Distressed M&A and the role of m&a in corporate restructuring

The global financial and economic crisis have led to a moderation in global M&A activity. The large M&A deals have disappeared and deal volume has fallen. Nonetheless, M&A remains a core part of business growth. Firms continue to look for acquisitions that allow them to capture a new customer base, technologies and products, access new markets and increase market share. While some years ago the M&A market was characterized by growing firms with a healthy track record, transactions involving distressed firms are increasing. Many investors, managers, advisors and academics are familiar with traditional mergers and acquisitions but little is known about distress-related M&A. However the surge in restructurings and failures has marked the M&A landscape and triggered a growing interest in these type of transactions. The practitioner-oriented and academic literature provide us with some insights but the risks and benefits of such transactions remain largely ambiguous. The goal of this dissertation is to increase our understanding of transactions involving troubled firms

Distressed M&A and the role of M&A in corporate restructuring

The global financial and economic crisis have led to a moderation in global M&A activity. The large M&A deals have disappeared and deal volume has fallen. Nonetheless, M&A remains a core part of business growth. Firms continue to look for acquisitions that allow them to capture a new customer base, technologies and products, access new markets and increase market share. While some years ago the M&A market was characterized by growing firms with a healthy track record, transactions involving distressed firms are increasing. Many investors, managers, advisors and academics are familiar with traditional mergers and acquisitions but little is known about distress-related M&A. However the surge in restructurings and failures has marked the M&A landscape and triggered a growing interest in these type of transactions. The practitioner-oriented and academic literature provide us with some insights but the risks and benefits of such transactions remain largely ambiguous. The goal of this dissertation is to increase our understanding of transactions involving troubled firms

The risk effects of acquiring distressed firms

Existing research shows that bidder default risk increases following acquisitions due to a rise in post-acquisition leverage and managerial risk-taking actions offsetting the potential for asset diversification. This study examines whether the risk effects of acquiring distressed targets are fundamentally different and investigates possible explanations for any dissimilarities. Bidders often acquire relatively smaller distressed targets in domestic and related industries and have a higher initial target stake and more financial flexibility, thereby minimizing risk exposure. Controlling for several characteristics of bidder investment behaviour in both types of deals, however, we find that the increase in bidder default risk is substantially larger when acquiring distressed firms

The risk effects of acquiring distressed firms

Bankruptcy prediction has been a topic of research for decades, both within the financial and the academic world. The implementations of international financial and accounting standards, such as Basel II and IFRS, as well as the recent credit crisis, have accentuated this topic even further. This paper describes both regularized and non-linear kernel variants of traditional discriminant analysis techniques, such as logistic regression, Fisher discriminant analysis (FDA) and quadratic discriminant analysis (QDA). Next to a systematic description of these variants, we contribute to the literature by introducing kernel QDA and providing a comprehensive benchmarking study of these classification techniques and their regularized and kernel versions for bankruptcy prediction using 10 real-life data sets. Performance is compared in terms of binary classification accuracy, relevant for evaluating yes/no credit decisions and in terms of classification accuracy, relevant for pricing differentiated credit granting. The results clearly indicate the significant improvement for kernel variants in both percentage correctly classified (PCC) test instances and area under the ROC curve (AUC), and indicate that bankruptcy problems are weakly non-linear. On average, the best performance is achieved by LSSVM, closely followed by kernel quadratic discriminant analysis. Given the high impact of small improvements in performance, we show the relevance and importance of considering kernel techniques within this setting. Further experiments with backwards input selection improve our results even further. Finally, we experimentally investigate the relative ranking of the different categories of variables: liquidity, solvency, profitability and various, and as such provide new insights into the relative importance of these categories for predicting financial distress

The risk effects of acquiring distressed firms

Bankruptcy prediction has been a topic of research for decades, both within the financial and the academic world. The implementations of international financial and accounting standards, such as Basel II and IFRS, as well as the recent credit crisis, have accentuated this topic even further. This paper describes both regularized and non-linear kernel variants of traditional discriminant analysis techniques, such as logistic regression, Fisher discriminant analysis (FDA) and quadratic discriminant analysis (QDA). Next to a systematic description of these variants, we contribute to the literature by introducing kernel QDA and providing a comprehensive benchmarking study of these classification techniques and their regularized and kernel versions for bankruptcy prediction using 10 real-life data sets. Performance is compared in terms of binary classification accuracy, relevant for evaluating yes/no credit decisions and in terms of classification accuracy, relevant for pricing differentiated credit granting. The results clearly indicate the significant improvement for kernel variants in both percentage correctly classified (PCC) test instances and area under the ROC curve (AUC), and indicate that bankruptcy problems are weakly non-linear. On average, the best performance is achieved by LSSVM, closely followed by kernel quadratic discriminant analysis. Given the high impact of small improvements in performance, we show the relevance and importance of considering kernel techniques within this setting. Further experiments with backwards input selection improve our results even further. Finally, we experimentally investigate the relative ranking of the different categories of variables: liquidity, solvency, profitability and various, and as such provide new insights into the relative importance of these categories for predicting financial distress