Predicting Corporate Financial Distress Research Articles

A proposed multidimensional model for predicting financial distress: an empirical study on Egyptian listed firms

Although there has been a growing interest by researchers worldwide over the past decades to identify the factors pertaining to corporate financial distress and to develop financial distress prediction models that serve as early warning signs to the various firm stakeholders, notably to date, studies that were conducted were context specific and cannot be objectively generalized to other countries and rendered mixed inconclusive results. Therefore, the main objective of this study is to thoroughly investigate the factors that affect corporate financial distress in Egypt and to develop a multidimensional financial distress prediction model. Using comprehensive data of EGX100 listed firms, the researcher examines the role played by financial ratios, market-based indicators, macroeconomic factors, and corporate governance mechanisms in modeling corporate financial distress. Empirical results indicate that after controlling for the COVID-19 effects, the most significant financial ratios in predicting corporate financial distress are the working capital to total assets ratio, earnings before interest and taxes to total assets ratio, and the sales to total assets ratio. Such ratios are negatively related to the likelihood of corporate financial distress. However, the market value of equity to total liabilities ratio, and GDP growth rate have a positive impact on the likelihood of financial distress. However, the retained earnings to total assets ratio, the corporate governance mechanisms, the firm market capitalization, the interest rate, and the consumer price index are insignificant in predicting corporate financial distress in the Egyptian context. The resulting model demonstrates outstanding classification accuracy at around 96%.

Financial Distress Early Warning for Chinese Enterprises from a Systemic Risk Perspective: Based on the Adaptive Weighted XGBoost-Bagging Model

This paper aims to tackle the problem of low accuracy in predicting financial distress in Chinese industrial enterprises, attributable to data imbalance and insufficient information. It utilizes annual data on systemic risk indicators and financial metrics of Chinese industrial enterprises listed on the China’s A-share market between 2008 and 2022 to construct the adaptive weighted XGBoost-Bagging model for corporate financial distress prediction. Empirical findings demonstrate that systemic risk indicators possess predictive potential independent of traditional financial information, rendering them valuable non-financial early warning indicators for China’s industrial sector; moreover, they help to enhance the predictive accuracy of various comparative models. The adaptive weighted XGBoost-Bagging model incorporating systemic risk indicators effectively addresses challenges arising from data imbalance and information scarcity, significantly improving the accuracy of financial distress prediction in Chinese industrial enterprises under the 2015 Chinese stock market crash, the Sino-US trade friction, and the COVID-19 epidemic; as such, it can be used as an efficient risk early warning tool for China’s industrial sector.

Financial Indicators and Corporate Financial Distress Prediction in Context of Pakistan: A Systemic Review.

It is feasible to predict the financial distress that a Pakistani company may experience under the current conditions. This study draws attention to key factors influencing financial forecasting and mitigation by integrating the results of several research studies conducted over the last two decades. The literature places a great deal of emphasis on financial variables such as profitability, solvency, and liquidity as indicators. Other factors that may affect prediction models is an increase in inflation, interest rates, or the gross domestic product. Corporate governance, industry dynamics, market indicators, and legal and regulatory frameworks are among the many factors that impact financial distress prediction. As far as methodologies adopted in existing research are concerned, the machine learning methods are replacing statistical methods and are providing best prediction results. Moreover, this study provides a road map for the future research diretions to be employed for financial prediction related studies.

The role of feature importance in predicting corporate financial distress in pre and post COVID periods: Evidence from China

The prediction of firm financial distress during the COVID-19 crisis episode attracted massive academic attention since economic uncertainty was exacerbated. In this paper, we propose a firm financial distress prediction model based on the Extreme Gradient Boosting-Genetic Programming (XGB-GP) framework by investigating subsamples of pre-COVID and post-COVID periods. The key contribution of our paper is that we explore time-varying prediction features for pre-COVID and post-COVID periods. We illuminate that the earning financial indicator is the dominant feature for financial distress prediction during the pre-COVID period, whereas total financial leverage is the most important factor during the post-COVID period. On this basis, our XGB-GP financial distress prediction model exhibits higher prediction accuracy than the traditional models. As a result, managers can modify the financial leverage level to improve the financial situation of the firm by reducing the debt burden and increasing profitability during the post-COVID period.

