This file contains a list of the bankruptcy cases for public companies filed under Chapter 11 of the Bankruptcy Code opened and monitored since the fiscal year 2009.

Bankruptcies in the United States decreased to 23043 Companies in the second quarter of 2025 from 23309 Companies in the first quarter of 2025. This dataset provides - United States Bankruptcies - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Summary The proposed competition challenges professionals to analyze a custom dataset of Italian companies that went bankrupt in 2023. The dataset contains company IDs categorized by industry:

Axxxxx: Commerce Bxxxxx: Online Services Cxxxxx: Hospitality Dxxxxx: Construction Exxxxx: Restaurants Fxxxxx: Industry Gxxxxx: Startups The primary goal is to utilize machine learning algorithms to:

Identify Key Variables: Determine which features in the dataset significantly influence bankruptcy. Class-Based Analysis: Investigate whether different variables affect bankruptcy across company categories (ID groups). Predict Bankruptcy: Build models to predict the likelihood of bankruptcy based on the data. Handle Multicollinearity: Identify and mitigate issues of multicollinearity among variables. Evaluate Models: Use performance metrics like confusion matrices, ROC curves, AUC scores, and other evaluation criteria. Minimize False Positives: Achieve a balance between detection rate and false alarm rate while ensuring the models remain reliable, fast, and interpretable. Machine Learning Techniques Participants are required to apply and compare multiple machine learning algorithms, including:

Random Forest Support Vector Machines (SVM) Neural Networks Linear and Logistic Regression Models must be trained and tested on appropriate data splits, with comprehensive analysis performed on both training and test datasets.

Analysis This task involves several critical steps in machine learning analysis and evaluation:

Data Preparation and Feature Selection

Perform feature selection to reduce dimensionality and enhance model interpretability. Consider techniques to remove redundant or irrelevant features while retaining the most informative variables. Model Evaluation Metrics Participants will evaluate models using multiple performance metrics:

Predictive Accuracy: Ratio of correct predictions to total cases. True Positive Rate (Recall/Sensitivity): Ability of the model to detect bankrupt companies correctly. False Positive Rate (Fall-Out): Percentage of companies incorrectly classified as bankrupt. Precision: Proportion of true positives among all predicted positives. F1-Score: Weighted harmonic mean of precision and recall. Confusion Matrix: Visualization of classification performance. ROC Curve and AUC: Trade-offs between detection rate and false alarm rate. Class-Specific Analysis

Explore whether distinct variables influence bankruptcy differently across industry categories (e.g., financial variables for commerce vs. operational metrics for startups). Multicollinearity

Address multicollinearity using techniques like Variance Inflation Factor (VIF) to ensure that correlated predictors do not negatively impact model performance. Trade-Off Between Performance and Practicality

Balance high accuracy and low false positive rates with the need for computational efficiency and interpretability. Key Objectives for Participants Develop robust and scalable models to predict bankruptcy effectively. Identify critical variables for each industry group and assess their influence. Provide actionable insights through clear and concise visualizations and reports. Optimize performance to achieve reliable predictions while minimizing false alarms. Demonstrate expertise in applying machine learning algorithms and interpreting their results. This competition not only tests the technical capabilities of participants but also their ability to derive meaningful business insights from data, making it a compelling challenge for data science professionals.

As of January 2025, the largest all-time bankruptcy in the United States remained Lehman Brothers. The New York-based investment bank had assets worth 691 billion U.S. dollars when it filed for bankruptcy on September 15, 2008. This event was one of the major points in the timeline of the Great Recession, as it was the first time a bank of its size had failed and had a domino effect on the global banking sector, as well as wiping almost five percent of the S&P 500 in one day. Bank failures in the U.S. In March 2023, for the first time since 2021, two banks collapsed in the United States. Both bank failures made the list of largest bankruptcies in terms of total assets lost: The failure of Silicon Valley Bank amounted to roughly 209 billion U.S. dollars worth of assets lost, while Signature Bank had approximately 110.4 billion U.S. dollars when it collapsed. These failures mark the second- and the third-largest bank failures in the U.S. since 2001. Unprofitable banks in the U.S. The collapse of Silicon Valley Bank and Signature Bank painted an alarming picture of the U.S. banking industry. In reality, however, the state of the industry was much better in 2022 than in earlier periods of economic downturns. The share of unprofitable banks, for instance, was 3.4 percent in 2022, which was an increase compared to 2021, but remained well below the share of unprofitable banks in 2020, let alone during the global financial crisis in 2008. The share of unprofitable banks in the U.S. peaked in 2009, when almost 30 percent of all FDIC-insured commercial banks and savings institutions were unprofitable.

