Corporate Bankruptcy Prediction

Bankruptcy prediction is one of the most important research areas in corporate finance. Bankruptcies are an indispensable element of the functioning of the market economy, and at the same time generate significant losses for stakeholders. Hence, this book was established to collect the results of research on the latest trends in predicting the bankruptcy of enterprises. It suggests models developed for different countries using both traditional and more advanced methods. Problems connected with predicting bankruptcy during periods of prosperity and recession, the selection of appropriate explanatory variables, as well as the dynamization of models are presented. The reliability of financial data and the validity of the audit are also referenced. Thus, I hope that this book will inspire you to undertake new research in the field of forecasting the risk of bankruptcy

SME default prediction: A systematic methodology-focused review

This study reviews the methodologies used in the literature to predict failure in small and medium-sized enterprises (SMEs). We identified 145 SMEs’ default prediction studies from 1972 to early 2023. We summarized the methods used in each study. The focus points are estimation methods, sample re-balancing methods, variable selection techniques, validation methods, and variables included in the literature. More than 1,200 factors used in failure prediction models have been identified, along with 54 unique feature selection techniques and 80 unique estimation methods. Over one-third of the studies do not use any feature selection method, and more than one-quarter use only in-sample validation. Our main recommendation for researchers is to use feature selection and validate results using hold-out samples or cross-validation. As an avenue for further research, we suggest in-depth empirical comparisons of estimation methods, feature selection techniques, and sample re-balancing methods based on some large and commonly used datasets.publishedVersio

Corporate failure prediction of construction companies in Poland : evidence from logit model

Purpose: This paper aims to develop a corporate failure prediction model for construction companies in Poland that allow assessing their financial situation and credit risk. Design/Methodology/Approach: For this purpose, the following research methods have been used, descriptive and comparative analysis, subject literature review, and logit anal-ysis. The Polish construction companies' financial data in this research come from the Emerging Markets Information Service (EMIS). To achieve the main goal of the research, the logit model was built. The significance test, error matrix, and ROC curve were used to assess the quality of the estimated binary logit model. Findings: Based on the research, we identify seven financial indicators that significantly impact the probability of poor financial condition. The following variables are current assets turnover, debt to assets ratio, operating profit to assets, gross profit to assets, oper-ating profit plus amortization to short-term liabilities, current assets to assets ratio, and equity to assets ratio. The research results show that corporate failure prediction models are interesting and important tools to assess the financial situation. Based on the devel-oped model, it has been found that the growth of debts increases the credit risk of construc-tion companies. Moreover, the increase in the share of current assets in the total assets harms the financial condition. Also, the risk of insolvency decreases with growing profita-bility measured by the rate of return on assets. Practical Implications: The built logit model can be beneficial for investment loan provid-ers, insurance companies, and entities selecting contractors in construction projects due to the possibility of the credit risk assessment. Originality/Value: The use of logit models to identify statistically significant corporate failure prediction factors for construction companies in Poland.peer-reviewe

Legal and Institutional Determinants of Factoring in SMEs: Empirical Analysis across 25 European Countries

Weak protection of the rights of financiers intensifies agency problems in SME financing, inhibiting the optimal provision of credit necessary to grow and innovate. We use a survey data set of 4,348 SMEs from 25 European countries to analyze whether the use of factoring as a form of SME financing is less dependent on low quality of laws and institutions. We do so analyzing whether the use of factoring by SMEs differs across countries due to differences in the legal protection of creditors. Our findings indicate that firms operating in countries with legal environments that weakly protect the rights of creditors, with political instability or high enforcement costs, are more likely to use factoring. Managers of riskier and opaque companies operating in such inefficient environments can use the results of this study to better understand that there are suitable options to complement bank financing. Managers who seek loans can use the results to diversify their financing structure through the use of factoring. Since factoring can be used as a complement to bank loans or as a substitute for bank financing, it is important that policy makers take our results into account when revising policies concerning access to external financing.Ginés Hernández-Cánovas acknowledges financial support by Fundación Séneca (Project 15403/PHCS/10), and by Ministerio de Ciencia e Innovación (Project ECO2011-29080)

Corporate Finance

This book comprises 19 papers published in the Special Issue entitled “Corporate Finance”, focused on capital structure (Kedzior et al., 2020; Ntoung et al., 2020; Vintilă et al., 2019), dividend policy (Dragotă and Delcea, 2019; Pinto and Rastogi, 2019) and open-market share repurchase announcements (Ding et al., 2020), risk management (Chen et al., 2020; Nguyen Thanh, 2019; Štefko et al., 2020), financial reporting (Fossung et al., 2020), corporate brand and innovation (Barros et al., 2020; Błach et al., 2020), and corporate governance (Aluchna and Kuszewski, 2020; Dragotă et al.,2020; Gruszczyński, 2020; Kjærland et al., 2020; Koji et al., 2020; Lukason and Camacho-Miñano, 2020; Rashid Khan et al., 2020). It covers a broad range of companies worldwide (Cameroon, China, Estonia, India, Japan, Norway, Poland, Romania, Slovakia, Spain, United States, Vietnam), as well as various industries (heat supply, high-tech, manufacturing)

