Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download Incorporation of financial ratios into prudential definition of assets
Document related concepts
Federal takeover of Fannie Mae and Freddie Mac wikipedia , lookup
Moral hazard wikipedia , lookup
Syndicated loan wikipedia , lookup
Household debt wikipedia , lookup
Financial literacy wikipedia , lookup
Investment management wikipedia , lookup
Global financial system wikipedia , lookup
International asset recovery wikipedia , lookup
Interest rate ceiling wikipedia , lookup
Global saving glut wikipedia , lookup
Shadow banking system wikipedia , lookup
Securitization wikipedia , lookup
Public finance wikipedia , lookup
Financial economics wikipedia , lookup
Systemic risk wikipedia , lookup
Financial Crisis Inquiry Commission wikipedia , lookup
Financial crisis wikipedia , lookup
Financialization wikipedia , lookup
Systemically important financial institution wikipedia , lookup
Transcript
National Bank of Georgia Draft Paper Incorporation of financial ratios into prudential definition of assets: Micro and macro prudential perspective Giorgi Kadagidze, Otar Nadaraia, Salome Skhirtladze, Vakhtang Sikharulishvili, Ana Kvaratskhelia September 18, 2015 Incorporation of financial ratios into prudential definition of assets: Micro and Macro Prudential perspective September 18, 2015 Incorporation of financial ratios into prudential definition of assets: Micro and macro prudential perspective Giorgi Kadagidze (1), Otar Nadaraia (2), Salome Skhirtladze (3), Vakhtang Sikharulishvili (4), Ana Kvaratskhelia (5) September 18, 2015 Abstract In this paper we discuss current framework of prudential definition of assets and demonstrate that existing definitions are not comprehensive and lack consistency across countries. Thus, they provide significant room for asset quality misinterpretation. To address these problems we suggest the following measures: 1. Incorporation of financial ratios into prudential definition of assets quality; 2. Disclosure of different types of exposures by financial ratios and their role in financial institutions’ credit risk management processes; 3. Increased emphasis on the use of financial ratios in macro prudential oversight. We support these findings by the results of various studies, indicating that synthetic rating approach can explain the vast majority of defaults. The proposal is in line with the worldwide practice of increasing the focus on financial ratios for micro and macro prudential oversight, as well as with the objectives of the Basel Committee reform to find balance between simplicity, risk sensitivity and comparability. Keywords: Financial Ratios, Asset Classification System; Asset Quality; Prudential Definition of Assets, Disclosures; 1. 2. 3. 4. 5. National Bank of Georgia (NBG), Governor; NBG, Vice-Governor; NBG, Head of Finance and Accounting Department; NBG, Head of Credit Risk Division, Specialized Groups and Supervisory Policy Department; NBG, Head of Financial Reporting Division; -1- Incorporation of financial ratios into prudential definition of assets: Micro and Macro Prudential perspective September 18, 2015 Table of Contents Introduction ........................................................................................................................................................... - 3 1. Prudential definition of assets, current practice ........................................................................................... - 5 - 2. Financial ratios: Micro foundation ................................................................................................................. - 8 - 2.1. Corporate sector ............................................................................................................................................. - 8 2.2. Household sector .......................................................................................................................................... - 10 2.3. Financial ratios in Micro prudential framework ........................................................................................... - 11 3. Financial ratios: Macro foundation .............................................................................................................. - 13 - 4. Incorporation of financial ratios into asset classification system and disclosure requirements ................. - 18 - 4.1. Incorporation of financial ratios into asset classification system ................................................................. - 18 4.2. Incorporation of financial ratios into disclosure requirements .................................................................... - 20 Conclusion ............................................................................................................................................................ - 21 Appendix 1: Most commonly used financial ratios for predicting corporate bankruptcy................................... - 23 Appendix 2: Draft addition to Asset Classification System: National Bank of Georgia........................................ - 24 References: .......................................................................................................................................................... - 29 - -2- Incorporation of financial ratios into prudential definition of assets: Micro and Macro Prudential perspective September 18, 2015 Introduction Asset classification systems require banks to segregate financial assets into risk buckets according to credit risk characteristics. The prudent segregation of assets by credit quality is vital for estimating related expected and unexpected credit losses and assessing if a bank’s capital level adequately reflects the risks of underlying assets. The segregation also allows for benchmarking the bank against peer institutions and countries and evaluating its overall risk profile. Recent financial crisis highlighted that current asset classification systems differ across countries and institutions. Moreover, these systems promote such practice where banks might classify borrowers as they see fit in line with their needs, and not based on certain objective standards. In the first part of the paper we discuss several key aspects that contribute to inconsistency and lack of comparability, and highlight the need for incorporating objective criteria in both prudential and accounting frameworks. In the second part of the paper we summarize empirical studies to evaluate if financial ratios provide sufficient explanations to understand borrowers’ overall riskiness, and if these ratios can be regarded as effective objective criteria within asset classification framework. We discuss international experience of the use of ratios in credit risk measurement systems, including different scoring models based on accounting ratios, and the application of ratios in more complex models, such as credit rating agencies’ methodologies. We also examine international practice of incorporating financial ratios in micro prudential frameworks, such as Basel capital framework for standardized approach, and leveraged lending guidelines issued by the US Federal Reserve. Both empirical evidence and prudential experience indicate that financial ratios can predict a borrower’s probability of default with high degree of accuracy and thus, explain vast majority of risks. Financial ratios are widely applied in macro prudential policy framework as well. In the third part of the paper we demonstrate the advantages of using micro granular data and financial ratios in contrast to the conventional macro measures, such as credit/GDP, in estimating system-wide credit risks, inherent in banks’ financial assets and in the overall private sector. Based on the trends observed prior to and during the financial crisis in Canada, US and Israel, we argue that financial ratios have the capacity to distinguish between ‘normal’ fluctuations and systemic imbalances. In addition, we discuss several aspects in which financial ratios can effectively complement macro prudential objectives. Finally, as financial ratios have become the integral part of the financial stability assessment, we raise questions regarding the consistency of their interpretation and common understanding, including, the lack of micro or macro prudential policy responses to address existing weaknesses. In the final part of the paper we propose specific solutions to address existing weaknesses associated with asset classification systems. In particular, we discuss the approach developed by the National Bank of Georgia for the prudential asset classification framework, which entails the incorporation of financial ratios as additional objective criteria in the classification system. -3- Incorporation of financial ratios into prudential definition of assets: Micro and Macro Prudential perspective September 18, 2015 We believe that the abovementioned approach will address number of current challenges. For instance, it will enhance comparability across countries and across banks within jurisdictions and ensure consistent application of prudential and accounting asset classification systems. It will also establish that asset portfolios are segregated according to underlying credit risk characteristics. The ratios will further limit the likelihood of credit risk misinterpretation, and reduce the reliance on banks’ own estimates, thus, mitigating the problems associated with forbearance as well. Such approach will fill the gap associated with the accounting practice; in particular it will treat financial ratios as objective criteria for loan impairment assessment and thus will limit the bank’s discretion to underestimate credit losses. In addition, the incorporation of ratios into micro framework will be a powerful tool for preventing macro systemic imbalances. This bottom-up approach will grant the instrument of “where the power is” to the supervisor. The latter will result in immediate policy responses unlike the topdown approach, within which, even high quality research is often not followed by relevant actions. Lastly, we provide recommendations regarding the incorporation of financial ratios into Pillar 3 disclosure requirements, reduction of information asymmetry associated with banks’ loan