Bankruptcies in Japan increased to 965 Companies in October from 873 Companies in September of 2025. This dataset provides - Japan Bankruptcies - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The dataset is about bankruptcy prediction of Polish companies. The data was collected from Emerging Markets Information Service (EMIS), which is a database containing information on emerging markets around the world. The bankrupt companies were analyzed in the period 2000-2012, while the still operating companies were evaluated from 2007 to 2013. Basing on the collected data five classification cases were distinguished, that depends on the forecasting period: - 1stYear – the data contains financial rates from 1st year of the forecasting period and corresponding class label that indicates bankruptcy status after 5 years. The data contains 7027 instances (financial statements), 271 represents bankrupted companies, 6756 firms that did not bankrupt in the forecasting period. - 2ndYear – the data contains financial rates from 2nd year of the forecasting period and corresponding class label that indicates bankruptcy status after 4 years. The data contains 10173 instances (financial statements), 400 represents bankrupted companies, 9773 firms that did not bankrupt in the forecasting period. - 3rdYear – the data contains financial rates from 3rd year of the forecasting period and corresponding class label that indicates bankruptcy status after 3 years. The data contains 10503 instances (financial statements), 495 represents bankrupted companies, 10008 firms that did not bankrupt in the forecasting period. - 4thYear – the data contains financial rates from 4th year of the forecasting period and corresponding class label that indicates bankruptcy status after 2 years. The data contains 9792 instances (financial statements), 515 represents bankrupted companies, 9277 firms that did not bankrupt in the forecasting period. - 5thYear – the data contains financial rates from 5th year of the forecasting period and corresponding class label that indicates bankruptcy status after 1 year. The data contains 5910 instances (financial statements), 410 represents bankrupted companies, 5500 firms that did not bankrupt in the forecasting period.

Attribute Information:

X1 net profit / total assets X2 total liabilities / total assets X3 working capital / total assets X4 current assets / short-term liabilities X5 [(cash + short-term securities + receivables - short-term liabilities) / (operating expenses - depreciation)] * 365 X6 retained earnings / total assets X7 EBIT / total assets X8 book value of equity / total liabilities X9 sales / total assets X10 equity / total assets X11 (gross profit + extraordinary items + financial expenses) / total assets X12 gross profit / short-term liabilities X13 (gross profit + depreciation) / sales X14 (gross profit + interest) / total assets X15 (total liabilities * 365) / (gross profit + depreciation) X16 (gross profit + depreciation) / total liabilities X17 total assets / total liabilities X18 gross profit / total assets X19 gross profit / sales X20 (inventory * 365) / sales X21 sales (n) / sales (n-1) X22 profit on operating activities / total assets X23 net profit / sales X24 gross profit (in 3 years) / total assets X25 (equity - share capital) / total assets X26 (net profit + depreciation) / total liabilities X27 profit on operating activities / financial expenses X28 working capital / fixed assets X29 logarithm of total assets X30 (total liabilities - cash) / sales X31 (gross profit + interest) / sales X32 (current liabilities * 365) / cost of products sold X33 operating expenses / short-term liabilities X34 operating expenses / total liabilities X35 profit on sales / total assets X36 total sales / total assets X37 (current assets - inventories) / long-term liabilities X38 constant capital / total assets X39 profit on sales / sales X40 (current assets - inventory - receivables) / short-term liabilities X41 total liabilities / ((profit on operating activities + depreciation) * (12/365)) X42 profit on operating activities / sales X43 rotation receivables + inventory turnover in days X44 (receivables * 365) / sales X45 net profit / inventory X46 (current assets - inventory) / short-term liabilities X47 (inventory * 365) / cost of products sold X48 EBITDA (profit on operating activities - depreciation) / total assets X49 EBITDA (profit on operating activities - depreciation) / sales X50 current assets / total liabilities X51 short-term liabilities / total assets X52 (short-term liabilities * 365) / cost of products sold) X53 equity / fixed assets X54 constant capital / fixed assets X55 working capital X56 (sales - cost of products sold) / sales X57 (current assets - inventory - short-term liabilities) / (sales - gross profit - depreciation) X58 total costs /total sales X59 long-term liabilities / equity X60 sales / inventory X61 sales / receivables X62 (short-term liabilities *365) / sales X63 sales / short-term liabilities X64 sales / fixed assets

