Abstract/Summary

This study develops a predictive model to identify financial distress in large and medium-sized firms in Italy, focusing on seven key sectors classified under the Standard Industrial Classification (SIC): construction, manufacturing, transportation-communications-electric-gas-sanitary services, wholesale trade, retail trade, finance-insurance-real estate, and services. The model builds on the widely used Z”-Score model for bankruptcy prediction but modifies it by replacing the working capital to total assets ratio with the cash and cash equivalents to current liabilities ratio. This modification offers a more accurate reflection of liquidity pressures, particularly relevant for early-stage financial distress. Using Linear Discriminant Analysis (LDA) and logistic regression, the study classifies firms into distressed and non-distressed categories and estimates their likelihood of resorting to Troubled Debt Restructuring (TDR). The dataset spans multiple sectors, allowing for a sector-specific analysis of financial distress indicators. The results highlight that liquidity, leverage, and profitability ratios are critical in predicting early financial distress across industries, with distressed firms exhibiting lower liquidity and higher leverage compared to their non-distressed counterparts. While the study focuses on Italy, the model could be applied in other EU countries, such as Spain and France, which have similar TDR laws, extending its relevance across different national contexts. The findings provide valuable tools for corporate management, creditors, and investors in assessing financial risks. However, the research is limited by the small sample size, which may restrict its generalizability. Future studies could expand the application of this predictive model to a larger sample and test its effectiveness across all EU member states, enhancing its accuracy and practical applicability in a wider European context.