scholarly journals IFRS 9 Expected Loss: A Model Proposal for Estimating the Probability of Default for non-rated companies

2020 ◽  
Vol 23 (2) ◽  
pp. 180-196
Author(s):  
David Delgado-Vaquero ◽  
José Morales-Díaz ◽  
Constancio Zamora-Ramírez
Keyword(s):  
Credit Rating ◽  
Fair Value ◽  
Financial Ratios ◽  
Financial Assets ◽  
Probability Of Default ◽  
Historical Information ◽  
Rating Agencies ◽  
Model Reliability ◽  
Ifrs 9 ◽  
Positive Results

Bajo el modelo de provisiones por riesgo de crédito de la NIIF 9, las empresas deben estimar una Probabilidad de Default o quiebra (PD) para todos los activos financieros (y otros elementos) no valorados a valor razonable con cambios en la cuenta de resultados. Existen varias metodologías para estimar dicha PD utilizando información histórica o de mercado. No obstante, en algunos casos las empresas no disponen de información histórica o de mercado acerca de una contraparte. Para estos casos proponemos un modelo denominado Financial Ratios Scoring (FRS), a través del cual la entidad puede obtener un rating interno de la contraparte como primer paso para estimar la PD. El modelo se diferencia de otros modelos recientes en varios aspectos como, por ejemplo, el tamaño de la base de datos o el hecho de que se enfoca en empresas sin rating. Se basa en dar una puntuación a la contraparte en función de sus ratios financieros clave. La puntuación sitúa a la empresa en un percentil dentro de una distribución del sector previamente construida utilizando empresas con rating oficial u ofrecido por vendors. Hemos analizado la fiabilidad del modelo calculando el rating interno para empresas con rating oficial y hemos comparado el rating interno con el oficial, obteniendo resultados positivos. Under the IFRS 9 impairment model, entities must estimate the PD (Probability of Default) for all financial assets (and other elements) not measured at fair value through profit or loss. There are several methodologies for estimating this PD from market or historical information. However, in some cases entities do not possess market or historical information concerning a counterparty. For such cases, we propose a model called Financial Ratios Scoring (FRS), by means of which an entity can obtain a “shadow rating” for a counterparty as a first step in estimating the PD. The model differentiates from other recent models in several aspects, such as the size of the database and the fact that it is focused on non-rated companies, for example. It is based on scoring the counterparty according to its key financial ratios. The score will place the counterparty on a percentile within a previously constructed sector distribution using companies with a credit rating published by rating agencies or financial vendors. We have tested the model reliability by calculating the internal credit rating of several companies (which have an official/quoted credit rating), and by comparing the rating obtained with the official one, and obtained positive results.

Estimating Credit Rating and Transition Matrix of Savings Bank Industry Based upon IRB-Approach

2005 ◽  
Vol 13 (2) ◽  
pp. 61-85
Author(s):  
Myung Jig Kim ◽  
Sung Hwan Shin ◽  
Hong Sun Song
Keyword(s):  
Transition Matrix ◽  
Credit Rating ◽  
Survival Function ◽  
Financial Ratios ◽  
Probability Of Default ◽  
Rating Agencies ◽  
Savings Bank ◽  
New Basel Capital Accord ◽  
Irb Approach ◽  
Rating Category

This paper proposes a method that estimates credit ratings by mapping empirical probability of default (PD) and standardized historical financial ratios. Unlike standard approaches such as the parametric logit model. discriminant analysis. neural network. and survival function model. the proposed approach has an advantage of offering a multiple credit rating categories. as opposed to either default or not default. of obligors. It would provide an useful information to practitioners because the probability of default for each credit rating category is a critical input under New Basel Capital Accord. Emoirical results based upon the historical PD and financial ratios of Korean savings bank industry from 2000 and 2003 suggest that the industry’s average credit rating belong to a speculative grade. that is BB and below. In addition, the computed transition matrix indicates that volatility of transition matrix fluctuates substantially each year and the orobability of staying in the same rating category at the end of the year tended to be much smaller than the average reported by the rating agencies for the overall Korean companies. The proposed method can easily be applied to industries other than savings bank industry.

scholarly journals The Effects of Employee Stock Options on Credit Ratings

2008 ◽  
Vol 83 (5) ◽  
pp. 1273-1314 ◽  
Author(s):  
Yen-Jung Lee
Keyword(s):  
Stock Options ◽  
Credit Rating ◽  
Credit Ratings ◽  
Fair Value ◽  
Cash Flows ◽  
Share Repurchases ◽  
Credit Rating Agencies ◽  
Employee Stock Options ◽  
Rating Agencies ◽  
Unfavorable Effect