portfolio quality, and promotion of market discipline. Additional information integrated in Pillar 3 will enhance overall understanding of credit risks inherent in banks’ asset portfolios, facilitate useful comparison of institutions’ risk profiles, and promote increased market confidence in banks’ asset quality. -4- Incorporation of financial ratios into prudential definition of assets: Micro and Macro Prudential perspective September 18, 2015 1. Prudential definition of assets, current practice There is no uniform practice of segregating banks’ assets into risk buckets for prudential reporting purposes. Currently, the implementation of asset classification system is at the discretion of national authorities and this practice varies across countries and even across institutions within the same jurisdiction. The differences range from the number of asset buckets used, to the manner in which relevant criteria are defined and applied. Normally, asset classification systems are comprised of five buckets, and mostly follow the same structure: pass/standard, watch/special mention, substandard, doubtful and loss. The definition for each bucket entails the use of general qualitative and quantitative criteria, where the latter is based on the notion of past due days. According to its most basic definition, standard/pass category covers assets with no problems in terms of collectability, with sound fundamentals of borrowers, ability to absorb shocks on a going concern bases and no past due days. Weakened-watch category represents lower boundary of standard/pass loans, but with somewhat higher collectability risk than normal because of difficulties in fulfilling contractual obligations, or due to concerns about the borrower’s repayment capacity should the economic conditions deteriorate. The weakest asset category, and probably the most widely used metric to measure credit risk in the loan portfolio is the category of non-performing assets (NPA). NPA is a cover definition for defaulted or impaired (under accounting framework), doubtful, loss, write-off or restructured/forbearance classes of loans and constitutes an important component in Basel Revised Standardized Approach, where NPA takes part in the formation of supervisory capital. Under Basel capital framework, the asset is defined as defaulted when a borrower is unable to fully repay debt according to the contractual terms, or when a loan is 90 days past due. Cross-country studies show that 90 days past due criterion appears to be the most common criterion for NPA/default definition (D'Hulster, 2014). Further, there is no unified definition of restructured loans either; where the definition exists, it generally includes certain “forbearance events,” the strictness and scope of which varies widely among jurisdictions. Normally, two elements recur across different definitions: “a change in contract terms”, and “Financial difficulty of the borrower”, which are too broad and allow for wide interpretations (D'Hulster, 2014). In addition to variances in definitions, current framework promotes the practice of misinterpreting borrowers’ riskiness and late recognition of problem assets. This is mostly driven by overreliance on days past due criterion and the absence of a common understanding of or criteria for borrowers’ financial health. In addition, discretionally qualitative factors permit a wide interpretation of asset quality among institutions. The problem of overstated asset quality is evident in a number of crosscountry studies. For example, after introducing a simplified NPA definition1, as a result of the asset 1 simplified definition was specified as every material exposure with 90 days past-due, every exposure that was impaired under accounting principles (IFRS and GAAPs), forbearance and general qualitative criteria - every exposure that was in default according to CRR (i.e. "unlikely to pay"). -5- Incorporation of financial ratios into prudential definition of assets: Micro and Macro Prudential perspective September 18, 2015 quality review conducted by the ECB, non-performing exposures at EU banks were adjusted by 135.9 billion EUR (ECB, 2014). Past due days, as a single quantitative criterion, cannot capture increased risks associated with the borrower’s repayment capacity until the exposure gets into arrears. In addition, reliance on this criterion only makes it possible to evade classification of loans as past due and to overestimate the asset quality through loan restructurings. It must be noted that in general, modification of loan terms can be regarded as characteristic of normal business operations and thus, modifications do not necessarily indicate borrowers’ deteriorated repayment capacity. For example, receiving working capital financing is crucial for firms to maintain normal operating cycles. In practice, this operation is funded by banks with short-term facilities that are permanently revolved at maturity, although the borrower has the actual capacity to service the debt within contractual terms. Furthermore, extending maturity or consolidating outstanding loans are common practice when new business financing needs emerge or in the case of financing long-term projects if it keeps on track with planned path. Renegotiation of interest rates can also be caused by tight competition on the market, when banks refinance old loans to retain their customer base. Particular issues arise when a loan is renegotiated due to financial difficulties of the borrower and before it goes into arrears. Such events can be extensively used to avoid timely recognition of losses by masking the real creditworthiness of the borrower. However, once the asset classification system adequately captures the borrower’s financial strength, the modification of loan agreement loses its relevance. For instance, if a borrower with weakened repayment capacity (e.g. ICR <2) is allocated into a riskier asset bucket and hence, is subject to supervisory focus, then whether the loan is restructured or not is of lesser importance. Furthermore, qualitative criteria without any objective measures permit wide interpretations in measuring credit risk. In other words, the banks’ assessments are derived from subjective estimation of perceived risks associated with the borrower, which can impair comparability and consistency across banks and across countries. In this respect, a parallel can be drawn with IRB banks, where asset quality measurement is based on banks’ internal scoring models and there are no common objective criteria in place, which would help explain borrowers’ riskiness. Indeed, a number of recent studies points to the divergence in credit risk estimation and the practice of grading borrowers. For example, studies show that the absolute PD level for the same counterparty is differently estimated by different banks within a sample. As further explained, this divergence is driven by diverse perceptions of risks, or by methodological choices, such as various scoring models and definition of default, the degree of the involvement of expert judgements, different observation periods, and different approaches to calibrating internal scoring models with external sources (EBA 2013, Leslé and Avramova 2012). Identification of problem assets constitutes a challenge from the accounting perspective as well. For instance, IAS 39 standard requires entities to assess, at each reporting date, if there is any objective evidence that an individual loan, or a portfolio of loans, is impaired. Objective evidence of impairment is considered to stem from some loss event or events that, individually or together, have impact on the value of the estimated future cash flows attributable to the loan or the portfolio of loans. Examples of such events (triggers) include the evidence of financial distress on the part of an obligor, delinquency -6- Incorporation of financial ratios into prudential definition of assets: Micro and Macro Prudential perspective September 18, 2015 or default in relation to interest or principal payments, and the entry into (or a high probability of entry into) bankruptcy or financial reorganization. However, this definition still leaves an immense room for judgment on a number of critical elements, such as what exactly constitutes objective evidence and how to estimate future cash flows. Again, if loss events entail reliance on past due days and disregard financial strength of a borrower, the assessment will result in overstated asset quality. Other examples of critical questions that arise are: Should the borrower with ICR <1 be subject to impairment assessment? Once the repayment capacity is deteriorated, would it not affect expected future cash flows? Is the latter not an incurred loss event? Theoretically, incorporation of objective criteria indicating borrowers’ financial difficulties could address “too late and too low” problem in estimating impairment losses to a certain degree. One more notable challenge associated with the accounting practice is that there is less clarity regarding factors that drive differences between accounting and prudential asset classification. For instance, NPA assets are frequently associated with impaired assets under IAS 39, albeit with significant divergence in measurement outcomes. Normally, the differences are attributed firstly to the variances in “loss events” under regulatory and accounting definitions, and secondly to the treatment of collateral. To illustrate, under IAS 39 the asset is deemed to be impaired if loss event (or events) has an impact on the estimated future cash flows, taking collateral into account. Conversely, prudential definition of default loans stipulates that such loans shall present a risk of not being paid back in full without collateral realization. Thus, meaningful comparison between these two frameworks would entail comparison of NPA and assets that meet the requirements set out in IAS39.59, (more specifically, when the objective evidence of a loss event has been observed, but before impairment assessment). Nevertheless, current definitions, including the one suggested by EBA, do not address this issue, leaving room for differences in the application or underestimation of NPA. (D'Hulster, 2014). The points discussed above display substantial evidence for