Systematic Literature Review – Determinants of Corporate Financial Distress Prediction

The purpose of this study is to conduct a systematic literature review of the determinants of corporate financial distress prediction, based on scientific papers in related fields, which have been published. This paper tries to provide a thorough review of scientific papers on the topic of predicting the company's financial difficulties, researching, and showing the relationship among authors of different scientific papers and also showing the main determinants used and researched by the authors of these scientific papers in the last 10 years.Systematic literature review (SLR) is carried out using Garuda (Garba Rujukan Digital) as database to obtain scientific papers from a period of 10 years, namely 2012-2022, with the topic of predicting corporate financial distress from listed companies in Indonesia Stock Exchange (IDX). The first finding in this paper is that the prediction of corporate financial distress is a topic that is increasingly attracting the attention of researchers, as evidenced by the increasing number of scientific papers in this topic from year to year. Secondly, that financial ratios are the most influential determinants in the research carried out by authors of scientific papers, in different predictive models they used..

Prediction of corporate financial distress in the travel and tourism industry

Currently, most countries in the world endure financial distress due to the ongoing economic crisis. Thus, financial distress prediction (FDP) has become vital in speculating the business continuity for the corporate world. In the context of Sri Lanka, the travel and tourism industry has been highly exposed to political, economic and climate (weather) shocks, as well as being sensitive to the impacts of COVID-19. Along with those shakes the recent economic crisis and the need for FDP led the researchers to empirically investigate the discrimination zones, i.e., distress zone, grey zone, and safe zone of the travel and tourism industry in developed and developing countries. This study adopted Altman’s (1968) original Z-score model for data analysis. This research is based on secondary data from annual reports of 138 listed travel and tourism companies listed on the stock exchanges of developed and developing countries: the USA, Australia, Singapore, South Africa, Malaysia, and Sri Lanka during the five-years period (2016–2020). The purposive sampling method is used to obtain the secondary data for the study. The finding revealed that 96.38% of travel and tourism companies are in the distress zone, 2.90% grey zone, and 0.72% safe zone in developed and developing countries. This study recommends all the travel and tourism companies in developed and developing countries maintain enough retained earnings to avoid corporate failure. This study is significant since it evidences the FDP and signals the global economic depression.

Corporate Financial Distress Prediction in a Transition Economy

Corporate Financial Distress Prediction in a Transition Economy

Financial Distress Prediction: A Hybrid Tracking Model Approach

The purpose of this study was to build a highly accurate corporate financial distress tracking and prediction model based on hybrid machine learning technology. The research data were from Taiwan Economic Journal, and the research subjects were enterprises with financial distress risk announced in September 2022. In consideration of enterprise features, this study excluded the finance and insurance industries. The research period was three years (2019, 2020, and 2021) before the distress announcement. This study matched enterprises with financial distress and enterprises without financial distress (normal enterprises) at a ratio of 1:1 for each year. The sample size for each year included 374 enterprises with financial distress and 374 enterprises without financial distress. This study applied several machine learning technologies. At first, important variables were screened by applying artificial neural networks (ANNs). Next, prediction models were built based on decision tree C5.0 and random forest (RF) and were compared. According to the empirical result, the ANN-RF model provided a higher accuracy.

Prediction of corporate financial distress based on digital signal processing and multiple regression analysis

