International Effect on Family SME Financial Distress Prediction

Understanding the reasons of default risk is crucial to avoid the firm's bankruptcy. The purpose of this work is to analyze the impact of internationalization on firm's probability of distress. For it, this chapter aims to propose a model to predict default specific to family SMEs (small and medium enterprises). An unbalanced panel of 10,832 firms over the period from 2012-2018 is analyzed. Ex-ante criteria to classify firms in default or compliant is used. International SMEs have lower probability of default than domestic firms, and compliant firms export more. Results show that export ratio is an important determinant of the probability of default. Moreover, the ratios of liquidity, profitability, size, leverage, efficiency, cash flow, and age are also relevant. Moreover, these ratios explain default risk of both groups international and domestic SMEs. The proposed model has an accuracy of 92.9%, which increases to 95.6% if only export SMEs are analyzed.

MODELING LIFETIME EXPECTED CREDIT LOSSES ON BANK LOANS

The guidelines of various Accounting Standards require every financial institution to measure lifetime expected credit losses (LECLs) on every instrument, and to determine at each reporting date if there has been a significant increase in credit risk since its inception. This paper models LECLs on bank loans given to a firm that has promised to repay debt at multiple points over the lifetime of the contract. The LECL can be written as a sum of ECLs (estimated at reporting date) incurred at debt repayment times. The ECL at any debt repayment time can be written as a product of the probability of default (PD), the expected value of loss given default and the exposure at default. We derive a stochastic dynamical equation for the value of the firm’s asset by incorporating the dynamics of the factors. Also, we show how the LECL and the term structure of the PD can be estimated by solving a Black–Scholes–Merton like partial differential equation. As an illustration, we present the numerical results for the various credit loss indicators of a fictitious firm when the dynamics of the short-term interest rate is characterized by a Cox–Ingersoll–Ross mean-reverting process.

ASSESSMENT OF THE QUALITY OF THE ASSET POOL FORMED FOR SECURITIZATION IN THE CONTEXT OF ANALYTICAL PROVISION OF THE MANAGEMENT SYSTEM

The lack of an effective methodology for the analysis of asset securitization operations causes problems in the formation of a strategy for managing the process of financing the activities of the initiator of the securitization. The article substantiates the areas of analytical support of the management system in terms of asset securitization: the formation of financial and economic justification of the feasibility and feasibility of securitization of assets (study of potential investors and analysis of prospects for placement of securities; diligence); assessment of the original assets of the originator for the possibility of their securitization; identification of the impact of securitization of assets on the financial performance of the originator; assessment of risks associated with the use of securitization of assets); analysis of the quality of the asset pool generated by the originator for securitization; evaluation of the effectiveness of securitization operations for its initiator. It is also proposed to analyze the quality of the pool of assets generated by the originator for securitization, in the following sequence: assessment of the probability of default and the amount of expected losses on the pool of assets to be securitized; identification of factors influencing the early repayment of pool assets; assessment of the parameters of the formed pool of securitized assets, based on the analysis of its structure and the selected system of indicators; modeling of future payment flows in the pool of securitized assets, which provides the formation of analytical information to decide on the possibility of securitization using the assets available in the originator and diversification of risks through optimal concentration of assets.

Climate Risk with Particular Emphasis on the Relationship with Credit-Risk Assessment: What We Learn from Poland

This research seeks to identify non-financial enterprises exposed to the climate risk relating to transition risks and at the same time use of bank loans, as well as to conduct stress tests to take account of the financial risk related to climate change. The workflow through which to determine the ability of the banking sector to assess the potential impact of climate risk entails parts based around economic sector and company level. The procedure based on the sectoral level identifies vulnerable economic sectors (in the Sectoral Module), while the procedure based on company level (the Company Module) refers to scenarios presented in stress tests to estimate the probability of default under stressful conditions related to the introduction of a direct carbon tax. The introduction of the average direct carbon tax (EUR 75/tCO2) in fact results in increased expenditure and reduced sales revenues among enterprises from sectors with a high CO2 impact, with the result being a decrease in the profitability of enterprises, along with a simultaneously higher level of debt; an increase in the probability of default (PD) from 3.6%, at the end of 2020 in the baseline macroeconomic scenario, to between 6.31% and 10.12%; and increased commercial bank capital requirements. Financial institutions should thus use PD under stressful conditions relating to climate risk as suggestions to downgrade under the expert module.