arguing that under current classification framework, banks might classify borrowers as they see fit, in line with their needs and not based on certain objective standards; the latter trend holds true for accounting practices as well. We show that the quality, comparability and objectiveness of loan classification systems implies adherence to some objective criteria, which are beyond individual bank’s management control. In following sections we will explore whether financial ratios, indicating borrower’s financial strength can be regarded as common objective criteria in asset classification schemes, and whether they can help effectively address current challenges. -7- Incorporation of financial ratios into prudential definition of assets: Micro and Macro Prudential perspective September 18, 2015 2. Financial ratios: Micro foundation 2.1. Corporate sector The ability to correctly predict corporate distress or probability of default is the issue that has historically attracted attention in the financial literature. Modern practice for credit risk (probability of default) estimation provides a wide variety of models, ranging from simple (based on quantitative criteria) and more complex approaches, entailing the use of expert judgment in addition to quantitative criteria. All types of bankruptcy prediction models, both classic and modern, are essentially based on borrower-related financial information. Use of financial ratios for predicting business failures and bankruptcy risk has a century long tradition, with an early application by Fitzpatrick (1932), who found that the probability of default was related to the individual characteristics of corporates (Appendix 1 - the most commonly used financial ratios in predicting corporate bankruptcy). One of the most influential and earliest contributions to the literature on predicting corporate failure was the Altman (Altman, 1968) Z-score model, which was later upgraded to Zeta-score model. The original Z score model is a statistical model that incorporates five financial ratios in its analyses, namely liquidity, productivity, profitability, leverage and sales generation ability; the model displayed the accuracy ratio of up to 95% using a sample of 91 manufacturing firms. Further, Altman model used to evaluate a sample of 40 publicly traded companies in Israel was able to predict bankrupt companies with an accuracy rate of 95% one year prior to bankruptcy, while two years prior to bankruptcy it had an accuracy rate of 85% (Lifschutz, Jacobi 2010). Another often-quoted credit-rating study with accounting variables was performed by Ohlson (Ohlson, 1980), using 9 financial ratios to predict bankruptcy with the accuracy rate of about 88 %. Further, the Ohlson model was retested using data from Chinese publicly listed companies, indicating prediction accuracy rate above 95% (Wang, 2010). Several empirical studies indicate that address to firm size criteria significantly improves performance of such scoring models. For example, Altman (Altman, 2007) developed a model (with five ratios) adjusted for SME sector, on the bases of 2000 US firms over the period 1994-2002. The model demonstrated 30% higher performance than the generic corporate model (z-score). Studies also indicate that scoring models based on financial ratios have more or less similar performance across countries and regions. For instance, Z –score model based on four financial ratios (Working capital/Total assets, Retained Earnings/Total assets, Earnings before interest and taxes/Total assets and Book value of equity/Book value of total liabilities) was tested on a sample of firms from 32 European and three non-European countries and demonstrated prediction accuracy levels of about 75%, and exceptionally well (above 90%) for some countries (Altman 2014). During recent years, there is increased interest of central banks in studies around corporate bankruptcy. For example the model by national bank of Czech Republic (Jakubík and Teplý, 2011) based on seven financial ratios is capable to explain business failure at a 1-year prediction horizon. The -8- Incorporation of financial ratios into prudential definition of assets: Micro and Macro Prudential perspective September 18, 2015 model developed by Sveriges Riksbank (Giordani, 2013) predicts corporate bankruptcy based on three financial ratios (by taking into account size and age of the firms) with the 90% accuracy. Apart from the scoring models discussed above, financial ratios have a prominent role in complex credit risk models as well, such as the models used by credit risk rating agencies. For example, S&P relies on two core payback ratios: (1) funds from operations (FFO) to debt and (2) Debt to EBITDA, to determine the relative ranking of companies’ financial risk. Preliminary assessment is further adjusted through additional ratio analysis, in particular payback (cash from operations [CFO] to debt, free operating cash flow [FOCF] to debt, discretionary cash flow [DCF] to debt) and coverage ((FFO+ interest] to cash interest and EBITDA to interest) ratios. In determining the degree of financial risks associated with the borrower, S&P defines ratio standards/thresholds, further differentiated to account for business risk profile. For instance, within the same risk bucket, less stringent thresholds will be applied to companies operating in low-risk industries than to those companies that operate in industries with historically high volatility in earnings. To elaborate, slightly higher leverage will be tolerated for food producing companies than for auto manufacturers, since food producers demonstrated more resilience to business cycles or adverse changes in macroeconomic environment than auto manufacturers did. Chart 1 provides the visualization of core financial ratio thresholds by risk buckets according to S&P, where highly leveraged firms are those with Debt/EBITDA above 5 and above 6, for low and high business risk profiles, respectively. Chart 1: Thresholds of Leverage and coverage ratios by credit risk buckets, S&P Business risk is determined based on the company's Corporate Industry and Country Risk Assessment (CICRA); As for the business risk profile, for companies exhibiting low volatility (Low Business risk), the threshold levels for the applicable ratios to achieve a given assessment are less stringent than those in the medial or standard volatility tables, although the range of the ratios is narrower. Conversely, if the company -9- Incorporation of financial ratios into prudential definition of assets: Micro and Macro Prudential perspective September 18, 2015 exhibits medial or standard levels of volatility, the threshold for the applicable ratios to achieve a given cash flow/leverage assessment are elevated, albeit with a wider range of values; The range of financial risk profile assessments for a company is 1, minimal; 2, modest; 3, intermediate; 4, significant; 5, aggressive; and 6, highly leveraged. Source: S&P On the basis of the study discussed above and other related research it is possible to conclude that financial ratios possess the ability to signal a company’s financial health problems and their potential causes, and explain credit risk in the vast majority of cases. Empirical studies indicate that all groups of financial ratios (leverage, profitability, liquidity, activity, coverage) can effectively contribute to the modeling of business failure. 2.2. Household sector In the context of household sectors, number of empirical studies indicated that two financial ratios, particularly Loan-to-value (LTV) and Debt service-To-Income (DSTI or DSR), are high correlated with the household probability of default. For instance, Shubhasis Dey, Ramdane Djoudad, and Yaz Terajima (Bank of Canada, 2008) provide evidence that for DSR ratio beyond 35 per cent there is a significant increase in the probability of Mortgage-Debt delinquency (Chart 2). Chart 2: The Relationship between the DSR and the Probability of Mortgage-Debt Delinquency According to Bank of Canada estimates a critical DSR threshold for 2002 seems to be 35 per cent, since there is a large increase in the probability of mortgage-debt delinquency above this level. Source: Bank of Canada, 2008 - 10 - Incorporation of financial ratios into prudential definition of assets: Micro and Macro Prudential perspective September 18, 2015 Welter-Nicol and Dietsch (Banque de France, 2014), on the basis of a unique database containing 850 896 individual housing loans find that portfolio credit risk culminates in tranches close to the 100% LTV and the 35% DSTI thresholds. Campbell and Cocco (Campbell and Cocco, 2014) show the levels of LTV and DSTI ratios are correlated to defaults, and a loosening in these credit standards leads to a concentration of defaults. Mayer, Pence and Sherlund (Pence and Sherlund , 2008) find that higher LTV ratios lead to more defaults in US mortgage markets and have been important contributors to the subprime crisis. The drop in house prices caused many borrowers’ outstanding mortgage liability to exceed their home value, and this negative home equity level triggered their decision to default on their mortgages. 