Long-term risk class migrations of non-bankrupt and bankrupt enterprises

This paper investigates how the process of going bankrupt can be recognized much earlier by enterprises than by traditional forecasting models. The presented studies focus on the assessment of credit risk classes and on determination of the differences in risk class migrations between non-bankrupt enterprises and future insolvent firms. For this purpose, the author has developed a model of a Kohonen artificial neural network to determine six different classes of risk. Long-term analysis horizon of 15 years before the enterprises went bankrupt was conducted. This long forecasting horizon allows one to identify, visualize and compare the intensity and pattern of changes in risk classes during the 15-year trajectory of development between two separate groups of companies (150 bankrupt and 150 non-bankrupt firms). The effectiveness of the forecast of the developed model was compared to three popular statistical models that predict the financial failure of companies. These studies represent one of the first attempts in the literature to identify the long-term behavioral pattern differences between future “good” and “bad” enterprises from the perspective of risk class migrations

Financial Soundness Prediction Using a Multi-classification Model: Evidence from Current Financial Crisis in OECD Banks

The paper aims to develop an early warning model that separates previously rated banks (337 Fitch-rated banks from OECD) into three classes, based on their financial health and using a one-year window. The early warning system is based on a classification model which estimates the Fitch ratings using Bankscope bankspecific data, regulatory and macroeconomic data as input variables. The authors propose a “hybridization technique” that combines the Extreme learning machine and the Synthetic Minority Over-sampling Technique. Due to the imbalanced nature of the problem, the authors apply an oversampling technique on the data aiming to improve the classification results on the minority groups. The methodology proposed outperforms other existing classification techniques used to predict bank solvency. It proved essential in improving average accuracy and especially the performance of the minority groups

A Comparison on Leading Methodologies for Bankruptcy Prediction: The Case of the Construction Sector in Lithuania

Different economic environments differ in their characteristics; this prevents the usage of the same bankruptcy prediction models under different conditions. Objectively, the abundance of bankruptcy prediction models gives rise to the idea that these models are not in compliance with the changing business conditions in the market and do not meet the increasing complexity of business tasks. The purpose of this study is to assess the suitability of existing bankruptcy prediction models and the possibilities to increase the effectiveness of their application. In order to analyze theoretical aspects of the application of bankruptcy forecasting models and frame the research methodology, a systemic comparative and logical analysis of the scientific literature and statistical data, graphic data representation, induction, deduction and abstraction are employed. Results of the analysis confirm research hypotheses that bankruptcy prediction models based on macroeconomic variables are effective in identifying the number of corporate bankruptcies in a country and that the application of the model created on the grounds of macroeconomic indicators together with the traditional bankruptcy prediction model can improve the reliability of bankruptcy prediction. However, it was identified that models which are not specially adapted to companies in the construction sector are also suitable for forecasting their bankruptcies

Artificial intelligence in predicting the bankruptcy of non-financial corporations

Research background: In a modern economy, full of complexities, ensuring a business' financial stability, and increasing its financial performance and competitiveness, has become especially difficult. Then, monitoring the company's financial situation and predicting its future develop-ment becomes important. Assessing the financial health of business entities using various models is an important area in not only scientific research, but also business practice.Purpose of the article: This study aims to predict the bankruptcy of companies in the engineer-ing and automotive industries of the Slovak Republic using a multilayer neural network and logistic regression. Importantly, we develop a novel an early warning model for the Slovak engi-neering and automotive industries, which can be applied in countries with undeveloped capital markets. Methods: Data on the financial ratios of 2,384 companies were used. We used a logistic regres-sion to analyse the data for the year 2019 and designed a logistic model. Meanwhile, the data for the years 2018 and 2019 were analysed using the neural network. In the prediction model, we analysed the predictive performance of several combinations of factors based on the industry sector, use of the scaling technique, activation function, and ratio of the sample distribution to the test and training parts. Findings & value added: The financial indicators ROS, QR, NWC/A, and PC/S reduce the likelihood of bankruptcy. Regarding the value of this work, we constructed an optimal network for the automotive and engineering industries using nine financial indicators on the input layer in combination with one hidden layer. Moreover, we developed a novel prediction model for bank-ruptcy using six of these indicators. Almost all sampled industries are privatised, and most com-panies are foreign owned. Hence, international companies as well as researchers can apply our models to understand their financial health and sustainability. Moreover, they can conduct com-parative analyses of their own model with ours to reveal areas of model improvements.KEGA [001PU-4/2022]; Scientific Grant Agency of the Ministry of Education, Science, Research, and Sport of the Slovak Republic; Slovak Academy Sciences [1/0590/22]1/0590/22; Kultúrna a Edukacná Grantová Agentúra MŠVVaŠ SR, KEGA: 001PU-4/202