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The crux of this paper is to unveil efficient features and practical tools that can predict credit default.

Annual data of non-financial listed companies were taken from 2000 to 2020, along with 71 financial ratios. The dataset was bifurcated into three panels with three default assumptions. Logistic regression (LR) and k-nearest neighbor (KNN) binary classification algorithms were used to estimate credit default in this research.

The study’s findings revealed that features used in Model 3 (Case 3) were the efficient and best features comparatively. Results also showcased that KNN exposed higher accuracy than LR, which proves the supremacy of KNN on LR.

Using only two classifiers limits this research for a comprehensive comparison of results; this research was based on only financial data, which exhibits a sizeable room for including non-financial parameters in default estimation. Both limitations may be a direction for future research in this domain.

This study introduces efficient features and tools for credit default prediction using financial data, demonstrating KNN’s superior accuracy over LR and suggesting future research directions.

The purpose of this paper is to assess and compare the forecast ability of existing credit risk models, answering three questions: Can these methods adequately predict default events? Are there dominant methods? Is it safer to rely on a mix of methodologies?

The authors examine four existing models: O-score, Z-score, Campbell, and Merton distance to default model (MDDM). The authors compare their ability to forecast defaults using three techniques: intra-cohort analysis, power curves and discrete hazard rate models.

The authors conclude that better predictions demand a mix of models containing accounting and market information. The authors found evidence of the O-score's outperformance relative to the other models. The MDDM alone in the sample is not a sufficient default predictor. But discrete hazard rate models suggest that combining both should enhance default prediction models.

The analysed methods alone cannot adequately predict defaults. The authors found no dominant methods. Instead, it would be advisable to rely on a mix of methodologies, which use complementary information.

Better forecasts demand a mix of models containing both accounting and market information.

The findings suggest that more precise default prediction models can be built by combining information from different sources in reduced-form models and combining default prediction models that can analyze said information.

The purpose of this paper is to test whether the qualitative variables regarding the territory and the firm–territory relationship can improve the accuracy rates of small business default prediction models.

The authors apply a logistic regression to a sample of 141 small Italian enterprises located in the Marche region, and the authors build two different default prediction models: one using only financial ratios and one using jointly financial ratios and variables related to the relationship between firm and territory.

Including variables regarding the relationships between firms and their territory, the accuracy rates of the default prediction model are significantly improved.

The qualitative variables data collected are affected by subjective judgments of respondents of the firms studied. In addition, neither other qualitative variables (such as those regarding competitive strategies, or managerial skills) are included nor those variables regarding the relationships between firms and financial institutions are included.

The study suggests that financial institutions should include territory qualitative variables, and, above all, qualitative variables regarding the firm–territory relationship, when constructing business default prediction models. Including this type of variables, it could be able to reduce the tendency to place unnecessary restrictions on credit.

The field of business failure prediction modeling using variables regarding the relationship between firm–territory is a unexplored area as it count of a very few studies.

The purpose of this paper is to do a performance comparison of three different data mining techniques.

Logit model, decision tree and random forest are applied in this study on British, French, German, Italian, Portuguese and Spanish balance sheet data from 2006 to 2012, which covers 446,464 firms. Because of the strong imbalance with regard to the solvency status, classification trees and random forests are modified to adapt to this imbalance. All three model specifications are optimized extensively using resampling techniques, relying on the training sample only. Model performance is assessed, strictly, based on out-of-sample predictions.

Random forest is found to strongly outperform the classification tree and the logit model in almost all considered years and countries, according to the quality measure in this study.

Obtaining reliable estimates of default propensity scores is of immense importance for potential credit grantors, portfolio managers and regulatory authorities. As the overwhelming majority of firms are not listed on stock exchanges, annual balance sheets still provide the most important source of information. The obtained ranking of the three models according to their predictive performance is relatively robust, due to the consideration of several countries and a relatively long time period.

The purpose of this paper is to critically analyze the common assumption, made by many credit risk models such as the Moody's KMV Loss‐Calc model, of a β distribution for the loss‐given default (LGD). The paper shows that this assumption does not perform well in constructing analytic prediction intervals for LGD.

Simulation experiments were conducted to highlight the potential problems associated with this distributional assumption in constructing prediction intervals for LGD.

The simulation experiments show that, when starting from a different assumption concerning the shape of the population distribution, the beta distribution does not perform well in constructing prediction intervals for LGD.

The analysis performed in this study addresses a relevant subject. Indeed, a correct estimate of a credit exposure LGD is particularly relevant not only for internal risk management and management purposes, but also for regulatory reasons within the context of the internal ratings based approach of the recently approved capital regulation framework (Basel II).

The purpose of this paper is to assess the significance of the Merton distance-to-default (DD) in predicting defaults for a sample of listed Indian firms.

The study uses a matched pair sample of defaulting and non-defaulting listed Indian firms. It employs two alternative statistical techniques, namely, logistic regression and multiple discriminant analysis.