2.3. Financial ratios in Micro prudential framework There is wide application of financial ratios in micro prudential framework as well. It is to be noted, examples of recent reforms discussed below represent introduction of financial ratios into asset classification schemes, to certain extent. The 2013 US leveraged lending guidance was issued with the objective to address the supervisory focus and risk management expectations for financial institutions increasingly involved in leveraged lending activities during the recent years, in particular, those participating in origination and distribution of poorly underwritten and low-quality loans. The guidance defines leveraged and 'criticized' or 'special mention' loans by providing financial ratio (debt/EBITDA) as a key quantitative criteria. For instance, a loan is classified as leveraged if it involves a borrower whose Total Debt-to-EBITDA ratio or Senior Debt-to-EBITDA Ratio exceeds 4:1 or 3:1, respectively, or other defined levels as appropriate to the industry or sector. The definition of problem loans (criticized' or 'special mention') covers companies which cannot amortize or repay all senior debt from free cash flow, or half of its total debt, in five to seven years and companies with Debt-to-EBITDA over six times. The guidance also states that leverage in excess of 6.0X raises supervisory concern (Federal Reserve System, 2013). Another policy example of the inclusion of financial ratios in micro prudential framework is the recent reform of Basel capital framework. Under current initiative the risk weighting by reference to the external credit rating2 will be replaced by two financial ratios. However, it is also suggested to consider 2 The criticism of CRAs is focused on particular areas, such as conflict of interest with issuer, lack of transparency and comparability and procyclicality. More specifically CRAs demonstrated the ratings tend to be sticky, lagging markets, and then to overreact when they do change. Griffin and Tang (2012) demonstrate that CRAs made mostly positive adjustments to their models prediction of credit quality and that amount of upgrading increased substantially from 2003 to 2007. These upgrading were positively related to the downgrades when crisis started. Yongmin Cheny and Zhiyong Yaoz find that credit rating is procyclical: rating inflation is more likely to happen in a boom while rating deflation is more like to happen in a recession. Van Laere and Bart Baesens (2012) find that Moody’s and S&P set different standards for a particular rating grade while assessing banks. Matthieu Bussiere and Annukka Ristiniemi (Banque de France, 2012) provide empirical evidence, that sovereign credit ratings do not perform well to predict debt distress events. Furthermore, they provide empirical evidence that fundamentals exhibited better noise to signal ratio analysis. - 11 - Incorporation of financial ratios into prudential definition of assets: Micro and Macro Prudential perspective September 18, 2015 this model as a floor for IRB approach. The weights for the senior corporate exposures will be determined according to the obligor’s size (revenue) and leverage (total Debt/total Equity) and exposures secured by residential real estate will be weighted on the bases of loan-to-value and debtservice coverage (debt service payments relative to the borrower’s total income) ratios. For exposures to banks, the suggested risk drivers are based on capital and asset quality measures, for instance the capital adequacy ratio defined as Common Equity Tier 1 capital divided by Risk Weighted Assets, (CET1 ratio) and unreserved non-performing assets divided by total loans, leases and interest-bearing debt, (NPA ratio). The combination of the two risk drivers has been derived on the basis of quantitative analyses and expert judgements, and has proven to have strong predictive and explanatory capacity vis-à-vis solvency and financial strength of a borrower. The overall expectation from the new reform is to enhance risk sensitivity of capital by providing alternative measures/risk drivers and reducing the reliance on external credit assessments. In addition, the new reform aims to ensure that the credit risk weighting system will reflect the riskiness of exposures (under current practice, exposures without rating have similar weight regardless of credit risk quality) to a reasonable extent and also support better comparability across institutions. Thus, the cornerstone of the reform, which consists in finding balance between simplicity, risk sensitivity and comparability, addresses existing challenges3 associated with Basel capital framework. Interestingly, the responses of financial industry to the initial draft contained relatively similar messages regarding certain topics. For instance, it was highlighted that the reform would result in the significant increase in regulatory capital level (Comments received on the “revisions to the standardized approach for the credit risk consultative paper” BIS). Then, referring back to the recent ECB AQR exercise results, in the presence of financial ratios as common criteria, would one get the same level of NPL, provisioning and capital? Given the industry responses, do the current level of asset quality and capital adequately capture underlying risks? Finally, why only RWA? This question seems logical given that the challenges around asset classification and provisioning practices are the same. Is not the conceptual consideration finding balance between simplicity, risk sensitivity and comparability a fundamental solution from banks’ asset perspective as well? 3 Recent studies conducted by Basel Committee indicated significant variability and lower levels in RWA, especially for Banks reporting under Basel II. In retrospect, Basel II package of reforms in 2004 intended adoption of more advanced risk management practice among banks, in exchange for certain reward, particularly lower RWAs and capital requirements. The banks were granted discretion to determine regulatory capital with the use of own internal (value-at-risk) models (IRB Approach). However, the drive to make capital requirements more risk-sensitive and to benefit with lower RWA/capital levels has resulted in emergence of extremely complex risk modelling, so called Black Boxes (models for PD/LGD estimation). Complexity in turn contributed to increased variability and hindered comparability among institutions, resulted in eroded market confidence in banks risk based capital. In addition, extreme complexity promoted new opportunities for regulatory arbitrage, further strengthened with the incentives granted by Pillar 1 (IRB) to underestimate minimum capital requirements (BIS 2013, IMF 2012). - 12 - Incorporation of financial ratios into prudential definition of assets: Micro and Macro Prudential perspective September 18, 2015 3. Financial ratios: Macro foundation The recent financial crisis reinforced the importance of micro level data to financial stability monitoring and systemic risk assessment activities. Two fundamental aspects driving increased focus on granular data are corporate and household sector credit-risk facing financial institutions (asset quality review) and the channels through which private sector balance sheets structure can pose financial stability risks. The advantage of the use of the micro data, such as financial ratios is that in contrast to macro measures such as debt/GDP and DTI, micro data are granular enough to address the growing risks from certain micro trends. In addition macro measures may be misleading as they can mask relevant information regarding the distribution of the debt service burden across borrowers, especially when macro analyses entails benchmarking among peer countries. In this case relevant factors such as the variability of average term structure, income level, interest rates and overall level of development across countries are disregarded. For example a country with low credit/GDP level does not necessarily indicate less vulnerability, since this measure does not say much about how the debt service burden is distributed among borrowers. Moreover even with low credit/GDP level the debt service burden expressed in micro ratios might be higher on average as well, taking into account higher interest rates and lower term structures, common to developing countries (Nadaraia, 2014). Unlike to macro measures, financial ratios capture information on changes in the distribution of credit risk across different sectors and industries, and on the “vulnerable tails” that are thought to be relevant for the analysis of financial stability. This feature is relevant from financial cycle’s (Borio, 2010) perspective as well, as it can reliably distinguish between ‘normal’ fluctuations and trends, and systemic imbalances and serve as an early warning signal when it comes to excessive risk taking and practices of originating poorly underwritten loans. To illustrate, the cross-country comparison of the Canadian DSR distribution with the U.S. shows (Chart 3) that, in 2004, the household sector in Canada was less vulnerable to macro-economic shocks than U.S. households, expressed in fatter right-side tail of US DSR. Remarkably the distribution of DSR burden was similar for both countries in 2001. Such shifts in the shape of the US distribution can be explained by increasing risk taking in mortgage lending. In particular, the non-prime (high risk class Alta) mortgage borrowing in the US increased from 9 to 27 per cent from 2001 to 2004 (Goldman Sachs, 2007), while in Canada such loans formed a relatively small portion of new mortgage originations (Bank of Canada, 2008). - 13 - Incorporation of financial ratios into prudential definition of assets: Micro and Macro Prudential perspective September 18, 2015 Chart 3: DSR Distribution Canada vs U.S. Source: Bank of Canada (2008) Similarly, the 2008 financial crisis in Slovenia was preceded by the accumulation of excessive debt level among corporates measured by Debt/EBITDA ratio, which resulted further in corporate financial distress (Chart 4). In fact, recent empirical studies point that micro serve as a useful supplementary indicator for the financial sector. Moreover, the DSR provides a systemic banking crises at horizons of up to one 2012). information such as DSR of household sectors can build-up of vulnerabilities in the real economy and very accurate early warning signal of impending to two years in advance (Drehmann and Juselius, Chart 4: Leverage of the Slovenian corporate sector Overall debt leverage of the Slovenian corporate sector, measured by debt-to-EBITDA and debt-to-equity ratios, 2010-12 Net debt and number of firms according to debt-toEBITDA ratio, 2012 *an unsustainable debt-to-EBITDA leverage ratio exceeding 4, accounted for almost 80 per cent of total outstanding debt as of 2012 year. Source: EBRD, 2014 - 14 - Incorporation of financial ratios into prudential definition of assets: Micro and Macro Prudential perspective September 18, 2015 It is to be noted, that in the context of financial cycles, financial ratios have proven to be effective tools for dampening procyclicality and excessive risk taking or leverage. However current practice of incorporating financial ratios into the lending standard mostly applies to household sector (DSR, LTV), with some recent examples of US and Israel with targeting corporate