Corporate financial distress: new predictors and early warning

The number of bankruptcy was formerly few. However, over the last 30 years, due the financial crisis, COVID-19 pandemic, and energetic issues, corporate financial distress has evolved dramatically. The large number of bankruptcy and the increase of default risk has raised the interest for bankruptcy prediction model. Academic literature on accounting and finance has paid attention on bankruptcy prediction in order to find the variables that improve its predictive ability. Bankruptcy prediction models, also called credit scoring models, allow to assess the future ability of firms to meet its obligations and to evaluate firms’ credit risk. Prior researches find two approaches to predict the likelihood of firm’s default: accounting-based bankruptcy prediction models and market-based bankruptcy prediction models. These models use historical financial and market data to assess future firm’s performance. Since financial statement allows firms to share information about current and expected performance with stakeholders and market, the accounting-based bankruptcy models use information gathered from financial statement to predict the likelihood of firm’s default by distinguishing between financially distressed firms and “healthy” firms. The latter approach is based on the assumption that the information provided by financial statement are reliable and enough to assess the financial health of firms. Nevertheless, there is no clear consensus in the literature on which variables are good bankruptcy predictors. However, in recent years academic literature of accounting and finance highlights the limitation of financial ratios as predictors of bankruptcy (Beaver, 2005). Particularly, these studies provide evidence that other variables could affects firm’s future performance and solvency. Given the limitation of the information gathered from financial statement (financial ratios) to predict default, this book attempts to investigate whether and how different financial and non-financial variables influence corporate financial distress and bankruptcy prediction. In this book, we investigate the relevance of including “new” variables in traditional credit scoring models. We develop new models for public firms that predict financial distress and bankruptcy. Unlike previous studies, the models applied in this work use a combination of financial ratios, earnings management information, intellectual capital performance proxies and tax avoidance variables to analyze whether models containing these three kinds of variables are able to enhance the predictive power of bankruptcy prediction models. The purpose of present study is to provide models with high predictive ability by reducing the misclassification between bankrupt firms and non-bankrupt firms. Specifically the book aims to produce more reliable bankruptcy prediction models for creditors, investors, analysts and firm’s stakeholders in general.