The option-based DD is found to be statistically significant in predicting defaults and has a significantly negative relationship with the probability of default. The DD retains its significance even after the addition of Altman’s Z-score. This further establishes its robustness as a significant predictor of default.

The study re-establishes the utility of the Merton model in India using a simplified version of the Merton model that can be easily operationalized by practitioners, reasonably larger sample size and is done in a more recent period covering the post global financial crisis period. The findings could be valuable to banks, financial institutions, investors and managers.

This paper aims to study the default risk of small and medium-sized enterprises in the construction sector.

An unbalanced sample of 2,754 Portuguese companies from the construction sector, from 2008 to 2020, is analysed. Companies are classified in default or compliant following an ex-ante criterion. Then, using the stepwise analysis, the most relevant variables are selected, which are later used in the logit model. To verify the robustness of the results, a sample of legally insolvent companies is added (mixed criterion) and the initial sample is split into two subperiods.

Financial variables are the most relevant to predict the pattern for this sample. The main conclusions show that smaller and older companies, more indebted, with more liquidity and with higher EBIT have a higher probability of default. These conclusions are confirmed using a mixed criterion to classify companies as default or compliant and including a macroeconomic dummy.

This work not only contributes to enlarging the literature review but also makes relevant contributions to practice. Companies from the construction sector can understand which indicators must control to avoid financial problems. The government also has relevant information that can help in adapting or creating regulations for recovering or revitalizing companies.

This study proposed an ex-ante criterion that can be used for all types of companies. Most works use a legal or a mixed criterion that does not allow for detecting signs of financial problems in advance. Moreover, the sample used is almost unexplored – SMEs from a sector with great mortality rate.

This paper aims at developing an early warning signal model for predicting corporate default in emerging market economy like India. At the same time, it also aims to present methods for directly estimating corporate probability of default (PD) using financial as well as non‐financial variables.

Multiple Discriminate Analysis (MAD) is used for developing Z‐score models for predicting corporate bond default in India. Logistic regression model is employed to directly estimate the probability of default.

The new Z‐score model developed in this paper depicted not only a high classification power on the estimated sample, but also exhibited a high predictive power in terms of its ability to detect bad firms in the holdout sample. The model clearly outperforms the other two contesting models comprising of Altman's original and emerging market set of ratios respectively in the Indian context. In the logit analysis, the empirical results reveal that inclusion of financial and non‐financial parameters would be useful in more accurately describing default risk.

Using the new Z‐score model of this paper, banks, as well as investors in emerging market like India can get early warning signals about the firm's solvency status and might reassess the magnitude of the default premium they require on low‐grade securities. The default probability estimate (PD) from the logistic analysis would help banks for estimation of credit risk capital (CRC) and setting corporate pricing on a risk adjusted return basis.

The purpose of this paper is to develop a credit‐scoring model as an aggregate valuation procedure that integrates various financial and non‐financial factors and thereby improves small to medium‐sized enterprises' (SMEs) knowledge about their default risk.

Using panel data from a representative sample of Portuguese SMEs operating in the food or beverage manufacturing sector, this paper develops a logit scoring model to estimate one‐year predictions of default.

The probability of non‐default in the next year is an increasing function of profitability, liquidity, coverage, and activity and a decreasing function of leverage. Smaller firms and those with just one bank relationship have a higher probability of default. The findings suggest that a main bank has incentives to engage in hold up by increasing margins that ex post are too high.

Because SMEs differ from large corporations in their credit risk (e.g., riskier, lower asset correlations), this study has implications for both banks and supervisory actors. Banks should consider qualitative variables when setting internal systems and procedures to manage credit risk. Supervisory institutions should claim mixed credit ratings to determine regulatory capital requirements.

This paper offers a new model, focused specifically on SMEs, and explores the role of financial and non‐financial factors in determining internal credit risks.

This paper aims to find out significant macroeconomic variables, incorporated as sensitivity variables (macroeconomic sensitivities), affecting financial distress for a sample of listed Indian firms.

The study uses a matched pair sample of defaulting and non-defaulting listed Indian firms. It uses two alternative statistical techniques, viz., logistic regression and multiple discriminant analysis. The macroeconomic sensitivities are estimated by regressing the monthly stock return of the individual firm on the monthly changes in each macroeconomic variable.

Sensitivity to changes in the stock market (stock market sensitivity) and sensitivity to changes in inflation [Consumer Price Index (CPI) sensitivity] have a significant impact on the default probability of a firm. Stock market sensitivity has a significant positive relationship with the probability of default, and CPI sensitivity has a significant negative relationship with the probability of default.

The study links the developments in the external environment to the firm’s susceptibility to default. Furthermore, it highlights the significance of sensitivity of a firm to uncertainties in the macroeconomic environment and its impact on default risk. This establishes the fact that each firm is uniquely affected by the changes in the overall macroeconomic environment. The findings could be valuable to lenders such as banks and financial institutions, investors and policymakers.