sector as well. The application of micro data to macro analyses constitutes one way to assess overall corporate and household financial health, since their failures can have adverse effects on the economy as a whole by eroding the capital of financial institutions and the wealth of households and firms. The role of overly indebted and high risk agents in financial stability is closely associated with the phenomenon of balance sheet recession4, as it became evident in number of advanced countries during recent crisis. Thus the growing number of countries is incorporating ratio analyses into financial stability assessment. For example, in evaluating corporate sector fragility MAS incorporates financial ratio analyses, in particular leverage (Debt/equity and debt/EBITDA), ICR and liquidity (cash reserve level) ratios in combination with severe stress scenarios. For instance the Corporates which are highly leveraged both in their capital structure and in relation to earnings, i.e. corporates with debt-to-equity ratio of more than 2 times and debt-to-EBITDA ratio of more than 4 times, form 3% of all listed corporates. They account for 8% of total corporate debt. However, the share of total debt with debt/ebitda above 4 is nearly 60% of portfolio. Further, the ability of corporates to service interest expenses is measured by the ICR, and firms with ICR of less than 2, make up less than 20% of all listed corporates (Chart 5). Chart 5: Corporate soundness indicators, MAS Sources: Thomson Reuters, MAS estimates 4 Balance sheet recession is a development of the concept of “debt deflation”, introduced by Irving Fisher during the Great Depression, who believed that with the highly indebted private sector, the economy becomes vulnerable to falling into a vicious downward spiral. The concept was further extended by Richard Koo, who argues that great recession (2007), Japan’s stagnation (since 1990) and great depression (1930) have one common feature, they were driven by over indebted private sector. Notably it was bursting of a debt-financed asset bubble, followed by the substantial decrease of the net worth of large number of borrowers leaving their balance sheet deep underwater. In order to recover from negative equity position, overly indebted agents will wish to pay down debt and save more. Another common feature is that banks restrict overall credit supply and, more importantly, misallocate it due to need to repair their balance sheets. The growing evidence (US, UK, Spain, Ireland and other EU countries) suggests the larger the excess during the boom, the larger is the needed correction afterwards. Households and firms that accumulated more debt tend to cut their spending by more than those th which had less debt (BIS, 84 annual report) - 15 - Incorporation of financial ratios into prudential definition of assets: Micro and Macro Prudential perspective September 18, 2015 Under the stress scenario of 25% increase in interest costs and 25% decline in EBITDA, MAS estimates that the percentage of firms with ICRs of less than 2 will rise from 20% to 27% of all corporates; however, if the cash reserves are taken into consideration, the share of high risk firms decline to 5% (Chart 6). Chart 6: Corporate soundness indicators, MAS Sources: Thomson Reuters, MAS estimates Even though the financial ratios are becoming integral part of financial stability assessment, there are no common standards or approaches to the way this data is interpreted. For example, interpretations of corporate ratios vary among different publications of IMF. In particular, the debt at risk based on interest coverage ratio (EBIT/Interest) are set either at < 1 or <2 (ICR < 1 GFSR October, 2013; ICR <2 – GFSR October 2014; ICR <2 – Financial stability assessment of Italy; ICR < 1 – Financial stability assessment of Spain). Moreover, there is even less clarity around risk metrics. It is questionable whether sound borrowers should be able to serve principal or should interest-only servicing capacity suffice in the absence of risk. The reasons why ICR of more than 2 is not considered a major risk are neither clear, nor acceptable, especially, considering the existing low interest rate environment. Even in case of recovery, the increase in interest expenses should be relatively high when compared to the increase in earnings (Nadaraia, 2015). When risks are identified, mitigating factors, such as NPLs and capital buffers, are not discussed and follow-up recommendations and activities are lacking. For example, in its global financial stability report, the IMF provides debt distribution of EU countries by ICR ratio (Chart 7), with an unusually high level of corporate debt with ICR <2 (ICR below 2 indicating borrowers at risk). According to the data, more than 40% of debt in Germany belongs to the high risk bucket. The question is then if banks can sustain nearly half of corporate portfolio with high risks of repayment capacity, and to what extent are those risks reflected in banks’ asset quality or capital. The following question also arises: should not those exposures be impaired under IAS 39? Even though asset quality-related concerns in Germany are well identified in GFSR, this particular issue is not discussed in FSAP and Financial Stability Reports on Germany. - 16 - Incorporation of financial ratios into prudential definition of assets: Micro and Macro Prudential perspective September 18, 2015 Chart 7: Debt at risk Sources: IMF Global Financial Stability Report April 2015 When it comes to the interpretation of aggregate corporate ratios, there is no common agreement on the extent to which the cyclicality should be incorporated into the ratios. From micro perspective for example the rating methodology of credit rating agencies encompass through the cycle philosophy, where the borrowers PD measures the likelihood that a borrower will default over the future period using all available idiosyncratic information, but assuming adverse stress-scenarios, which in turn implies that issuers rating should be independent from the state of the business cycle. In addition, the severity of stress scenarios increases with the rating category, for example AAA class rating is supposed to withhold more extreme developments than B class rating (S&P). These considerations are incorporated into rating methodologies to the extent that the thresholds of financial ratios vary by business risks including cyclicality and by idiosyncratic risk factors as well. The notion of cyclicality is relevant from asset classification perspective as well, as some jurisdictions provide explicit requirements for standard assets to have ability to absorb shocks on a going concern bases. However, such definitions are too broad and the severity of shocks is up to the banks own interpretation. In fact recent studies show that the credit grading practice varies among banks in terms of cyclicality considerations. Some of them apply point in time approach (unstressed PD), while others follow TTC methodology. Even among TTC estimates, there is inconsistency in underlying assumptions, such as severity of stress scenarios (Basel) or selection of particular stress cases. To illustrate general business cycles, marked by fluctuations in overall economic activity and demand, are of only one type. Demand-driven cycles may be specific to a particular industry. Other types of cycles arise from variations in supply, as seen in the pattern of capacity expansion and retrenchment that is characteristic of the chemicals, forest products, and metals sectors. Given such inconsistency and lack of clarity, following questions arise: Should the ratios exhibit through the cycle philosophy? How severe should the stress tests be? Should the stress tests cover only idiosyncratic and sectoral risk factors, or overall macro level risks? We believe that the prudential definition of assets should entail relatively mild stress scenarios, covering both idiosyncratic and sectoral & macro factors, and should be addressed in banks’ provision levels, while more severe stress tests should be directed to capital charges. However, this topic is - 17 - Incorporation of financial ratios into prudential definition of assets: Micro and Macro Prudential perspective September 18, 2015 subject to further research and analyses, in particular to what extent should be the cyclicality addressed within asset classification and provisioning part and what should be the severity of such stress scenarios. 4. Incorporation of financial ratios into asset classification system and disclosure requirements 4.1. Incorporation of financial ratios into asset classification system To summarize, current asset classification frameworks lack consistency and comparability. In addition, existing definitions grant discretion to asset quality misinterpretation and late recognition of problem assets, as they fit banks’ business objectives. However, introduction of certain objective criteria that are beyond banks’ management control could be regarded as a solution to existing weaknesses. More specifically, with an adherence to borrower’s financial health indicators, the risks associated with the asset quality misinterpretation will be reduced and there will also be confidence that no significant risks, such as forbearance, are hidden inside the portfolio. In this part of the paper we discuss a specific solution introduced by the NBG in addressing challenges associated with the asset classification system. This solution entails the incorporation of financial ratios as an addition to the current classification framework, particularly, for standard loan definition. The empirical evidence from both micro and macro perspectives discussed in our paper shows that there is no single financial ratio or combination of financial ratios that could be regarded as the most efficient; however, ratios from leverage, coverage, profitability and liquidity groups have demonstrated significant capacity to explain the riskiness of a financial asset. Thus, in line with international experience, the NBG model entails the introduction of four financial ratios for corporates and two for household sector, namely, liquidity, ICR and leverage (debt/equity and Debt/EBITDA) for corporates and LTV and DSR for households (Appendix 2). The essential