Bankruptcies in Germany decreased to 1979 Companies in August from 2197 Companies in July of 2025. This dataset provides - Germany Bankruptcies - actual values, historical data, forecast, chart, statistics, economic calendar and news.

In 2023, there were ****** cases of business bankruptcy filed nationwide in the United States. While an increase from the previous year, the number of business bankruptcies in the United Stats has seen an overall decline, reaching a peak in 2009.

Bankruptcies in Sweden increased to 1028 Companies in October from 811 Companies in September of 2025. This dataset provides - Sweden Bankruptcies - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Bankruptcies in Australia increased to 1481 Companies in October from 1104 Companies in September of 2025. This dataset provides - Australia Bankruptcies - actual values, historical data, forecast, chart, statistics, economic calendar and news.

In 2023, about 6,500 companies were declared bankrupt in Spain, which represented nearly 700 less companies declared bankrupt than the previous year. The number of companies declared bankrupt decreased considerably during the first half of the period under consideration, although it started increasing from 2020 onwards.

In the fourth quarter of 2024, 95 economic entities filed for bankruptcy in Poland, a three percent decrease from the previous year's corresponding period. Most of these cases occurred in the industry sector.

ABSTRACT This study sought to analyze the variables that can influence company bankruptcy. For several years, the main studies on bankruptcy reported on the conventional methodologies with the aim of predicting it. In their analyses, the use of accounting variables was massively predominant. However, when applying them, the accounting variables were considered as homogenous; that is, for the traditional models, it was assumed that in all companies the behavior of the indicators was similar, and the heterogeneity among them was ignored. The relevance of the financial crisis that occurred at the end of 2007 is also observed; it caused a major global financial collapse, which had different effects on a wide variety of sectors and companies. Within this context, research that aims to identify problems such as the heterogeneity among companies and analyze the diversities among them are gaining relevance, given that the sector-related characteristics of capital structure and size, among others, vary depending on the company. Based on this, new approaches applied to bankruptcy prediction modeling should consider the heterogeneity among companies, aiming to improve the models used even more. A causal tree and forest were used together with quarterly accounting and sector-related data on 1,247 companies, 66 of which were bankrupt, 44 going bankrupt after 2008 and 22 before. The results showed that there is unobserved heterogeneity when the company bankruptcy processes are analyzed, raising questions about the traditional models such as discriminant analysis and logit, among others. Consequently, with the large volume in terms of dimensions, it was observed that there may be a functional form capable of explaining company bankruptcy, but this is not linear. It is also highlighted that there are sectors that are more prone to financial crises, aggravating the bankruptcy process.

From UCI Machine Learning Repository: SOURCE

Brief Introduction to Dataset

The dataset is about bankruptcy prediction of Polish companies.The bankrupt companies were analyzed in the period 2000-2012, while the still operating companies were evaluated from 2007 to 2013.

Overview:

The dataset consists of financial data used to predict bankruptcy among Polish companies. The data was collected from the Emerging Markets Information Service (EMIS, http://www.securities.com), focusing on the period from 2000 to 2013. The original dataset included five separate files, each corresponding to different forecasting periods. These periods reflect varying years of financial data and their corresponding bankruptcy status.