Abstract In order to reduce the default rate of corporate bond market, the author proposes to use digital signal processing and multiple regression analysis to study the prediction system of financial distressed companies. First, design the research method, Logistic regression model is the most commonly used multivariate statistical method when modeling binary dependent variables, it can solve the problem of nonlinear classification, it has no specific requirements for the distribution of variables, and the accuracy of judgment is high. The author selects 32 financial ratios from the perspectives of solvency, operating ability, profitability, development ability, per share index, and risk level. Taking special treatment (ST) due to abnormal financial status as a sign of financial distress in listed companies, when selecting samples, the matching principle is adopted to select non-ST companies as matching samples. Two methods of logistic regression and support vector machine are used for empirical testing, and both in-sample testing and out-of-sample prediction are performed. The results show that when using the logistic regression method, the propensity to default indicator (TTD) reflected in the text content, it can indeed improve the out-of-sample prediction accuracy of the financial distress prediction model, and it is consistent with the in-sample test, this is mainly reflected in the reduction of the first type of error, that is, the probability of misjudging a financially distressed company as a normal company. Changes in the proportions have little effect on the relative importance of financial ratio variables when modeling with support vector machines, the propensity to default indicator (TTD) entered the top ten important variables in both ratios, and ranked fourth among all indicators when the ratio was 1:2, importance has increased significantly. From this it can be seen that, when using support vector machine to build a financial distress prediction model, the propensity to default indicator (TTD) has played an important role. In the case of using the support vector machine method, adding the default tendency indicator (TTD) reflected by the text information can also improve the accuracy of the financial distress prediction model.

On logit and artificial neural networks in corporate distress modelling for Zimbabwe listed corporates

Corporate financial distress prediction is a pivotal aspect of economic development. The ability to foretell that a company will be getting into financial distress is essential for decision-makers, shareholders, and policymakers in making the best decisions and policies for sustainable development. Prediction accuracy is of paramount importance in the implementation of distress mitigation measures, a critical component attracting investment in particular to most of the developing countries in Africa. The advent of the fourth industrial revolution saw Artificial Intelligence (AI) taking centre stage in financial risk modelling. This growth has however not precluded the role of traditional statistical methods in modelling financial risk. There is a lack of consensus amongst academia and practitioners on the accuracy of these two groups of methodologies in distress prediction. Protagonists of the conventional school of thought still hold on to statistical methods being more accurate whilst the new age proponents believe AI has brought in higher levels of predictive strength and model accuracy. This study seeks to compare the accuracy of Logit and Artificial Neural Networks (ANN) in corporate distress prediction. The two modelling techniques were applied to an 8-year panel dataset from the Zimbabwe Stock Exchange. The Logit model outperformed the ANN by an overall accuracy of 92.21% compared to ANN with 85.8%. Heightened prediction accuracy is bound to improve the return to shareholders by enhancing financial risk management within emerging markets. This study also seeks to contribute to the ongoing debate on the superiority between AI techniques and statistical techniques.

Does Corporate Governance Enhance Financial Distress Prediction?

This study validates some aspects of agency theory, resource dependency theory, and organization theory referring to the Sri Lankan context. The sample includes 205 non-financial listed firms prepared in a balanced panel for six years. Implications are provided for the insufficiency of financial variables in predicting corporate financial distress. Financial aspects together with corporate governance jointly enhance the predictive power of financial distress. Less likelihood of financial distress is explained by board size, board independence, institutional ownership, non-institutional ownership concentration, and board ownership. Boards with 5-9 members are likely to be optimal. Firms fail with the concentrated ownership structure. The expected monitoring role of large institutional shareholders and blockholders is inhibited by their expropriation. The expropriation could also occur with the unitary leadership. Contextually, results make a distinctive contribution to the literature owing to the lack of quality audits for governance compliances, family dominance, and board erraticism. Moreover, corporate control within business groups and economic and political instability are also portrayed.

Financial distress prediction using a corrected feature selection measure and gradient boosted decision tree

Corporate financial distress prediction research has been ongoing for more than half a century, during which many models have emerged, among which ensemble learning algorithms are the most accurate. Most of the state-of-the-art methods of recent years are based on gradient boosted decision trees. However, most of them do not consider using feature importance for feature selection, and a few of them use the feature importance method with bias, which may not reflect the true importance of features. To solve this problem, a heuristic algorithm based on permutation importance (PIMP) is proposed to modify the biased feature importance measure in this paper. This method ranks and filters the features used by machine learning models, which not only improves accuracy but also makes the results more interpretable. Based on financial data from 4,167 listed companies in China between 2001 and 2019, the experiment shows that compared with using the random forest (RF) wrapper method alone, the bias in feature importance is indeed corrected by combining the PIMP method. After the redundant features are removed, the performance of most machine learning models is improved. The PIMP method is a promising addition to the existing financial distress prediction methods. Moreover, compared with traditional statistical learning models and other machine learning models, the proposed PIMP-XGBoost offers higher prediction accuracy and clearer interpretation, making it suitable for commercial use.