ABSTRACT: This paper examines whether outstanding employee stock options (ESOs), which represent the firm’s contractual obligation to deliver shares upon ESO exercise, affect firms’ credit ratings. I hypothesize that outstanding ESOs play two information roles—(1) suggesting equity infusion, and (2) predicting share repurchases—that help credit-rating agencies evaluate the issuing company’s debt service ability. Consistent with these hypothesized roles, results indicate that the present values of expected cash proceeds and tax benefits from ESO exercise have favorable effects on credit ratings. In contrast, the present value of the expected cost of ESO-related share repurchases has an unfavorable effect on credit ratings and this unfavorable effect is more pronounced for firms with a greater tendency to repurchase shares. The after-tax fair value of outstanding ESOs, which summarizes the effects of the above three ESO-related cash flows, is negatively associated with credit ratings. Taken together, these findings are consistent with credit-rating agencies incorporating the information conveyed by outstanding ESOs regarding potential equity infusion and ESO-related repurchases in their credit risk assessments and assigning lower credit ratings to firms with greater values of outstanding ESOs.

scholarly journals Dynamic Mapping of Probability of Default and Credit Ratings of Russian Banks

2020 ◽  
Vol 14 (4) ◽  
pp. 31-46
Author(s):  
Ella Khromova
Keyword(s):  
Credit Risk ◽  
Credit Rating ◽  
Credit Ratings ◽  
Quantitative Measure ◽  
Optimal Time ◽  
Investment Horizon ◽  
Investment Strategies ◽  
Probability Of Default ◽  
Rating Agencies ◽  
Dynamic Mapping

Investors are interested in a quantitative measure of banks’ credit risk. This paper maps the credit ratings of Russian banks to default probabilities for different time horizons by constructing an empirical dynamic calibration scale. As such, we construct a dynamic scale of credit risk calibration to the probability of default (PD).Our study is based on a random sample of 395 Russian banks (86 of which defaulted) for the period of 2007-2017. The scale proposed by this paper has three features which distinguish it from existing scales: dynamic nature (quarterly probability of default estimates), compatibility with all rating agencies (base scale credit ratings), and a focus on Russian banks.Our results indicate that banks with high ratings are more stable just after the rating assignment, while a speculative bank’s probability of default decreases over time. Hence, we conclude that investors should account for not only the current rating grade of a bank, but also how long ago it was assigned. As a result, a rising capital strategy was formulated: the better a bank’s credit rating, the shorter the investment horizon should be and the closer the date of investment should be to the rating assignment date in order to minimise credit risk.The scientific novelty of this paper arises from the process of calibration of a rating grade to dynamic PD in order to evaluate the optimal time horizon of investments into a bank in each rating class. In practical terms, investors may use this scale not only to obtain a desired credit rating, but also to identify quantitative measure of credit risk, which will help to plan investment strategies and to calculate expected losses.

Credit Ratings and Rating Agencies

2020 ◽  
Author(s):  
Miles Livingston ◽  
Lei Zhou
Keyword(s):  
Credit Rating ◽  
Corporate Bonds ◽  
The United States ◽  
Corporate Bond ◽  
Insurance Companies ◽  
High Yield ◽  
Credit Rating Agencies ◽  
Probability Of Default ◽  
Rating Agencies ◽  
Bond Issues

Credit rating agencies have developed as an information intermediary in the credit market because there are very large numbers of bonds outstanding with many different features. The Securities Industry and Financial Markets Association reports over $20 trillion of corporate bonds, mortgaged-backed securities, and asset-backed securities in the United States. The vast size of the bond markets, the number of different bond issues, and the complexity of these securities result in a massive amount of information for potential investors to evaluate. The magnitude of the information creates the need for independent companies to provide objective evaluations of the ability of bond issuers to pay their contractually binding obligations. The result is credit rating agencies (CRAs), private companies that monitor debt securities/issuers and provide information to investors about the potential default risk of individual bond issues and issuing firms. Rating agencies provide ratings for many types of debt instruments including corporate bonds, debt instruments backed by assets such as mortgages (mortgage-backed securities), short-term debt of corporations, municipal government debt, and debt issued by central governments (sovereign bonds). The three largest rating agencies are Moody’s, Standard & Poor’s, and Fitch. These agencies provide ratings that are indicators of the relative probability of default. Bonds with the highest rating of AAA have very low probabilities of default and consequently the yields on these bonds are relatively low. As the ratings decline, the probability of default increases and the bond yields increase. Ratings are important to institutional investors such as insurance companies, pension funds, and mutual funds. These large investors are often restricted to purchasing exclusively or primarily bonds in the highest rating categories. Consequently, the highest ratings are usually called investment grade. The lower ratings are usually designated as high-yield or “junk bonds.” There is a controversy about the possibility of inflated ratings. Since issuers pay rating agencies for providing ratings, there may be an incentive for the rating agencies to provide inflated ratings in exchange for fees. In the U.S. corporate bond market, at least two and often three agencies provide ratings. Multiple ratings make it difficult for one rating agency to provide inflated ratings. Rating agencies are regulated by the Securities and Exchange Commission to ensure that agencies follow reasonable procedures.

