Search Results (54)

This study addresses the increasing emphasis on sustainability and the importance of understanding how environmental risk influences business failure, a factor unexplored in traditional financial prediction models. Environmental risk, or environmental financial exposure, refers to the potential percentage of a company’s revenue at risk due to the environmental damage it causes. Previous research has not sufficiently integrated environmental variables into failure prediction models. This study aims to determine whether environmental risk significantly predicts business failure and how it interacts with conventional financial indicators. Utilizing data from 971 Spanish cooperative companies in 2022, including financial ratios, the VADIS bankruptcy propensity indicator, and the TRUCAM environmental risk score, the study employs the Extreme Gradient Boosting (XGBoost) machine learning algorithm, chosen for its robustness in handling multicollinearity and nonlinear relationships. The methodology involves training and validation samples, cross-validation for hyperparameter tuning, and interpretability techniques such as variable importance analysis and partial dependence plots. Results demonstrate that the variable related to environmental risk (TRUCAM) ranks among the top predictors, alongside liquidity, profitability, and labor costs, with higher TRUCAM values correlating positively with failure risk, underscoring the importance of sustainable cost management. These findings suggest that firms facing substantial environmental risk are more prone to financial distress. By incorporating this environmental variable into a machine learning framework, this work contributes to the interaction between sustainability practices and corporate viability. Full article

This paper investigates the effectiveness of machine learning algorithms in enhancing the accuracy and reliability of predicting financial distress. The dataset includes Altman Z-Scores and Corporate Governance Compliance (CGC) indicators calculated for manufacturing firms listed on the Bucharest Stock Exchange (BSE) from 2016 to 2022. Leveraging Signaling Theory, the study analyzes financial and governance data for 60 non-financial firms, comprising 420 firm-year observations. Financial distress is classified into three categories: no distress, moderate distress, and severe distress. The study employs a Random Forest classification model, leveraging artificial intelligence techniques to identify critical predictive variables and evaluate their combined effectiveness in signaling financial distress. The findings reveal that machine learning algorithms significantly improve the predictive accuracy and reliability of financial distress classifications, effectively distinguishing between different distress levels by integrating financial ratios and corporate governance variables. These results emphasize the advantages of involving artificial intelligence and advanced analytics in financial distress prediction models, enhancing transparency and strengthening investor confidence. The research contributes to the literature on digital transformation in financial analysis and corporate governance, offering practical implications for investors, managers, creditors, and policymakers in emerging market environments. Full article

In the context of new quality productive forces, enterprises must leverage technological innovation and intelligent management to enhance financial risk resilience. This article proposes a financial distress prediction model based on deep learning, combined with a CNN, BiLSTM, and attention mechanism, using SMOTE for sample imbalance and Hyperband for hyperparameter optimization. Among four CNN-BiLSTM-AT model structures and seven mainstream models (CNN, BiLSTM, CNN-BiLSTM, CNN-AT, BiLSTM-AT, CNN-GRU, and Transformer), the 1CNN-1BiLSTM-AT model achieved the highest validation accuracy and relatively faster training speed. We conducted 100 repeated experiments using data from two companies, with validation on 2025 data, confirming the model’s stability and effectiveness in real-world scenarios. This article lays a solid empirical foundation for further optimization of financial distress warning models. Full article

Debt levels are a crucial factor when assessing the financial stability of agricultural firms, and excessive indebtedness is usually the most important indicator of financial distress. As agriculture is a capital-intensive sector with a high reliance on borrowed funds, firms in this sector are more vulnerable to insolvency. This study examines the performance of artificial neural networks (ANNs) and decision trees (DTs) in predicting the bankruptcy of Slovak agricultural enterprises. In an attempt to compare the models’ performances, the most consequential indebtedness ratios are investigated through machine learning approaches. ANN and DT models are found to perform significantly better than traditional forecast methods. ANN achieved an AUC of 0.9500, accuracy of 96.37%, precision of 96.60%, recall of 99.68%, and an F1-score of 98.12%, determining its robust predictive ability. DT performed a little better on AUC (0.9550) and achieved an accuracy of 97.78%, precision of 98.69%, recall of 99.01%, and an F1-score of 98.85%, determining its predictive ability and interpretability. These findings confirm the potential for applying AI-based models to enhance financial risk assessment. This study provides informative results for financial analysts, policymakers, and corporate managers in support of early intervention strategies. Additional research would be required to explore state-of-the-art AI techniques to further refine bankruptcy forecasting and financial decision-making in vulnerable sectors like agriculture. Full article

