How do key performance ratios predict financial distress across industries within european multinational corporations and how can managers use this information for decision-making depending on the executive leadership style

Abstract

This thesis explores how key financial performance ratios, specifically those related to profitability, liquidity, and solvency, can be used to predict financial distress across European multinational corporations (MNCs). The research shows an improvement in predictive accuracy by applying and comparing a newly developed logistic regression model with commonly recognized predictive models like the Altman Z-Score and Ohlson O-Score. The new model correctly identifies 16 distress scenarios, while the traditional models only identified 14 and 12, respectively. In order to comprehend how various executive styles perceive and react to distress signals, the study also presents an innovative interdisciplinary viewpoint by including leadership theory, particularly the transformational and transactional leadership frameworks. The results demonstrate the importance of leadership in mediating the practical application of predicted insights, hence supporting the applicability of contingency theory in financial crisis management. Additionally, while the model shows strong predictive power in the context of European corporations, its precision and usefulness might be greatly improved by enlarging the dataset by covering a wider and more varied variety of multinational companies. By incorporating regional legislative, cultural, and economic variances, this expansion would enhance the model's resilience and generalisability. Finally, by emphasising the value of integrating strategic, leadership-driven decision-making frameworks with quantitative financial diagnostics, this study advances both financial and management literature. It has useful ramifications for executive leadership development in high-stakes environments, predictive analytics development, and corporate risk management.