The impact of corporate social responsibility on financial distress: empirical evidence

PurposeThe present study aims to explore the role of corporate social responsibility (CSR) on the likelihood of financial distress for a sample of 139 Pakistan Stock Exchange (PSX) listed firms throughout 2008–2019.Design/methodology/approachPanel logistic regression (PLR) and the dynamic generalized method of moments (GMM) estimator are used to examine the impact of CSR on financial distress. The investment in CSR measures through a multidimensional financial approach which comprises the sum of the contribution made by the company in the form of charitable donation, employees’ welfare and research and development, whereas the Altman Z-score and ZM-Score are used as an indicator of financial distress. The higher the Z-score lower will be the probability of financial distress, whereas the higher ZM score shows a greater probability of financial distress risk.FindingsThe authors find a significant negative impact of CSR on financial distress in both PLR and GMM models. This finding is consistent with the stakeholder view of CSR, as an investment in CSR not only aligns the interest between shareholders and stakeholders but also mitigates the risk of financial distress as well.Research limitations/implicationsLike other studies, the present study is not free from limitations. First, financial firms skipped from the sample, although literature witnesses a lot of studies highlight the financial firms' commitment to achieving CSR goals. Second, financial distress occurs in different stages, the authors fail to establish linkage CSR engagements at different stages of CSR. In the future, researchers can make a valuable addition by covering these missing links in present studies.Practical implicationsThe findings of this study provide more insight to corporate managers and investors about the association between the quality of investment in CSR and the degree of financial distress, concerning Pakistani firms. Furthermore, this study contributes to the existing literature by adding new evidence from developing countries such as Pakistan which are helpful for regulatory bodies and policymakers in the formulation of long-term CSR strategies to manage financial distress.Originality/valueThe study extends the body of existing literature on CSR and the likelihood of financial distress in Pakistan. The results suggest that policymakers may pay special attention to the quality of CSR while predicting corporate financial distress.

The Role of Board Independence and Ownership Structure in Improving the Efficacy of Corporate Financial Distress Prediction Model: Evidence from India

The study aimed to investigate the role of non-financial measures in predicting corporate financial distress in the Indian industrial sector. The proportion of independent directors on the board and the proportion of the promoters’ share in the ownership structure of the business were the non-financial measures that were analysed, along with ten financial measures. For this, sample data consisted of 82 companies that had filed for bankruptcy under the Insolvency and Bankruptcy Code (IBC). An equal number of matching financially sound companies also constituted the sample. Therefore, the total sample size was 164 companies. Data for five years immediately preceding the bankruptcy filing was collected for the sample companies. The data of 120 companies evenly drawn from the two groups of companies were used for developing the model and the remaining data were used for validating the developed model. Two binary logistic regression models were developed, M1 and M2, where M1 was formulated with both financial and non-financial variables, and M2 only had financial variables as predictors. The diagnostic ability of the model was tested with the aid of the receiver operating curve (ROC), area under the curve (AUC), sensitivity, specificity and annual accuracy. The results of the study show that inclusion of the two non-financial variables improved the efficacy of the financial distress prediction model. This study made a unique attempt to provide empirical evidence on the role played by non-financial variables in improving the efficiency of corporate distress prediction models.