Financial distress poses a significant risk to companies worldwide, irrespective of their nature or size. It refers to a situation where a company is unable to meet its financial obligations on time, potentially leading to bankruptcy and liquidation. Predicting distress has become a crucial application in business classification, employing both Statistical approaches and Artificial Intelligence techniques. Researchers often compare the prediction performance of different techniques on specific datasets, but no consistent results exist to establish one model as superior to others. Each technique has its own advantages and drawbacks, depending on the dataset. Recent studies suggest that combining multiple classifiers can significantly enhance prediction performance. However, such ensemble methods inherit both the strengths and weaknesses of the constituent classifiers. This study focuses on analyzing and comparing the financial status of Indian automobile manufacturing companies. Data from a sample of 100 automobile companies between 2013 and 2019 were used. A novel Firm-Feature-Wise three-step missing value imputation algorithm was implemented to handle missing financial data effectively. This study evaluates the performance of 11 individual baseline classifiers and all the 11 baseline algorithm’s combinations by using ensemble method. A manual ranking-based approach was used to evaluate the performance of 2047 models. The results of each combination are inputted to hard majority voting mechanism algorithm for predicting a company’s financial distress. Eleven baseline models are trained and assessed, with Gradient Boosting exhibiting the highest accuracy. Hyperparameter tuning is then applied to enhance individual baseline classifier performance. The majority voting mechanism with hyperparameter-tuned baseline classifiers achieve high accuracy. The robustness of the model is tested through k-fold Cross-Validation, demonstrating its generalizability. After fine-tuning the hyperparameters, the experimental investigation yielded an accuracy of 99.52%, surpassing the performance of previous studies. Furthermore, it results in the absence of Type-I errors. Full article

As corporate sector stability is crucial for economic resilience and growth, machine learning has become a widely used tool for constructing early warning systems (EWS) to detect financial vulnerabilities more accurately. While most existing EWS research focuses on bankruptcy prediction models, bankruptcy signals often emerge too late and provide limited early-stage insights. This study employs a random forest approach to systematically examine whether a company’s insolvency status can serve as an effective multi-stage financial distress EWS. Additionally, we analyze how the financial characteristics of insolvent companies differ from those of active and bankrupt firms. Our empirical findings indicate that highly accurate insolvency models can be developed to detect status transitions from active to insolvent and from insolvent to bankrupt. Furthermore, our analysis reveals that the financial determinants of these transitions differ significantly. The shift from active to insolvent is primarily driven by structural and operational ratios, whereas the transition from insolvent to bankrupt is largely influenced by further financial distress in operational and profitability ratios. Full article