consideration here is that the model must not be interpreted as a single criterion for asset quality definition. Rather, this model is an extension to the existing framework, suggesting the incorporation of certain soft measures. This view is supported by the arguments that the reliable measurement of a borrower’s riskiness can only be inferred through the analysis of idiosyncratic and business risk factors and that financial ratios alone cannot always offer adequate understanding of a particular borrower’s riskiness. For example, when evaluating corporate exposures, factors such as competitiveness, market positioning, diversification and management quality are also relevant for estimating the degree of riskiness. Furthermore, the inclusion of financial ratios can ensure that once these ratios deteriorate (for example ICR becomes less than 2), possible increase of credit risks becomes subject to prudential attention and responses in a timely and consistent manner. In order to specify financial characteristics common to standard loans, the model provides thresholds for listed financial ratios (based on international experience), with special attention to certain credit risk characteristics, such as income level, firm size and industry type. For instance, a corporate loan shall be classified as "Standard" if the borrower’s average financial indicators meet particular criteria, e.g. ICR >=3; Debt/ EBITDA <=3.0; and Equity/Assets >=30%. However, these average indicators may - 18 - Incorporation of financial ratios into prudential definition of assets: Micro and Macro Prudential perspective September 18, 2015 vary depending on industry-specific factors and firm size (the acceptable ranges are provided in Appendix 2). Similarly, the acceptable threshold ranges for retail portfolio vary in line with the borrower’s monthly disposable income level. Further, if a bank violates the proposed benchmarks, the bank must have solid and specific arguments in terms of business and financial risks for assigning the “standard” category. This will be a powerful mechanism from the prudential perspective. For example, for exposures classified as standard, with Debt /Ebitda more than 4, banks will provide relevant explanation on how associated risks are mitigated, and whether the particular borrower holds enough cash reserves or other instruments to fulfill debt repayment within contractual terms. With the deterioration of asset quality measured by financial ratios, the banks should explain whether it is due to external developments or shifts to riskier business models. It will also work also as an early warning signal, indicating where supervisory resources should be directed. Appropriately addressing cyclicality is another relevant consideration within the asset classification framework. As we already discussed in Macro foundation part, we believe that asset quality shall exhibit resilience to relatively mild stress scenarios and cover both idiosyncratic and sectoral & macro factors, while more severe stress tests should be subject to capital charges. In this respect, the factors other than industry characteristics shall be integrated into the asset quality definition as well. Examples are severity of interest and exchange rate changes, especially for unhedged borrowers. One more additional general parameter is the term structure of the loan. More specifically, in order for a loan to be classified as “standard”, it is necessary for the borrower to have the capacity to extend the maturity of the loan in the future, giving the borrower the possibility of receiving significant concessions with regard to payments. However, it must be noted that particular stress scenarios, their severity, and overall thresholds for ratios are subject to further analyses on international level, since this particular approach is aligned to the specific characteristics of Georgian market and economy. The draft version is currently subject to consultation and discussions with the industry. The final version will reflect the results of ongoing quantitative impact study. The study is performed based not on historical data, but rather on the testing of current classification of exposures, using synthetic rating approach. In particular, the goal of QIS is to identify what share of standard exposures can be explained by this approach. The initial results are promising. QIS is being performed on IFRS-based statements as well (see the chart at the end of this appendix). In addition, liquidity ratio will be added to the list of already existing ones. As a conclusion, we believe that the incorporation of financial ratios will address a number of current challenges. Firstly, it will ensure that asset portfolios are segregated based on underlying credit risk characteristics. The ratios will limit the ability to misinterpret credit risks and reduce the reliance on banks’ own estimates, thereby, mitigating problems associated with forbearance as well. Incorporation of cyclicality considerations will address the lack of clarity and inconsistency associated with stress tests and related segregation into provisioning and capital charges. Secondly, the system will fill the gap associated with the accounting practice; in particular, the system will serve as an objective criterion for loan impairment assessment and thus, limit the banks’ discretion to underestimate credit losses. Thirdly, the incorporation of ratios into micro framework will be a powerful tool for preventing - 19 - Incorporation of financial ratios into prudential definition of assets: Micro and Macro Prudential perspective September 18, 2015 macro systemic imbalances. This bottom-up approach will grant the instrument of “where the power is” to the supervisor. The latter will result in immediate policy responses unlike the top-down approach, within which, even high quality research is often not followed by relevant actions. 4.2. Incorporation of financial ratios into disclosure requirements Credit risk disclosures are vital for understanding the institution-specific risk profile, underlying its asset portfolio, and relevant credit risk mitigation techniques for managing these risks. Consistent, comprehensive and comparable information, reported in a timely manner is of tremendous importance for complementing the supervisory review process. It also is an essential element for understanding a bank’s risk-return profile and the fair value of its assets to investors and other interested parties. During financial crisis, it became apparent that the both Pillar 3 and IFRS 7 frameworks failed to promote the identification of material risks at banks portfolio these frameworks also did not provide sufficient and comparable information to enable regulators and market participants to assess underlying risks and compare them across institutions and countries. The inconsistency and the lack of relevant information is the result of the extensive discretion that both standards granted to banks in choosing the level of detail to disclose. For example, IFRS 7 offers only high level qualitative and quantitative requirements for credit risk disclosures, and the granularity and the sort of information to be disclosed in relation to asset quality and risk concentrations (by industry, location, counterparty, and more) is up to the management’s decision. Even though Pillar 3 sets more prescribed rules for both quantitative and qualitative disclosures, it has also failed to address adequately the granularity, risk sensitivity and comparability considerations.. The revised Pillar 3 disclosures (Basel, 2015) are an attempt to address to the existing information asymmetry and comparability issues. With an objective to promote market discipline and confidence about a bank’s exposure to risk, the revised standards set more enhanced and prescribed rules credit risk disclosures; however still leaving granularity and asset quality issues partially unaddressed. To illustrate, for banks using standardized approach asset portfolios are segregated by issuer/portfolio (by Sovereign; Banks; Corporate; etc) and by risk level (past dues and Higher-risk categories (eg exposure weighted at 150% or higher risk weights reflecting the higher risks associated with these assets) form separate asset classes); While for IRB banks, the credit risk disclosure requirements are more granular. Particularly, the banks are required to further stratify asset portfolio (by Sovereign; Banks; Corporate; etc) by PD scales, like 0,0 to <0.15; 0.15 to <0.25; 0.25 to <0.50, etc. Thus, it is still unclear to what extent does the new rule address asset quality segregation issue in the context of overall corporate loan portfolio. Given the existing divergence in internal PD estimation models, there is a question mark on the comparability for IRB banks as well. To address challenges of credit risk related disclosure, we suggest integrating financial ratios by major asset classes as an addition to the current requirements. As noted, the ratios cannot cover and explain - 20 - Incorporation of financial ratios into prudential definition of assets: Micro and Macro Prudential perspective September 18, 2015 all risks associated with the bank’s assets; however they will provide meaningful indication to risks. Further, the banks will provide explanations on the role of financial ratios in credit risk management. Hence, portfolio distribution by ratios will boost general market understanding about the risks inherent in portfolios. It will also facilitate meaningful comparison of risk profile among institutions and support the evaluation process whether banks have enough capital to cover expected and unexpected credit losses. Because of its flexibility feature, incorporation of financial ratios can enable to further extend granularity by segregating portfolio by major industry segments, location or other factors accompanied with relevant ratios. Finally, it will enhance market confidence in banks asset quality and underlying risks and will further promote market discipline. Conclusion In this section we will once again shortly summarize the key problems and suggestions set forth within this paper. The paper provides the discussion around the weaknesses of existing systems for asset classification, and highlights central concerns that constitute the grounds for resulting misinterpretations and lack of comparability with regards to asset quality. We point to the need for incorporating objective criteria in the asset classification framework to address current shortfalls. For this purpose, we propose a particular solution, namely, introduction of financial ratios as an addition to the existing prudential and accounting classification frameworks. The proposed solution is based on the asset classification framework developed by National Bank of Georgia, with the objective to reduce inconsistency, lack of comparability and judgmental interpretation of asset quality. The NBG model entails introduction of four financial ratios for corporates and two for household sector, namely, liquidity, ICR and leverage (debt/equity and Debt/EBITDA) for corporates, and LTV and DSR for households. The approach used by NBG, to test synthetic rating based definition on existing exposures rather, than using historical data has its advantages. 