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Original Files and Forecasting Periods

1stYear.arff: Contains financial data from the 1st year of the forecasting period with bankruptcy status after 5 years. It includes 7,027 instances (271 bankrupted, 6,756 non-bankrupted).

2ndYear.arff: Contains financial data from the 2nd year with bankruptcy status after 4 years. It includes 10,173 instances (400 bankrupted, 9,773 non-bankrupted).

3rdYear.arff: Contains financial data from the 3rd year with bankruptcy status after 3 years. It includes 10,503 instances (495 bankrupted, 10,008 non-bankrupted).

4thYear.arff: Contains financial data from the 4th year with bankruptcy status after 2 years. It includes 9,792 instances (515 bankrupted, 9,277 non-bankrupted).

5thYear.arff: Contains financial data from the 5th year with bankruptcy status after 1 year. It includes 5,910 instances (410 bankrupted, 5,500 non-bankrupted).

Bankruptcies in Italy increased to 1991 Companies in the third quarter of 2023 from 1889 Companies in the second quarter of 2023. This dataset provides - Italy Bankruptcies- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Bankruptcy dataset is a dataset which contains the financial information and the bankruptcy status of the companies for specific years. Variable D is the Bankruptcy/Non Bankruptcy flag, where 1 stands for Bankruptcy while 0 stands for Non Bankrupt companies. Variables R1 to R24 contain financial information which will be used while building a logistic regression model.

United States Number of Bankruptcy Filings: Annual: Non Business data was reported at 765,863.000 Unit in 2017. This records a decrease from the previous number of 770,846.000 Unit for 2016. United States Number of Bankruptcy Filings: Annual: Non Business data is updated yearly, averaging 873,540.000 Unit from Dec 1980 (Median) to 2017, with 38 observations. The data reached an all-time high of 2,039,214.000 Unit in 2005 and a record low of 284,517.000 Unit in 1984. United States Number of Bankruptcy Filings: Annual: Non Business data remains active status in CEIC and is reported by Administrative Office of the United States Courts. The data is categorized under Global Database’s United States – Table US.O013: Number of Bankruptcy Filings.

Business bankruptcies represent the total bankruptcy filings all business entities make in a calendar year. Data is sourced from the Administrative Office of the US Courts.

This paper explores predicting early signals of business failure using modern models for bankruptcy prediction. It reviews how continuous operations enhance market value, strengthening competitiveness and reputation among stakeholders. The study involves medium and large companies in the Montenegrin market from 2015 to 2020, comprising 30 bankrupt and 70 financially stable firms. Logistic regression is also employed to create a logit model for early detection of bankruptcy signals in companies. This research establishes the empirical validity of modern models in predicting business failure in the Montenegrin market, particularly through logistic regression. Significant indicators, such as the Degree of Indebtedness (DI) and turnover ratio of business assets (TR), exhibit strong predictive power with a p-value less than 0.001 according to Likelihood ratio tests. The paper underscores the potential benefits of bankruptcy prediction for both internal and external stakeholders, especially investors, in enhancing the competitiveness of Montenegro’s large and medium-sized companies. Notably, the research contributes by bridging the gap between theory and practice in Montenegro, as bankruptcy prediction models have not been extensively applied in the market. The authors suggest the possible applicability of the created logit model to neighboring countries with similar economic development levels. In that sense, the concept of predicting bankruptcy is positioned as integral to corporate strategy, impacting the overall reduction of bankruptcies. The paper concludes by highlighting its role as a foundation for future research, addressing the literature gap in the application of bankruptcy prediction models in Montenegro. The created logit model, tailored to the specific needs of Montenegrin companies, is presented as an original contribution, emphasizing its potential to strengthen the competitiveness of companies in the market.

In 2024, the total liabilities of bankrupt companies in Japan amounted to *** trillion yen, a decrease of about *** percent from the previous year. The number of corporate bankruptcies in the country rose by **** percent year on year.