COVID-19 pandemic risk and probability of loan default: evidence from marketplace lending market

As the COVID-19 pandemic adversely affects the financial markets, a better understanding of the lending dynamics of a successful marketplace is necessary under the conditions of financial distress. Using the loan book database of Mintos (Latvia) and employing logit regression method, we provide evidence of the pandemic-induced exposure to default risk in the marketplace lending market. Our analysis indicates that the probability of default increases from 0.056 in the pre-pandemic period to 0.079 in the post-pandemic period. COVID-19 pandemic has a significant impact on default risk during May and June of 2020. We also find that the magnitude of the impact of COVID-19 risk is higher for borrowers with lower credit ratings and in countries with low levels of FinTech adoption. Our main findings are robust to sample selection bias allowing for a better understanding of and quantifying risks related to FinTech loans during the pandemic and periods of overall economic distress.

Impact of COVID-19 on the Robustness of the Probability of Default Estimation Model

Probability of default (PD) estimation is essential to the calculation of expected credit loss under the Basel III framework and the International Financial Reporting Standard 9. Gross domestic product (GDP) growth has been adopted as a key determinant in PD estimation models. However, PD models with a GDP covariate may not perform well under aberrant (i.e., outlier) conditions such as the COVID-19 pandemic. This study explored the robustness of a PD model with a GDP determinant (the test model) in comparison with that of a PD model with a credit default swap index (CDX) determinant (the alternative model). The test model had a significantly greater ratio of increase in Akaike information criterion than the alternative model in comparisons of the fit performance of models including 2020 data with that of models excluding 2020 data (i.e., that do not cover the COVID-19 pandemic). Furthermore, the Cook’s distance of the 2020 data of the test model was significantly greater than that of the alternative model. Therefore, the test model exhibited a serious robustness issue in outlier scenarios, such as the COVID-19 pandemic, whereas the alternative model was more robust. This finding opens the prospect for the CDX to potentially serve as an alternative to GDP in PD estimation models.

Probabilistic Calibration and Genetic Algorithm-based Bank Credit Strategies for MSMEs and Enlightenment to Tobacco Enterprise Management

Objectives: To further strengthen the role of Micro, Small and Medium Enterprises (MSMEs) in maintaining the vitality of national economy, governments around the world introduced many special policies. They kept guiding the banking industry to increase the support for MSMEs and reduce their financing difficulties in banks. Basing on the analysis of the bank's credit strategy for small and medium-sized enterprises of similar size, this paper gives the management strategy for small and medium-sized enterprises in tobacco industry to obtain bank credit when they cannot expand their turnover. In this paper, we proposed a binary classification model-based probabilistic calibration algorithm to calculate the default probability of enterprises in the formation of risk measurement model, and found the optimal solution of credit strategy using an improved genetic algorithm. Firstly, we discovered the enterprise’s information and invoice data of 123 micro and medium-sized enterprises with existing credit ratings. We extracted several features from multiple perspectives, such as size, relationship in supply chain, profitability, performance ability, and level of development, and removed the correlations among the indicators using principal component analysis (PCA). Secondly, the retained principal components were used as covariates, and we determined the credit ratings of the firms and the probability of default using discrete variables such as the credit ratings of the firms and whether they defaulted. Finally, we substituted the probability of default into the credit risk model to calculate the loss expectation and profit expectation of the credit portfolio, and used the profit expectation of the credit portfolio as the objective function of the 0-1 programming equation to derive the credit strategy with the lowest risk exposure and the highest return basing on the genetic algorithm.

