- Overview of Mergers & Acquisitions
- M&A: Earnings Per Share & The “Bootsrap” Effect
- Industry Lifecycle Phase and M&A
- Pre-Offer Defense Takeover Mechanisms
- Post-Offer Defense Takeover Mechanisms
- Herfindahl-Hirschman Index (HHI)
- Valuing Target Companies
- Merger Gains to Shareholders & Post Merger Valuation
- Types of Restructuring
Overview of Mergers & Acquisitions
Acquisitions: When an acquiring company buys a portion of a target company.
Merger: When an acquiring company buys all of a target company; the acquirer remains and the acquired no longer exists as an independent corporate entity.
Integration Forms
M&A transactions can be segmented by the manners in which the acquired is integrated with the acquirer.
Subsidiary Merger: the target becomes a subsidiary of the acquiring company. The acquiring company may use this form of integration in order to retain the brand recognition of the acquired entity.
Statutory Merger: the acquired no longer exists; it becomes part of the acquirer.
Consolidation: neither the acquired, nor the acquirer remain, rather both combine to form a new company.
Merger Types
Mergers can also be described by the way the business operations of the acquirer and the target relate to one another.
Horizontal Mergers: the combination of two companies in the same business line. For example, one beverage production company may decide to purchase another beverage production company.
Vertical Mergers: the purchase of a target company which performs an upstream or downstream function in the acquirer’s industry value chain.
Backward Integration: the acquirer purchases a company closer to the raw material extraction phase of the industry value chain. For example, a natural gas commercial distributer may decide to purchase a natural gas miner.
Forward Integration: the acquirer purchases a company closer to the market delivery phase of the industry value chain. For example, a gold miner may decide to purchase a chain of retail jewelry stores.
Conglomerate Merger: this is the case where an acquirer purchases a company in an unrelated line of business. For example, an airplane manufacturer may decide to purchase a chain of hospitals.
Reasons for M&A
Ideally, mergers are executed with the expectation that the target will increase the equity value of the acquirer. Below some common merger motivations are described.
- Cost Synergies: Mergers have the potential to lower costs for the combined companies, either through the elimination of redundant functions or by eliminating profits from “middle-man” points in the value chain.
- Revenue Synergies: Mergers may provide the combined companies an opportunity to cross sell complementary products.
- Growth: An acquisition might provide a company with more rapid growth potential than organic growth provided by reinvesting earnings.
- Pricing Power: A horizontal merger can reduce competition and allow the acquirer to raise its prices. A vertical merger can allow the acquirer to better control prices downstream or upstream in the value chain. When a merger has the potential to provide an acquirer with too much market power, government regulations may prevent the merger from taking place.
- Increased Capability: An acquiring company may pursue a target for its in-house technical expertise.
- Unlocking Value: An acquirer may view a target as underperforming financially and feel confident that it can facilitate the realization of the target’s full potential after taking control.
- Diversification: Companies themselves are investors who seek to reduce risk and increase returns through the successful deployment of capital.
- International M&A Concerns: Companies may engage in M&A beyond their domestic borders for multiple financial or strategic reasons.
Data Science in Finance: 9-Book Bundle
Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.
What's Included:
- Getting Started with R
- R Programming for Data Science
- Data Visualization with R
- Financial Time Series Analysis with R
- Quantitative Trading Strategies with R
- Derivatives with R
- Credit Risk Modelling With R
- Python for Data Science
- Machine Learning in Finance using Python
Each book includes PDFs, explanations, instructions, data files, and R code for all examples.
Get the Bundle for $29 (Regular $57)Free Guides - Getting Started with R and Python
Enter your name and email address below and we will email you the guides for R programming and Python.