Indirect financial distress costs in non-financial firms: evidence from an emerging market

PurposeThis study aims to analyse 508 financially distressed firm-year observations for the period 2010–2018 of Pakistan Stock Exchange (PSX) listed firms to examine the magnitude of indirect financial distress costs (IFDC) and to investigate which firm-specific variable is relatively important in explaining these indirect costs. This will not only enrich empirical literature but also helpful in cross-country comparison.Design/methodology/approachOptimal model selection along with panel data analysis technique is used to select the most optimal model to observe the findings. Financial distress is measure through Altman’s Z-score and firm-specific variables cover leverage, level of intangible assets, investment policy, tangible assets, firm’s size, level of liquid assets and Tobin’s Q of sample firms.FindingsThe findings of this study show that the average size of IFDC for the sample observations is 6.70%. In addition to this, finding further suggest that leverage, the level of intangible assets and changes in investment policy have positive while the size of the firm and Tobin’s Q have a significant negative impact on IFDC. Further, this paper argues that the level of tangible assets and liquid assets are statistically unimportant in observing the IFDC for PSX financially distressed firm-year observations.Practical implicationsThe findings of this study provide more insight to corporate managers and investors about the association between firm-specific financial characteristics and IFDC concerning Pakistani firms. Furthermore, this study contributes to the existing literature by adding new evidence from developing countries such as Pakistan which are helpful for regulatory bodies and policymakers in the formulation of long-term strategies to manage the financial distress costs.Originality/valueThe study extends the body of existing literature on IFDC regarding Pakistan. The results suggest that policymakers may pay special attention to the quality of a firm’s capital structure strategies while predicting corporate financial distress costs.

The impact of corporate social responsibility on financial distress: evidence from developing economy

PurposeThis study aims to explore the role of corporate social responsibility (CSR) on the likelihood of financial distress for a sample of 139 Pakistan Stock Exchange (PSX) listed firms throughout 2008–2019.Design/methodology/approachThe dynamic generalized method of moments (GMM) estimator is used to examine the impact of CSR on financial distress. The investment in CSR is measured through a multidimensional financial approach which comprises the sum of the contribution made by the company in the form of charitable donation, employees’ welfare and research and development, while the Altman Z-score is used as an indicator of financial distress. The higher the Z-score, the lower will be the probability of financial distress.FindingsThe authors find a significant positive impact of CSR on financial distress in GMM model. This finding is consistent with the shareholder view and over-investment hypothesis of CSR as management makes an investment in CSR to get personal benefits, which resultantly leads the firm toward financial distress state. Further, this positive relationship remains present for firms having strong involvement in foreign business through exports.Research limitations/implicationsLike other studies, the present study is not free from limitations. First, financial firms are skipped from the sample, although literature witnesses a lot of studies highlight the financial firms’ commitment to achieving CSR goals. Second, financial distress occurs in different stages, and this study fails to establish a linkage between CSR engagement at different stages of financial distress. In the future, researchers can make valuable addition by covering these missing links in present studies.Practical implicationsFindings suggest several practical implications. For policymakers, they should encourage firms to adopt more socially responsible behavior as it not only prevents them from distress but also comes with better investment behavior, minimize bankruptcies and make economies more strong and stable. Second, results suggest corporate managers emphasize socially responsible behavior as its benefits are beyond the “societal benefits” as it lessens financial distress through lower cost of debt, lesser financial constraints and reduced cost of information asymmetry, and it minimizes the cost of capital. Lastly, investors make risk premium assessments related to future earnings by determining the likelihood of financial distress in the future.Originality/valueThe study extends the body of existing literature on CSR and the likelihood of financial distress in Pakistan, which is according to the best knowledge of the authors, not yet studied before. The results suggest that policymakers may pay special attention to the quality of CSR while predicting corporate financial distress.

Corporate financial distress prediction based on controlling shareholder’s equity pledge

ABSTRACT In the past literature, the prediction model of financial distress mainly used the information of corporate finance and corporate governance, and less considered the predictive effect of controlling shareholder information. Based on the sample of A-share listed companies in China from 2014 to 2019, this paper empirically investigates the incremental effect of controlling shareholder’s equity pledge on improving the accuracy of financial distress prediction. It is found that the information of controlling shareholder’s equity pledge can significantly improve the accuracy of financial distress prediction. After changing the definition of the explained variable and the data source, the conclusion remains robust. This paper verifies the role of controlling shareholder’s equity pledge in predicting financial distress, extends the selection of financial distress prediction model variables to the controlling shareholder level, and enriches the research on the economic consequences of equity pledge.

