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.

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.

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.

Internal credit rating systems: methodology and economic value

2009 ◽  
Vol 3 (2) ◽  
pp. 11-34 ◽  
Author(s):  
Radu Neagu ◽  
Sean Keenan ◽  
Kete Chalermkraivuth
Keyword(s):  
Credit Rating ◽  
Economic Value ◽  
Rating Systems ◽  
Systems Methodology

EXPRESS: Households under Economic Change: How Micro- and Macroeconomic Conditions Shape Grocery Shopping Behavior

2021 ◽  
pp. 002224292110368
Author(s):  
Thomas P. Scholdra ◽  
Julian R. K. Wichmann ◽  
Maik Eisenbeiss ◽  
Werner J. Reinartz
Keyword(s):  
Business Cycle ◽  
Shopping Behavior ◽  
Economic Conditions ◽  
Grocery Shopping ◽  
National Brands ◽  
Macroeconomic Conditions ◽  
Spending Behavior ◽  
Shopping Basket ◽  
The Business Cycle ◽  
Structural Shifts

Economic conditions may significantly affect households' shopping behavior and, by extension, retailers' and manufacturers' firm performance. By explicitly distinguishing between two basic types of economic conditions—micro conditions in terms of households' personal income and macro conditions in terms of the business cycle—this study analyzes how households adjust their grocery shopping behavior. The authors observe more than 5,000 households over eight years and analyze shopping outcomes in terms of what, where, and how much they shop and spend. Results show that micro and macro conditions substantially influence shopping outcomes, but in very different ways. Microeconomic changes lead households to adjust primarily their overall purchase volume—that is, after losing income, households buy fewer products and spend less in total. In contrast, macroeconomic changes cause pronounced structural shifts in households' shopping basket allocation and spending behavior. Specifically, during contractions, households shift purchases toward private labels while also buying and consequently spending more than during expansions. During expansions, however, households increasingly purchase national brands but keep their total spending constant. The authors discuss psychological and sociological mechanisms that can explain the differential effects of micro and macro conditions on shopping behavior and develop important diagnostic and normative implications for retailers and manufacturers.

scholarly journals Credit concession through credit scoring: Analysis and application proposal

2017 ◽  
Vol 13 (1) ◽  
pp. 51 ◽  
Author(s):  
Oriol Amat ◽  
Raffaele Manini ◽  
Marcos Antón Renart
Keyword(s):  
Financial Institutions ◽  
Design Methodology ◽  
Credit Scoring ◽  
Real Data ◽  
Statistical Techniques ◽  
Probability Of Default ◽  
Relative Importance ◽  
Linear Discriminant ◽  
Scoring Model ◽  
Credit Scoring Model

Purpose: The study herein develops and tests a credit scoring model which can help financial institutions in assessing credit requests. Design/methodology/approach: The empirical study has the objective of answering two questions: (1) Which ratios better discriminate the companies based on their being solvent or insolvent? and (2) What is the relative importance of these ratios? To do this, several statistical techniques with a multifactorial focus have been used (Multivariate Analysis of Variance, Linear Discriminant Analysis, Logit and Probit Models). Several samples of companies have been used in order to obtain and to test the model. Findings: Through the application of several statistical techniques, the credit scoring model has been proved to be effective in discriminating between good and bad creditors. Research limitations:  This study focuses on manufacturing, commercial and services companies of all sizes in Spain; Therefore, the conclusions may differ for other geographical locations.Practical implications:  Because credit is one of the main drivers of growth, a solid credit scoring model can help financial institutions assessing to whom to grant credit and to whom not to grant credit.Social implications: Because of the growing importance of credit for our society and the fear of granting it due to the latest financial turmoil, a solid credit scoring model can strengthen the trust toward the financial institutions assessment’s. Originality/value: There is already a stream of literature related to credit scoring. However, this paper focuses on Spanish firms and proves the results of our model based on real data. The application of the model to detect the probability of default in loans is original.

scholarly journals Fiscal fault, financial fix? Capital Markets Union and the quest for macroeconomic stabilization in the Euro Area

2018 ◽  
Vol 22 (2) ◽  
pp. 117-138 ◽  
Author(s):  
Benjamin Braun ◽  
Marina Hübner
Keyword(s):  
Capital Markets ◽  
Governance Structure ◽  
Macroeconomic Stabilization ◽  
European Monetary ◽  
Macroeconomic Conditions ◽  
Structural Capacity ◽  
Capital Markets Union ◽  
Fiscal Austerity ◽  
The Business Cycle ◽  
The Impact

This article seeks to situate and explain the European Union’s push for a Capital Markets Union – and thus for a more market-based financial system – in the broader context of macroeconomic governance in politically fractured polities. The current governance structure of the European Monetary Union severely limits the capacity of both national and supranational actors to provide a core public good: macroeconomic stabilization. While member states have institutionalized fiscal austerity and abandoned other macroeconomic levers, the European polity lacks the fiscal resources necessary to achieve stable macroeconomic conditions – smoothing the business cycle, ensuring growth and job creation and mitigating the impact of output shocks on consumption. Capital Markets Union, we argue, is the attempt of European policymakers to devise a financial fix to this structural capacity gap. Using its regulatory powers, the Commission, supported by the European Central Bank (ECB), seeks to harness private financial markets and instruments to provide the public policy good of macroeconomic stabilization. We trace how technocrats, think tanks, and financial-sector lobbyists, through the strategic use of knowledge and expertise, established securitization and market-based finance as solutions to the European Monetary Union’s governance problems.

IFRS 9 Expected Loss: A Model Proposal for Estimating the Probability of Default for Non-Rated Companies

2019 ◽  
Author(s):  
David Delgado-Vaquero ◽  
José Morales-Díaz ◽  
Constancio Zamora-Ramírez
Keyword(s):  
Probability Of Default ◽  
Expected Loss ◽  
Ifrs 9
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