Financial distress prediction models have been extensively utilised to assess the financial health of companies. However, their predictive accuracy can be significantly affected by extraordinary economic disruptions, such as the COVID-19 pandemic. Traditional models, particularly those designed for stable economic conditions, necessitate evaluation and potential adaptation to maintain their effectiveness during unprecedented circumstances. This study seeks to evaluate the performance of financial distress prediction models developed by authors from the Visegrad Four (V4) when applied to Slovak automotive companies before, during, and after the COVID-19 pandemic. Initially, the best-performing models from those selected were identified in the pre-pandemic period (2017–2019). The performances of these models were subsequently analysed during the pandemic and post-pandemic periods (2020–2022). Finally, their coefficients were re-estimated to enhance accuracy while preserving the original variables, ensuring the interpretability of any changes. The objective is to identify the models with the highest performance during the pre-pandemic period, assess their reliability under crisis conditions, and suggest improvements through coefficient re-estimation. While the majority of models experienced significant declines in performance during the pandemic, some retained adequate predictive accuracy. The re-estimated coefficients improved the overall accuracy of the models and also enhanced the sensitivity of some, offering stakeholders the option to utilise either the original or adjusted models based on their specific context. To complement the analysis, we also constructed new models for the pandemic and post-pandemic periods, allowing for a more comprehensive evaluation of financial distress prediction under changing economic conditions. This study provides a framework for adapting financial prediction models to unprecedented economic conditions, contributing valuable insights for researchers and practitioners seeking to enhance predictive tools within dynamic economic environments. Full article

This article investigates the effectiveness of various classification techniques in predicting financial distress for Albanian firms. The dataset includes 16 financial ratios from the financial statements of 187 of the largest non-financial businesses operating in Albania, covering the period from 2011 up to 2014, and ranked by 2014 revenues. The methods used in predicting financial distress are logistic regression, Ada Boost, Naïve Bayes, decision trees, support vector machine (SVM), neural network, and random forest. To compare the effectiveness of the models applied we used Classification Accuracy (CA), confusion matrix, and area under the curve (AUC) as evaluation criteria. The results demonstrate the superior predictive ability of ensemble methods, with random forest achieving more accurate forecasts than other methods, followed by Ada Boost. The research contributes to the literature by showing the added value of machine learning models in emerging markets with unique practice and economic conditions and proposing an alternative classification approach for the classification of financial distress when lacking bankruptcy data. Finally, the empirical findings evidence that the strengths of ensemble learning methods are reinforced in unbalanced not-big datasets of a unique emerging economy. These insights are relevant for lending institutions and researchers aiming to refine credit risk models in unique markets where access to relevant data is a challenge. Full article

Businesses are influenced by the cyclical nature of economic development and distinct stages in the corporate life cycle. Accurate early-warning mechanisms are crucial to mitigating bankruptcy risk, enabling timely rescue measures. This article analyses the reliability of various bankruptcy prediction models, including those by Kliestik et al., Poznanski, the modified Zmijewski, Jakubik–Teply, and Virag–Hajdu, across corporate life cycle stages. Reliability was assessed using five metrics: accuracy, balanced accuracy, F1 and F2 scores, and the Matthews correlation coefficient (MCC). The sample included over 5000 SMEs from Central Europe, with financial data from 2022. The findings reveal a U-shaped trend in financial distress risk, with start-ups and declining enterprises facing the highest risks. The results indicate that the Kliestik et al. model shows consistent reliability across all life cycle stages, while the Poznanski model shows more variability. Conversely, the Virag–Hajdu model exhibits significant variability in reliability, with its best performance observed during the Decline stage. The modified Zmijewski and Jakubik–Teply models show lower MCC values overall, with the modified Zmijewski model performing better at predicting the financial distress of mature shake-out firms compared to other stages. Full article

This study addresses a significant gap in the literature by comparing the effectiveness of traditional statistical methods with artificial intelligence (AI) techniques in predicting bankruptcy among small and medium-sized enterprises (SMEs). Traditional bankruptcy prediction models often fail to account for the unique characteristics of SMEs, such as their vulnerability due to lean structures and reliance on short-term credit. This research utilizes a comprehensive database of 7104 Belgian SMEs to evaluate these models. Belgium was selected due to its unique regulatory and economic environment, which presents specific challenges and opportunities for bankruptcy prediction in SMEs. Our findings reveal that AI techniques significantly outperform traditional statistical methods in predicting bankruptcy, demonstrating superior predictive accuracy. Furthermore, our analysis highlights that a firm’s position within the Global Value Chain (GVC) impacts prediction accuracy. Specifically, firms operating upstream in the production process show lower prediction performance, suggesting that bankruptcy risk may propagate upward along the value chain. This effect was measured by analyzing the firm’s GVC position as a variable in the prediction models, with upstream firms exhibiting greater vulnerability to the financial distress of downstream partners. These insights are valuable for practitioners, emphasizing the need to consider specific performance factors based on the firm’s position within the GVC when assessing bankruptcy risk. By integrating both AI techniques and GVC positioning into bankruptcy prediction models, this study provides a more nuanced understanding of bankruptcy risks for SMEs and offers practical guidance for managing and mitigating these risks. Full article

