Predicting leveraged buyout success

Table of Contents

1. Introduction

2. Literature review and definition of fixer-uppers and non-fixer-uppers

2.1 Transaction characteristics of leveraged buyouts

2.2 Development of LBOs in Continental Europe and in the United States

2.3 Review of empirically tested drivers for becoming a LBO target

2.3.1 Major findings

2.3.2 Further investigations and new aspects of empirical research

2.4 Fixer-uppers and non-fixer-uppers

3. Data and Methodology

3.1 Sample selection

3.2 Employed variables

3.3 Descriptive statistics

3.4 Methodology

4. Empirical results

4.1 Equations and controls

4.2 Major findings

5. Conclusion

Research Objectives and Core Themes

The primary research objective of this thesis is to identify company characteristics that significantly influence the probability of successfully closing a leveraged buyout (LBO) in the European market. Specifically, the study aims to determine whether the identification of a target company as a "fixer-upper" or "non-fixer-upper"—based on its growth and performance profile—serves as a predictive driver for takeover success probability, moving beyond existing research that focuses solely on the identification of potential LBO targets.

• Analysis of key firm characteristics and their impact on LBO takeover success in Europe.
• Distinction between "fixer-upper" and "non-fixer-upper" company profiles based on historical growth rates.
• Application of logit regression models to test the impact of financial variables on deal closure.
• Evaluation of the influence of firm size, cash flow, and financial structure on the LBO process.

Excerpt from the Book

Summary of Chapters

1. Introduction: Provides an overview of the financial crisis context and introduces the research gap regarding factors that influence the successful closure of LBOs in Europe.

2. Literature review and definition of fixer-uppers and non-fixer-uppers: Reviews LBO characteristics, market trends in the US and Europe, and defines the research-specific concepts of fixer-uppers versus non-fixer-uppers.

3. Data and Methodology: Details the sample selection from CapitalIQ (2000-2010), defines the regression variables, and outlines the use of logit models for testing.

4. Empirical results: Presents the regression models and discusses the findings concerning company size, cash flow, and the impact of the defined "fixer-upper" variables.

5. Conclusion: Summarizes the study's findings, acknowledges the complexity of LBO success, and suggests directions for future research.

Keywords

Leveraged Buyout, LBO, Takeover Success, Fixer-upper, Non-fixer-upper, European Market, Private Equity, Free Cash Flow, Logit Regression, Capital Structure, Debt Financing, Corporate Governance, Financial Crisis, Takeover Probability, M&A Activity.

Frequently Asked Questions

What is the core focus of this research paper?

This thesis investigates the drivers of takeover success for leveraged buyouts (LBOs) in the European market, focusing on firm characteristics that determine whether an announced LBO is successfully closed or cancelled.

What are the primary themes explored?

The study explores LBO deal construction, the role of firm size and cash flow, and proposes a classification of target companies into "fixer-uppers" and "non-fixer-uppers" to predict transaction outcomes.

What is the primary research objective?

The objective is to identify which specific company characteristics significantly influence the probability of a successful takeover, moving beyond mere target identification to assessing deal feasibility.

Which scientific methodology is applied?

The author employs a quantitative approach using logistic regression (logit models) on a sample of European LBO transactions to analyze the correlation between various financial indicators and the probability of transaction success.

What does the main section cover?

The main body covers the literature review of LBO drivers, the definition and conceptualization of the "fixer-upper" profile, a detailed description of the data collection process, and the results of the empirical regression models.

Which keywords best characterize this work?

The study is best described by keywords such as Leveraged Buyout (LBO), takeover success, fixer-upper profiles, European M&A market, and logistic regression analysis.

How does the author define a "fixer-upper"?

A "fixer-upper" is defined as a target company characterized by below-median growth rates, implying that it requires additional maintenance, financial aid, or operational restructuring after the buyout to secure success.

What is the significance of "total assets" in this research?

The research finds that company size, measured by total assets, is significantly and negatively correlated with takeover success, suggesting that larger, more complex firms are more difficult to successfully take private through LBOs.

What is the role of cash flow in the LBO process according to the findings?

The empirical results indicate that a higher cash flow-to-total assets ratio is a positive driver for LBO success, as it provides the target with the capacity to service the high debt loads typically involved in these transactions.

What limitation does the author mention regarding the fixer-upper classification?

The author notes that the definition of fixer-uppers based on the sample median of growth rates might be too simplistic and recommends that future research should use more sophisticated methods to classify these firm profiles.