverage and liquidity. In this study, it is determined that accounting information is useful in examining recovery rates for defaulted bank loans for private corporations under downturn conditions in a developing economy. Moreover, the research project reveals that the prediction results of recovery rate models are augmented by including macroeconomic factors. This research result is in line with the discovery of Bellotti and Crook (2012), who posited that recovery rate prediction models' forecasting results are improved by including macroeconomic variables.
The remainder of the chapter is set out as below. Section 5.2 outlines the literature review. The methodology is outlined in section 5.3, and in section 5.4 the data and variables are described. In section 5.5, the empirical results and analysis are stated.
157 Lastly, section 5.6 articulates the conclusions and possible directions for future research.