Credit Rating and Its Interaction With Financial Ratios

2019 ◽  
pp. 251-268
Author(s):  
Shraddha Mishra ◽  
Reenu Bansal
Keyword(s):  
Credit Rating ◽  
Multinomial Logistic Regression ◽  
Financial Ratios ◽  
Rating Agencies ◽  
Discriminating Power ◽  
Time Period ◽  
Tax Returns ◽  
Equity Ratio ◽  
Credit Worthiness ◽  
The Relationship

Credit rating evaluates credit worthiness of corporate and securities issued by government. It provides investors with unbiased reviews and opinion about the credit risk of various securities. The main aim of the chapter is to identify the relationship between the financial ratios and rating symbols. The sample of 158 firms is taken into consideration that discriminates best ratings given by credit rating firms. In order to examine the variability in ratings issued by various rating agencies, the time period of eight years starting from April 2009 to March 2017 has been selected. The study employed the multinomial logistic regression model to explain the relationship among the variables. The analysis suggests that variables such as debt to equity ratio, profit after tax, returns on capital employed, and return on net worth are those having the highest impact on ratings and thus there is also discriminating power among Indian rating agencies.

scholarly journals ANALISIS MODEL PREDIKSI PEMERINGKATAN OBLIGASI SYARIAH PERUSAHAAN DENGAN PENDEKATAN RASIO KEUANGAN DI BURSA EFEK INDONESIA

2014 ◽  
Vol 10 (2) ◽  
Author(s):  
Karima Tamara
Keyword(s):  
Prediction Model ◽  
Stock Exchange ◽  
Research Question ◽  
Financial Ratios ◽  
Probability Of Default ◽  
Bond Ratings ◽  
Bond Rating ◽  
Financial Ratio ◽  
Rating Agencies ◽  
Formed Model

This study aimed to determine whether financial ratio was a predictor to predict future bond ratings, and which one was significant. This study concentrates on techniques to predict bond ratings. Bond rating is important because these rankings provide an informative statement and provide a signal about the probability of default of a company's debt. The Islamic bond companies listed in Indonesia Stock Exchange (IDX) and assessed by rating agencies PEFINDO in 2009- 2011 were used to answer the research question. Leverage (LTLTA), liquidity (CaCl), solvency (CFOTL), profitability (OIS), and productivity (STA) were used as variable of financial ratio. This study examined the financial ratios that can establish sharia bond rating prediction model using discriminant analysis (MDA). The results were : (1) there were different level of Islamic bond in financial ratios of liquidity (CaCl), solvency (CFOTL), profitability (OIS), and productivity (STA); (2) financial ratios, that could form the prediction model of Islamic bonds ranking, were CaCl , OIS , STA, (3) the accuracy level of the formed model were 94.3%.

scholarly journals DEBTOR RATING AS A TOOL OF DEBT MANAGEMENT

2020 ◽  
pp. 121-129
Author(s):  
I. Hanus ◽  
I. Plikus ◽  
T. Zhukova
Keyword(s):  
Credit Risk ◽  
Default Risk ◽  
Credit Rating ◽  
Average Rate ◽  
Credit Ratings ◽  
Default Probability ◽  
Probability Of Default ◽  
Corporate Default ◽  
Ifrs 9 ◽  
Credit Losses

