Anticipating Insolvency through Modeling: Bankruptcy Prediction Techniques
In a significant development for financial researchers, a dataset of 8,262 public companies listed in the New York Stock Exchange (NYSE) or NASDAQ from 1999 to 2018 has been created. This dataset, which does not include companies listed on stock exchanges other than these two, is designed to aid in training bankruptcy prediction models.
The dataset includes a wealth of financial health data for each company, with 18 annual accounting and financial variables such as current assets or cost of goods sold. This extensive collection of data covers a broad spectrum of a company's financial performance, providing valuable insights for researchers.
The dataset lists whether or not a company filed for bankruptcy the following year, making it an invaluable tool for predictive analysis. The data was likely sourced from financial databases such as COMPUSTAT, Bloomberg, or WRDS (Wharton Research Data Services), which provide historical firm-level financial data. Access to these databases usually requires institutional or paid subscriptions.
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While the dataset itself is not publicly linked in the search results, it matches descriptions of U.S. public company datasets used in bankruptcy prediction literature. To access the dataset, researchers can check the source research papers, contact the authors, use financial databases, or look for data repositories like Harvard Dataverse or Kaggle for similar bankruptcy prediction financial datasets. If more details on the dataset name or the research paper using it are available, that can help locate a direct access path.
For those interested in exploring the financial world, this dataset offers a unique opportunity to delve into the intricacies of predicting bankruptcy and understanding the financial health of public companies. With its extensive data and wide coverage, this dataset is sure to be a valuable resource for both academic and applied research.
Researchers can utilize the newly created dataset of 8,262 public companies from 1999 to 2018, listed on the NYSE or NASDAQ, to train AI-based bankruptcy prediction models. The dataset consists of 18 financial health variables and indicates whether a company filed for bankruptcy the following year.
This collection of data, sourced from financial databases, covers a company's broad financial performance, offering valuable insights for researchers interested in data-and-cloud-computing and finance, particularly investing and the stock-market.
With its potential for predictive analysis, this dataset is an invaluable addition to technology-driven research aimed at gaining a deeper understanding of corporate financial health and predicting future bankruptcies.
To gain access to the dataset, researchers can refer to source research papers, contact authors, use financial databases like COMPUSTAT, Bloomberg, or WRDS, or explore data repositories such as Harvard Dataverse or Kaggle for similar bankruptcy prediction financial datasets.