This paper systematically reviews the behavior of numerical indicators in predicting future bankruptcy of companies through statistical analysis models. Following the PRISMA standard, ten primary studies were included in the review. The obtained results underline (1) the ability of numerical indicators, through simple statistical analysis models, to forecast the bankruptcy of businesses and companies and (2) the reliability of cash flows in predicting financial distress through statistical analysis, and (3) models are built with indicators from a specific economy; it is impossible to consider them stable and unchanging, as changes in a country’s economic conditions can potentially impact their predictive accuracy. Full article

This paper proposes an explicable early warning machine learning model for predicting financial distress, which generalizes across listed Nordic corporations. We develop a novel dataset, covering the period from Q1 2001 to Q2 2022, in which we combine idiosyncratic quarterly financial statement data, information from financial markets, and indicators of macroeconomic trends. The preferred LightGBM model, whose features are selected by applying explainable artificial intelligence, outperforms the benchmark models by a notable margin across evaluation metrics. We find that features related to liquidity, solvency, and size are highly important indicators of financial health and thus crucial variables for forecasting financial distress. Furthermore, we show that explicitly accounting for seasonality, in combination with entity, market, and macro information, improves model performance. Full article

Healthcare leaders are faced with many financial challenges in the contemporary environment, leading to financial distress and notable instances of bankruptcies in recent years. What is not well understood are the specific conditions that may lead to organizational economic failure. Though there are various models that predict financial distress, existing regression methods may be inadequate, especially when the finance variables follow a nonnormal frequency pattern. Furthermore, the regression approach encounters difficulties due to multicollinearity. Therefore, an alternate stochastic approach for predicting the probability of hospital bankruptcy is needed. The new method we propose involves several key steps to better assess financial health in hospitals. First, we compute and interpret the relationship between the hospital’s revenues and expenses for bivariate lognormal data. Next, we estimate the risk of bankruptcy due to the mismatch between revenues and expenses. We also determine the likelihood of a hospital’s expenses exceeding the state’s median expenses level. Lastly, we evaluate the hospital’s financial memory level to understand its level of financial stability. We believe that our novel approach to anticipating hospital bankruptcy may be useful for both hospital leaders and policymakers in making informed decisions and proactively managing risks to ensure the sustainability and stability of their institutions. Full article

The construction industry is infamous for its high insolvent failure rate because construction projects require complex processes, heavy investment, and long durations. However, there is a lack of a comprehensive framework and a requirement for such a framework in predicting the financial distress of construction firms. This paper reviews relevant literature to summarize the existing knowledge, identify current problems, and point out future research directions needed in this area using a scientometric analysis approach. Based on a total of 93 journal articles relating to predicting construction company failure extracted from multiple databases, this study conducts a holistic review in terms of chronological trends, journal sources, active researchers, frequent keywords, and most cited documents. Qualitative analysis is also provided to explore the data collection and processing procedures, model selection and development process, and detailed performance evaluation metrics. Four research gaps and future directions for predicting construction company failure are presented: selecting a broader data sample, incorporating more heterogeneous variables, balancing model predictability and interpretability, and quantifying the causality and intercorrelation of variables. This study provides a big picture of existing research on predicting construction company insolvent failure and presents outcomes that can help researchers to comprehend relevant literature, directing research policy-makers and editorial boards to adopt the promising themes for further research and development. Full article

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. Full article