DETERMINANTS OF INTEREST RATES ON CORPORATE DEBT

The objective of this article is theoretical and methodological justifying of determining algorithm of the cost of debt capital for enterprises functioning in emerging markets (EM). The methods of research: analysis and synthesis, system analysis, comparative analysis, empirical and statistical methods, factor analysis. Results. In this article key determinants of interest rates on debt capital for enterprises and their impact on the procedure of discount rate calculation are determined. The issue of the cost of debt calculation of enterprises in condition of absence of complete information concerning systematic and non-systematic crediting risks is studied. Differences between interest rate on the loan fixed in credit agreement and expected by creditors the cost of debt are identified. It is determined that the key factor impacting the deviation level of market value of debt capital from the nominal, and respectively, deviation of the cost of debt from the cost of capital is probability of default (PD). At the minimum values of PD, the contract interest rate corresponds to the rate of cost of debt and it is advisable to use it for discount rate calculation. Critical analysis of alternative methodological approaches of the cost of debt calculation is made. Ways of integrating of market information concerning credit default swaps into the process of expected cost of debt calculation are justified. Factors of shadowing of rates of the cost of debt and ways of reducing of shadow transactions’ level in the credit market are identified. Conclusions. At high PD values, expected by market premium for default risk may exceed the contract interest rate, which necessitates constant monitoring of credit risks and appropriate adaptation of interest rates. In the paper the algorithm of such adaptation are proposed. It is shown that in the case of non-use of interest rates adjustment taking into account changes in PD, CDS and LGD, premium for creditors’ systematic risk can differ significantly from market values of similar enterprises (peer-group), and estimated value of the cost of debt can acquire negative values. Contract (promised) interest rate should be set in such way that the premium for systematic risk of providing debt capital will be at the level of similar companies and does not change significantly as a result of probability of default changes. If in practice the opposite situation occurs, it is the evidence of contract interest rate shadowing, absence of effective system of assessment and management of credit risks. For solving the problem of interest rate transparency and filling of information gaps concerning PD borrowers in EM countries, should intensify CDS market. Keywords: debt capital, default probability, non-performing loans, credit default swap, credit spread, debt capital premium, shadow economy. JEL Classification E47 Formulas: 16; fig.: 0; tabl.: 3; bibl.: 15.

SHAP and LIME: An Evaluation of Discriminative Power in Credit Risk

In credit risk estimation, the most important element is obtaining a probability of default as close as possible to the effective risk. This effort quickly prompted new, powerful algorithms that reach a far higher accuracy, but at the cost of losing intelligibility, such as Gradient Boosting or ensemble methods. These models are usually referred to as “black-boxes”, implying that you know the inputs and the output, but there is little way to understand what is going on under the hood. As a response to that, we have seen several different Explainable AI models flourish in recent years, with the aim of letting the user see why the black-box gave a certain output. In this context, we evaluate two very popular eXplainable AI (XAI) models in their ability to discriminate observations into groups, through the application of both unsupervised and predictive modeling to the weights these XAI models assign to features locally. The evaluation is carried out on real Small and Medium Enterprises data, obtained from official italian repositories, and may form the basis for the employment of such XAI models for post-processing features extraction.

ANALYSIS OF MACHINE-BUILDING ENTERPRISES TO ENHANCE CAPITAL BUDGETING IN CURRENT CONDITIONS

An integral assessment of the production capacity of several machine-building enterprises, which includes their financial position analysis, is performed in the article. The methodological basis for determining the integral indicator of the financial position is a method of multifactorial discriminatory analysis. Five classes assigned based on the level of financial position range from the highest level of capacity to meet obligations and the lowest probability of default to the lowest level with a high probability of default. The analysis shows that the financial independence ratio exceeds the critical value for 60% of enterprises, 40% pay off debt in time and their financial risk ratio value is close to industry standard, 20% entities encounter problems with paying off current debt. In general, according to the results of integral assessment of the considered enterprises production capacity, over 60% of the entities belong to the first class, 20% - each to the fifth and second classes. These results serve as an input in the development of required rate of return assessment model to enhance capital budgeting at machine-building enterprises.