On the determinants and prediction of corporate financial distress in India

PurposeThe main aim of the study is to identify some critical microeconomic determinants of financial distress and to design a parsimonious distress prediction model for an emerging economy like India. In doing so, the authors also attempt to compare the forecasting accuracy of alternative distress prediction techniques.Design/methodology/approachIn this study, the authors use two alternatives accounting information-based definitions of financial distress to construct a measure of financial distress. The authors then use the binomial logit model and two other popular machine learning–based models, namely artificial neural network and support vector machine, to compare the distress prediction accuracy rate of these alternative techniques for the Indian corporate sector.FindingsThe study’s empirical results suggest that five financial ratios, namely return on capital employed, cash flows to total liability, asset turnover ratio, fixed assets to total assets, debt to equity ratio and a measure of firm size (log total assets), play a highly significant role in distress prediction. The study’s findings suggest that machine learning-based models, namely support vector machine (SVM) and artificial neural network (ANN), are superior in terms of their prediction accuracy compared to the simple binomial logit model. Results also suggest that one-year-ahead forecasts are relatively better than the two-year-ahead forecasts.Practical implicationsThe findings of the study have some important practical implications for creditors, policymakers, regulators and other stakeholders. First, rather than monitoring and collecting information on a list of predictor variables, only six most important accounting ratios may be monitored to track the transition of a healthy firm into financial distress. Second, our six-factor model can be used to devise a sound early warning system for corporate financial distress. Three, machine learning–based distress prediction models have prediction accuracy superiority over the commonly used time series model in the available literature for distress prediction involving a binary dependent variable.Originality/valueThis study is one of the first comprehensive attempts to investigate and design a parsimonious distress prediction model for the emerging Indian economy which is currently facing high levels of corporate financial distress. Unlike the previous studies, the authors use two different accounting information-based measures of financial distress in order to identify an effective way of measuring financial distress. Some of the determinants of financial distress identified in this study are different from the popular distress prediction models used in the literature. Our distress prediction model can be useful for the other emerging markets for distress prediction.

Forecasting corporate financial distress in the Southeast Asian countries: A market-based approach

This study is conducted to investigate the prediction of corporate financial distress based on the Merton (1974) market-based Distance to Default (DD) model over the period from 1997 to 2016 which covers a range of economic financial circumstances, including the Asian Financial Crisis (AFC) and Global Financial Crisis (GFC). The study focusses on the six largest countries in the ASEAN Economic Community (AEC), comprising of Indonesia, Malaysia, the Philippines, Singapore, Thailand, and Vietnam. Unlike previous studies which focus mainly on bankruptcy, this paper focusses on early warning distress indicators that signal distress well before bankruptcy. This is when firms experience difficulty in servicing debt as measured by interest coverage ratio (ICR) at a firm level and non-performing loans (NPLs) at a country level. Key empirical findings from this paper indicate that the market-based distance-to-default (DD) model is generally a good early warning indicator of financial distress in the following year, particularly for ICR, but that prediction accuracy varies between individual countries in the Southeast Asian region.

Financial risk assessment in shipping: a holistic machine learning based methodology

Corporate financial distress (FD) prediction models are of great importance to all stakeholders, including regulators and banks, who rely on acceptable estimates of default risk, for both individual borrowers and bank loan portfolios. Whilst this subject has been covered extensively in finance research, its application to international shipping companies has been limited while the focus has mainly been on the application of traditional linear modelling, using sparse, cross-sectional financial statement data. Insufficient attention has been paid to the noisy and incomplete nature of shipping company financial statement information. This study contributes to the literature through the design, development and testing of a novel holistic machine learning methodology which integrates predictor evaluation and missing data analysis into the distress prediction process. The model was validated using a longitudinal dataset of over 5000 company year-end financial statements combined with macroeconomic and market predictors. We applied this methodology first for individual company level distress prediction before testing the models’ ability to provide accurate confidence intervals by backtesting conditional value-at-risk estimations of the distress rates for bank portfolios. We conclude that, by adopting a holistic approach, our methodology can enhance financial monitoring of company loans and bank loan portfolios thereby providing a practical “early warning system” for financial distress.