IFRS 9 “Financial Instruments” introduced a new model of impairment based on expected credit losses, in which the impairment is based on expected credit losses, and the provision for losses is recognized before the credit loss, i.e. companies recognize losses immediately after initial recognition of the financial asset and revise the amount of the provision for expected credit losses at the reporting date. To create a provision for credit losses, IFRS 9 allows using several practical tools, including the rating debtors’ method. However, IFRS 9 does not express a clear opinion on how the expected credit loss for receivables should be calculated. In this regard, in our opinion, it is possible to apply an individual approach to the choice of credit risk assessment method, determining the debtor’s credit rating and the choice of the default probability, and so on. The paper substantiates that the debtors’ rating by the level of corporate default risk is a method that can reliably assess the probable risks. This method uses credit ratings. The paper proposes using the international credit ratings, which will allow a more objective creditworthiness assessment of both foreign and domestic debtors, taking into account macroeconomic factors used by rating agencies to determine the class of credit risk. The article presents the credit rating of Ukraine and changes in the credit rating of Ukraine for 15 years (2004-2019), shows the model of applying the international default probability rates. Two variants of applying this model are offered. Under the first option, the total amount of receivables from the counterparty / group of debtors is multiplied by the percentage of default probability. The second option involves applying the selected ratio according to the credit rating class at the last stage of calculating the expected credit losses by the simplified method. Due to the fact that there is no single approach to choosing the probability of default and everything relies on expert opinion, we propose using the data of the Annual Global Corporate Default And Rating, which is an analysis of market conditions in the world, the corporate defaults overview, the coefficient of bankruptcy probability of economic entities for each of the risk groups. The paper proposes using the annual rate of corporate defaults, as the expected credit losses must be calculated by companies on a regular basis and revalued at least once a year (on the balance sheet date). It is substantiated that the use of the average rate (Average Rate) to assess the probability of default, it is this rate that takes into account the past experience of companies that are in the corresponding zone of default risk for all the periods under consideration.

Credit Scoring – General Approach in the IFRS 9 Context

2021 ◽  
Vol 19 (162) ◽  
pp. 384-396
Author(s):  
Luminiţa-Georgiana ACHIM ◽  
Elena MITOI ◽  
Valentin MOLDOVEANU ◽  
Codrut-Ioan TURLEA ◽  
Keyword(s):  
Credit Rating ◽  
Credit Scoring ◽  
Probability Of Default ◽  
Rating Systems ◽  
Macroeconomic Conditions ◽  
Time Estimates ◽  
Ifrs 9 ◽  
The Business Cycle ◽  
Past Experiences ◽  
Validation Tests

With the coming into force of the standard IFRS 9 – Financial Instruments, in January 2018, financial institutions passed from an incurred loss model to a forward-looking model for the computation of impairment losses. As such, the IFRS 9 models use point-in-time, estimates of Probability of Default and Loss Given Default and provide a more faithful representation of the credit risk at a given as they are based on past experiences as well as the most recent and forecasted economic conditions. However, given the short-term fluctuations in the macroeconomic conditions, the final outcome of the Expected credit loss models is highly volatile due to their sensitivity to the business cycle. With regard to Probability of Default estimation under IFRS 9, the most commonly methods are: Markov Chains, Survival Analysis and single-factor models (Vasicek and Z-Shift). The development of the score-cards is still the same as in the case of the Internal Ratings Based Probability of Default models, encouraging institutions to use the already available credit rating systems and perform adjustment to the calibration. This paper outlines a non-exhaustive list of quantitative validation tests would satisfy the requirements of the IFRS 9 standard.

Effectiveness of Government Regulation of the Banking Sector: Assessment and Rating of Banks

2020 ◽  
pp. 85-95
Author(s):  
Halyna O. Kryshtal
Keyword(s):  
Government Regulation ◽  
Banking Sector ◽  
Credit Rating ◽  
Analytical Data ◽  
The State ◽  
Economic Sector ◽  
Rating Agencies ◽  
Depth Study ◽  
State Regulator ◽  
Almost All

The article deals with the causes of the negative situation in the banking sector, as the state of the bank depends on the analysis of almost all aspects of banking activity for some time. It is determined that during the banking sector audits, the state regulator uses analytical data on the banking sector's operations with its monetary obligations, compliance with maturities and maturities of assets that operate and terms and amounts of liabilities, namely, dealing with banking sector liquidity. As their financial reliability is important in the banking sector, therefore, bank clients are a socio-economic sector, needing an objective and independent assessment, as reliability directly affects the socio-economic development of the country. The banking sector was analyzed in 2016-2019 and it was found that during this period violations of laws and regulations issued by the state regulator were made in the banking sector. A number of penalties, written warnings and administrative penalties were applied by the state regulator. The method of determining the rating of banks in respect of which penalties were applied by the state regulator is proposed. The rating allows investors and potential clients to understand the situation in the banking market and helps banks identify their weaknesses and correct their work. The application of the proposed economic and mathematical model in the rating of participants in the banking sector can have a positive effect on: improving the quality of management in the banking sector and transparency in the activities of each individual bank; standardization of technologies of rating of the banking sector under the prism of the applied sanctions by the state regulator. Therefore, there is a need for an in-depth study of the techniques used by credit rating agencies in the banking sector and the identification of the main problems in establishing the rating of the banking sector. Key words: banking sector, state regulator, economic sector, efficiency, rating, rating, social sector.

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