Corporate financial distress: new predictors and early warning

Velia Gabriella Cenciarelli

Corporate financial distress: new predictors and early warning

Financial distress forecasting models allow to assess the future ability of companies to meet their obligations and to assess the credit risk of companies. Given the limitations of the information used so far to forecast financial distress, this study attempts to investigate whether and how different financial and non-financial variables influence the forecasting of corporate financial distress. A book that can have relevant practical implications for banks, investors, analysts, chief financial officers (CFOs) and managers in general interested in bankruptcy forecasting.

Pages: 136

ISBN: 9788835167136

Edizione:1a edizione 2024

Publisher code: 365.1324

Can print: No

Can Copy: No

Can annotate: Sì

Format: PDF con DRM for Digital Editions

Info about e-books

In recent years, due to the financial crisis, the COVID-19 pandemic, and geopolitical conflicts, corporate financial distress has evolved dramatically. The large number of bankruptcies and the increase of default risk have raised the interest for financial distress prediction models. Financial distress prediction models allow assessing the future ability of firms to meet their obligations and to evaluate the firms' credit risk. Prior research finds 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 statements and market data to assess future firm's performance. These approaches assume that the information provided by both financial statements and financial markets are reliable and enough to assess the financial health of firms. Particularly, these studies provide evidence that other variables could affect the firm's future performance and solvency (intangibles, audit, corporate governance). Given the limitation of the information used to predict financial distress, this study attempts to investigate whether and how different financial and non-financial variables influence corporate financial distress prediction.
This study can have relevant practical implications for banks, investors, analysts, chief financial officers (CFOs), and managers in general which are interested in bankruptcy prediction. Hence, new explanatory variables improve the predictive power of financial distress prediction models reducing type II error rate. This implies a significant reduction of costs for financial institutions and creditors and improve the efficiency of investor's decisions. Moreover, financial distress prediction models are critical tools for Chief Financial Officers in enabling proactive risk management, supporting strategic decision-making, and avoiding firm's bankruptcy.
Additionally, this research aligns with the European Union's recent recommendations for enhancing early warning systems and financial distress prediction. This study therefore addresses the issues that are timely and of great interest for the scientific community and policy makers.

Velia Gabriella Cenciarelli is Assistant Professor of Accounting at the School of Banking, Finance and Insurance Sciences of Catholic University of the Sacred Heart of Milan. Her main research interests are focused on financial accounting, bankruptcy prediction and credit risk. She has published papers in leading academic journals. She is member of the editorial board and ad hoc referee for National and International journals.

Giulio Greco, Preface
Introduction
Corporate financial distress in the italian "economia aziendale" and in the international literature
(Corporate financial distress phenomenon: an introduction; The historical evolution of corporate financial distress through the Italian accounting scholars; Causes of corporate financial distress; Corporate financial distress and involved parties; Early studies on corporate financial distress and bankruptcy prediction)
Literature review on corporate financial distress prediction models
(Corporate financial distress prediction models: an overview; Statistical methods to predict corporate financial distress; Machine learning methods and artificial intelligence to predict corporate financial distress; Non-financial variables to predict corporate financial distress)
Corporate financial distress: in search of new predictors
(Introduction; Chapter 11 background; Corporate financial distress and earnings management: literature review and hypothesis development; Corporate financial distress and intellectual capital performance: literature review and hypothesis development; Corporate financial distress and tax avoidance: literature review and hypothesis development; Research design; Results; Discussion and Conclusions)
Conclusions
References

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