RISK PLANNING AND BUSINESS CRISIS MANAGEMENT

The course provides students with the theoretical and empirical tools to understand, measure, and manage firms’ financial risks. After introducing capital structure theory and financing choices, it covers debt limits, financial distress, and default prediction models, concluding with advanced topics such as classification and survival analysis.
A special focus is placed on statistical modeling of corporate and financial data, using logistic regression and survival models for default prediction, with hands-on applications on real datasets.

- Analyze the capital structure and the factors that influence the risk of bankruptcy;
- Understand the mechanisms of financial distress and insolvency;
- Apply empirical models for risk and default prediction (logit);
- Implement survival models and Cox regression;
- Utilize statistical software (Python) for empirical analysis on company data.

Basic statistics, econometrics, and principles of corporate finance.
An introductory knowledge of a programming language is required, for instance Python, R, MATLAB, STATA, or JULIA — not limited to any specific one.

Part I – Fundamentals and Capital Structure
Theory of capital structure: trade-off, pecking order, and empirical determinants.
The debt limit and market frictions.

Part II – Financial Distress and Default Prediction
Financial distress and insolvency: definitions, measures, and empirical implications.
Evidence and cases of distress in the Italian context.
Literature review on default predictive models.

Part III – Quantitative Methods
Classification models: logistic regressions
Survival analysis: censoring, hazard and survival functions.
The Cox model and log-rank tests.
Extensions: time-dependent covariates, regularization (Ridge, Lasso, Elastic Net).
Performance evaluation: AUC, C-index, economic interpretation.

- Capital Structure: Basic Concepts in Hillier, D. J., Ross, S. A.,Westerfield, R.W., Jaffe, J.,&Jordan, B. D. (2010). Corporate finance.McGrawHill.
- Capital Structure: Limits to the use of Debt in Hillier, D. J., Ross, S. A., Westerfield, R.W., Jaffe, J.,&Jordan, B. D. (2010). Corporate finance. McGrawHill.
- Casino-Martínez, A., López-Gracia, J.,&Mestre-Barberá, R. (2025). Capital structure and institutional status in the EuropeanUnion. Empirica, 1-36.
- Financial Distress in Hillier, D. J., Ross, S. A.,Westerfield, R.W., Jaffe, J.,& Jordan, B. D. (2010). Corporate finance.McGrawHill.
- Corporate financial distress in the Italian “economia aziendale” and in the international literature in Cenciarelli, V. G. Corporate Financial Distress: New Predictors and Early Warning. 2020.
- Literature Review On Corporate Financial Distress PredictionModels in Cenciarelli, V. G. Corporate Financial Distress: New Predictors and EarlyWarning. 2020
- Crisi d’impresa e sue previsioni: Un approccio economico-aziendale in Ziliotti, M.,& Marchini, P. L. (2014). Analisi economica e modelli di Crisi d’impresa.
- James, G., Witten, D., Hastie, T., Tibshirani, R., Taylor, J. (2023). An Introduction to Statistical Learning: with Applications in Python, Springer.
- Slides delle lezioni

Learning outcomes will be assessed through a written exam aimed at evaluating both theoretical understanding and the ability to apply quantitative methods to real-world contexts. The evaluation may also include the presentation and discussion of case studies or short empirical project works, either individually or in groups, related to financial risk analysis and default prediction.

written

The final grade is expressed on a 0–30 scale. The top grade of 30 cum laude corresponds to an excellent performance, demonstrating complete mastery of theory, methods, and applications. Grades between 28 and 30 indicate a very good level, showing in-depth and autonomous understanding. A mark between 25 and 27 reflects good and solid knowledge, with minor inaccuracies. Grades between 22 and 24 denote a satisfactory understanding with some gaps. Scores between 18 and 21 indicate sufficient comprehension of the main topics. A result below 18 is considered a fail and indicates inadequate knowledge of the subject.

Lectures and case studies discussions