1. It is useful in instances where historical data is absent and 2. The approach is relatively simple and more reliable, - given supervisors are confident on appropriate classification of existing standard exposures, they can relatively easily check what share is consistent with additional “ratio approach” definition of standard exposures (Chart 8). To address the challenges associated with credit risk related disclosures, market discipline and information asymmetry around the quality of banks’ loan portfolio, we also suggest that financial ratios should be integrated as an addition to the current disclosure requirements under Pillar 3. At the same time, a substantial amount of work still needs to be performed in this direction. In particular, future expansion of/elaborations on this study may include using global databases such as Orbis or Amadeus to access and analyze related comprehensive research studies on international level. Besides their incorporation into the asset classification system, internationally valid scoring models, based on financial ratios, can also be regarded as a supervisory tool to validate banks’ default probability models under Internal Ratings-Based Approach (IRB). Yet another possible suggestion is to analyze financial ratios with regard to country-specific factors, such as the level of economic development, average interest rates, term structure, country risk, and volatility in earnings across - 21 - Incorporation of financial ratios into prudential definition of assets: Micro and Macro Prudential perspective September 18, 2015 economies. In addition, special emphasis should be put on clarifying the severity of stress scenarios for the definition of standard exposures, and on defining which scenarios should be addressed via provisioning, and which - via capital. These scenarios should include not only macro, but sectorial level vulnerabilities as well. - 22 - Incorporation of financial ratios into prudential definition of assets: Micro and Macro Prudential perspective September 18, 2015 Appendix 1: Most commonly used financial ratios for predicting corporate bankruptcy Ratio Class Profitability Ratios Debt Coverage Ratio definition Expected effect on the probability of default ROA (Net Income/Total Assets) (-) Decrease ROE (Net Income/Equity) (-) Decrease EBIT/Assets (-) Decrease Operating Profit Margin (-) Decrease EBITDA/Interest Expense (-) Decrease EBIT/ Interest Expense (-) Decrease DSCR - Debt Service Coverage ratio (-) Decrease Debt/Total Assets (+) Increases Debt/EBITDA (+) Increases Current Ratio (-) Decrease Leverage Ratios Liquidity Ratios Activity Ratios Growth Variables Comments High profitability reduces the probability of default; High debt coverage decreases the probability of default; High leverage increases the probability of default; High liquidity reduces the probability of default; Cash securities to current assets (-) Decrease Interest expense/Sales (+) Increases Inventories to sales (+) Increases Accounts receivable Turnover (-) Decrease Sales Growth (+/-) See comment Asset Growth (+/-) See comment Net Sales (-) Decrease Total Assets (-) Decrease Size Variables Different activity ratios have different relationships to default; Growth variables behave like a double-edged sword: both rapid growth and rapid decline (negative growth) will tend to increase a firm’s default probability; Large firms default less often; - 23 - Incorporation of financial ratios into prudential definition of assets: Micro and Macro Prudential perspective September 18, 2015 Appendix 2: Draft addition to Asset Classification System: National Bank of Georgia5 Corporate loans In addition to the requirements outlined in the regulations on asset classification, a loan shall be classified as "Standard" if the borrower’s average financial indicators must meet the following criteria: Interest Coverage Ratio (EBIT6 / Interest Expenses) >=3; Debt/ EBITDA7 <=3.0; Equity/Assets >=30%; “State organizations”, “financial institutions” – Equity/Assets >=20%. “Pawnshop Loans” – LTV8 <90% Loans collateralized by precious metals and stones. These average indicators may vary depending on industry-specific factors. The proposed ratios shall not constitute the sole argument for deciding whether a borrower may be classified as “standard.” The availability of specifically defined ratios must not induce banks to base their assessment of borrowers solely on the NBG’s benchmarks, since this would give risk management a rather mechanical character. When assessing a borrower within a comprehensive credit analysis framework, attention must also be paid to other important indicators. If a bank violates the proposed benchmarks, it must have solid and specific arguments in terms of business and financial risks for assigning the “standard” category. Commercial banks must submit a list of such loans and the arguments for a “standard” classification to the National Bank of Georgia. Banks must use the following ratios when analyzing a borrower based on risk sectors. 5 The draft version is currently subject to consultation and discussions with industry. Final version will reflect results of ongoing quantitative impact study. The study is being performed not based on historical data, but rather on test of current classification of exposures using synthetic rating approach. In particular, the goal of QIS is to identify what share of standard exposures can be explained by this approach. The initial results are optimistic. QIS is being performed on IFRS based statements as well (see chart at the end of this appendix). Also, liquidity ratio will be added to the list of already existing ones. 6 Earnings before interest and tax. Earnings before interest, taxes, depreciation and amortization. 8 Loan to Value Ratio: The ratio of a loan to the value of an asset purchased. 7 - 24 - Incorporation of financial ratios into prudential definition of assets: Micro and Macro Prudential perspective September 18, 2015 Ratios by Sector Risk Sector Debt/EBITDA EBIT/Interest Expenses Real Estate Development Production and Trade of Construction materials Auto dealers Construction Companies (non-developers) Real Estate Management Production of Consumer Foods and Goods Hotels and Tourism Agriculture sector Restaurants Service Healthcare Production and Trade of Durable Goods Trade (other) Production and Trade of Clothes, Shoes and Textiles Pharmacy Trade of Consumer Foods and Goods Oil Importers and Retailers Telecommunication Industry Energy Other Production Other, including scrap metals 2.50 2.75 2.75 2.50 3.00 3.00 3.00 3.00 3.00 3.00 3.00 2.75 2.75 3.25 3.25 3.25 3.00 3.00 2.75 3.50 2.75 2.75 3.50 3.25 3.25 3.25 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 2.75 2.75 2.75 2.75 2.75 2.75 2.50 3.25 3.25 For small and medium loans, the debt/EBITDA ratio per sector must be 20% less than those indicated above, whereas the EBIT/interest expense ratio should exceed the figures given above by 20%. Different approaches may be used for borrowers in the micro loans portfolio, but these must be agreed with the National Bank of Georgia. “State Organizations” – Companies in this sector must be assessed in accordance with the requirements of the respective sector. The leverage indicator (Equity/Assets) must be applied in relation to those legal persons who provide public service. If a business entity operating in this sector has been granted an unconditional state guarantee, the financial data of the given entity may be scrutinized less intensively. - 25 - Incorporation of financial ratios into prudential definition of assets: Micro and Macro Prudential perspective September 18, 2015 “Pawnshop Loans” – The LTV coefficient is important for a loan portfolio collateralized by precious metals. A borrower cannot be classified as “standard” if his/her collateral does not adequately cover existing loans. When calculating the LTV coefficient, the most up-to-date market value of the collateral must be taken into consideration. “Project Financing” – In the process of project financing, the initial years of a project may not prove profitable. The bank should take into consideration benchmarks defined in accordance with the project’s average indicators. “Trade of commodities” – During commodities trading (goods that are actively traded and whose value is determined by international markets) a borrower may not meet the main benchmarks when a specific trade operation is financed. In such cases, a bank must have internal policies and procedures in place that define the adequate margin for mitigating losses caused by possible price changes. Gradation of loans to be individually assessed For the purpose of the aforementioned regulations, loans that require individual assessment must be divided into the following segments: Micro, SME, and Corporate loans. Each commercial bank is required to have internal policies and procedures that determine the characteristics of the aforementioned segments, and these must be approved by the National Bank of Georgia. The policies and procedures are required to take into account at least two parameters: the volume of loans and turnover-sales. As a rule, the volume of loans per segment, taking into consideration all related parties, should be close to the following limits: 1. <=250,000 GEL – Micro loans 2. >250,000 GEL -<=2,500,000 GEL – SME loans 3. >2,500,000 GEL – Corporate loans Additional general parameters for ”standard” loans In order for a loan to be classified as “standard”, it is necessary for the borrower to have the capacity to extend the maturity of the loan in the future, which will give him/her the possibility of receiving significant concessions with regards to payments. Granular portfolios (retail, business) Considering the absence of the complete historical data, the possible loss reserves of a granular portfolio that requires collective assessment must not be solely based on historical data, since such data might be inadequate for the current period. In parallel with historical statistics, banks must consider the rate of the portfolio’s growth (with regards to both business and retail loans) and its accompanying effects, as well as every other possible outcome that may influence the portfolio’s possible loss reserve. A bank must take this information into account as far as possible. Such risk-defining factors include, but are not limited to: - 26 - Incorporation of financial ratios into prudential definition of assets: Micro and Macro Prudential perspective September 18, 2015 Changes in the risk profile of the granular portfolio; o Payment to income, LTV and other qualitative characteristics; Changes in policies and procedures; Concentration risk; Restructurings, write-offs, and modifications of contract terms; Changes in the tendencies of the portfolio; o Days past due, nonperforming loans, etc. Changes in economic conditions; Other factors. A bank might not have specific indicators for the factors listed above, in which case it must employ professional and conservative judgment. In the granular portfolio, particular attention must be paid to those cases when the ratio of monthly payments to the monthly net income of an employed borrower, co-debtor, guarantor and his/her household exceeds the limits listed below: Monthly payments on loans/net monthly income – maximum indicator 30% 35% 40% 45% Volume of net monthly income in GEL 1000< 1000-2000 2000-4000 >4000 When conducting a collective assessment of business borrowers, the approaches for individually assessing business loans should be applied even more conservatively. When classifying a loan as “standard”, a bank must take into consideration the minimum volume of a borrower’s/group’s income. In order to classify a loan as “standard”, it is important for a borrower to have the capacity to extend the maturity of the loan in the future, which will give him/her the possibility to receive significant concessions with regards to payments. The maturity of non-collateralized consumer loans (excluding credit cards) should not exceed 48 months. In addition, the ratio of the service life of a consumer product to the maturity of the loan must also be taken into account as far as possible. Particular attention must be paid to loans collateralized by real estate, the LTV of which exceeds: 75%-80% in the case of Tbilisi, Kutaisi, Batumi, and Rustavi. 65%-70% in the case of other settlements. - 27 - Incorporation of financial ratios into prudential definition of assets: Micro and Macro Prudential perspective September 18, 2015 If these Loan-to-Value coefficient margins are violated, the liquidity of the collateral must be taken into consideration. Stress tests: In case of currency induced credit risk (CICR) the borrower is expected to absorb 15% and 10% currency shocks for USD and EUR exposures, respectively. In addition, the repayment capacity shall be stable in case of interest rate increase by 4% and 2 % for GEL and USD exposures, respectively. Chart 8: IFRS Provisioning and ICR Source: Nadaraia 2014 - 28 - Incorporation of financial ratios into prudential definition of assets: Micro and Macro Prudential perspective September 18, 2015 References: Allen D. E. & Powell R. J. (2011).Credit risk measurement methodologies. 19th International Congress on Modelling and Simulation, Perth, Australia, 12–16 December 2011 Altman E.I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance; vol. XXIII September 1968 No.4 Altman E.I. & Sabato G. (2007). Modeling Credit Risk for SMEs: Evidence from the US market. Abacus Volume 43, Issue 3, pages 332–357, September 2007. doi: 10.1111/j.1467-6281.2007.00234. Altman E.I., Iwanicz-Drozdowska M., Laitinen E. K. & Suvas A. (2014). Distressed Firm and Bankruptcy prediction in an international context: a review and empirical analysis of Altman’s Z-Score Model Bank for international settlements (2013/14). 84th Annual Report 1 April 2013–31 March 2014 Bank for international settlements (2015). Comments received on the "Revisions to the standardised approach for credit risk - consultative paper" Bank for International settlements (2015). Revised Pillar 3 disclosure requirements Banque de France (2012). Credit Ratings and Debt Crises. Banque de France Working Paper No. 396 Basel Committee on Banking Supervision (2013). The regulatory framework: balancing risk sensitivity, simplicity and comparability. Issued for comment by 11 October 2013 July 2013 Basel Committee on Banking Supervision (2015). Revisions to the Standardised Approach for credit risk. Issued for comment by 27 March 2015 Board of Governors of the Federal Reserve System Federal Deposit Insurance Corporation Office of the Comptroller of the Currency (2013). Interagency Guidance on Leveraged Lending Borio C. (2012). The financial cycle and macroeconomics: What have we learnt? Bank for International Settlements (BIS), Working Papers No 395 Campbell J. Y. & Cocco J.F. (2014). A Model of Mortgage Default Chen Y. & , Zhiyong Y. (2013). Rating Inflation versus Rating Deflation: On the Procyclical Credit Ratings. Munich Personal RePEc Archive (MPRA) Paper 51159, University Library of Munich, Germany Damijan J. P. (2014). Corporate financial soundness and its impact on firm performance: Implications for corporate debt restructuring in Slovenia. European Bank for Reconstruction and Development (EBRD), Working Paper No. 168, Prepared in May 2014 Dey S., Djoudad R. &Terajima Y. (2008). A tool for assessing financial vulnerabilities in the household Sector. Bank of Canada Review - Summer 2008 - 29 - Incorporation of financial ratios into prudential definition of assets: Micro and Macro Prudential perspective September 18, 2015 D'Hulster K., Salomao-Garcia, V. & Letelier, R. (2014). Loan classification and provisioning: current practices in 26 ECA countries. World Bank Group Working Paper Series #92831 Dietsch M. & Welter-Nicol C. (2014). Do LTV and DSTI caps make banks more resilient? Bank of France, Débats économiques et financiers N°13 Drehmann Mathias & Juselius Mikael (2012). Do debt service costs affect macroeconomic and financial stability? European Banking Authority (2013). Interim results update of the EBA review of the consistency of riskweighted assets European Banking Authority (2013). Summary report on the comparability and pro-cyclicality of capital requirements under the Internal Ratings Based Approach in accordance with Article 502 of the Capital Requirements Regulation European Central Bank (2014). Aggregate report on the comprehensive assessment, October 2014 Faruqui U.(2008). Indebtedness and the Household Financial Health: An Examination of the Canadian Debt Service Ratio Distribution. Bank of Canada, Working Paper 2008-46 Giordani P.,Jacobson T., Schedvin E.V. & Villani M. (2011). Taking the Twists into Account: Predicting Firm Bankruptcy Risk with Splines of Financial Ratios. Sveriges Riskbank Working Paper Series 256; November 2011 Griffin J. M., Nickerson J.& Tang D. Y. (2013). Rating Shopping or Catering? An Examination of the Response to Competitive Pressure for CDO Credit Ratings. Review of Financial Studies 26(9), 2270-2310 IAS 39 Financial Instruments: Recognition and Measurement - issued by IASB Jakubík P. &Teplý P. (2011). The JT Index as an indicator of financial stability of corporate sector. Prague Economic Papers Vol. 20 No. 2; DOI: 10.18267/j.pep.394 Koo R. C. (2011). The world in balance sheet recession: causes, cure, and politics. Real-world economics review, issue no. 58 Laere E.V., Vantieghem J. & Baesens B. (2012). The difference between Moody’s and S&P bank ratings: is discretion in the rating process causing a split? Risk Management Institute (RMI) Working Paper No. 12/05, Submitted: September, 2012 Le Leslé V. & Avramova S. (2012). Revisiting Risk-Weighted Assets “Why Do RWAs Differ Across Countries and What Can Be Done About It? International Monetary Fund (IMF) Working Paper WP/12/90 Lifschutz S. &Jacobi A. (2010). Predicting Bankruptcy: Evidence from Israel. International Journal of Business and Management, Vol. 5, No. 4; April 2010 - 30 - Incorporation of financial ratios into prudential definition of assets: Micro and Macro Prudential perspective September 18, 2015 Macroeconomic Surveillance Department Monetary Authority of Singapore (2014). Financial stability review. Published in November 2014, ISSN 1793-3463 Mayer C. J. , Pence K. M. & Sherlund S. M. (2008). The Rise in Mortgage Defaults. Finance and Economics Discussion Series Divisions of Research & Statistics and Monetary Affairs Federal Reserve Board, Washington, D.C. Nadaaia O. (2014). How to Determine Local Efficient Lending Standards. National Bank of Georgia, International Conference "The Lending Standards", January 31, 2014, Tbilisi, Georgia Nadaaia O. (2015). Some Areas of FSAP Practice from International and Georgian Perspective. National Bank of Georgia, IMF – World Bank Constituency Meeting 2015 FSAPs in the Context of a Changing World; Sofia, May 2015 National bank of Georgia (2014). Regulation on Assets Classification and the Creation and Use of Reserves for Losses by Commercial Banks. Decree N 51/04 of the Governor of the National Bank of Georgia; June 17, 2014 Standard & Poor's Financial (2010). The Time Dimension Of Standard & Poor’s Credit Ratings. McGraw Hill Companies Standard & Poors Rating Services (2013). S&P - Ratings Direct - Corporate Methodology. McGraw Hill Financial Wang Y. & Campbell M. (2010). Do Bankruptcy Models Really have predictive Ability? Evidence using China Publicly Listed Companies. International Management Review, Vol. 6 No. 2 2010 - 31 -
