In Mergers and Acquisitions (M&A), accurate financial modelling can determine a deal’s success or failure. At the heart of every robust merger or acquisition model are the assumptions and inputs used. These critical data inputs shape strategic decisions and influence valuations.
In this guide, we’ll cover mastering assumptions and data inputs in mergers and acquisitions financial modelling, enriched with expert insights and real-world examples.
For those looking to dive deeper into M&A, our PE Funds database, Merger Model, and WSO Modelling Elite Programmes offer valuable insights.
As defined by Investopedia, M&A involve the consolidation of companies or their assets through various financial transactions. A merger combines two firms into a new legal entity, while an acquisition occurs when one company purchases another outright.
Revenue growth rates are a fundamental assumption in M&A financial modelling. They predict the future sales performance of the combined entity. Accurate growth rates are derived from a thorough analysis of market trends, historical performance, and competitive positioning. According to a study by McKinsey, companies that accurately project their revenue growth are 60% more likely to achieve their performance targets.
The cost of capital includes both the cost of debt and the cost of equity. This assumption impacts the discount rate used in Discounted Cash Flow (DCF) analysis and the overall valuation of the target company. It’s essential to base this assumption on current market conditions and the specific risk profile of the companies involved.
An acquisition premium is the extra amount paid over the market value of the target company. This premium reflects the expected synergies and strategic benefits of the acquisition. Historical data suggests that acquisition premiums typically range from 20% to 40% but can vary based on industry and market conditions.
Synergies are the expected cost savings and revenue gains from an acquisition or merger. These may come from cost reductions, market expansion, and operational efficiencies. Accurately estimating synergies requires a deep understanding of both companies’ operations, cost structures, and strategic alignment.
Types of Synergies [Source: Dealroom]
Data gathering and analysis is the foundaation of creating real, accurate assumptions. To build advanced M&A models, you must be proficient in gathering and analyzing data from various sources, including industry reports, financial statements, and market research. Here’s how to approach it:
A study by Harvard Business Review found that deals backed by robust, data-driven assumptions are 30% more likely to succeed. This underscores the importance of thorough data analysis in M&A modelling.
Assumptions | Best Case | Optimistic Case | Pessimistic Case |
Revenue Growth | 5% | 8% | 3% |
Cost of Debt | 4% | 3.5% | 4.5% |
Synergy Realization | 80% | 100% | 60% |
This table illustrates how different assumptions affect the financial projections of a merger. By adjusting key drivers—such as revenue growth, cost of debt, and synergy realization—analysts can assess best-case, base-case, and worst-case outcomes.
Becoming a top notch financial modeller requires proficiency in various tools and techniques. While Excel remains the foundation of financial modelling, integrating other tools like Python or R for data manipulation and visualization are essential in today’s job market.
Using Python, for example, you can automate data extraction and analysis, making it easier to update your financial model with the latest data. This can save time and reduce the risk of errors in manual data entry.
In the acquisition of YouTube by Google, Google meticulously documented their assumptions about video consumption trends and advertising revenue potential. This helped them make informed decisions and manage investor expectations.
M&A financial modelling blends technical skills with strategic thinking, requiring a diverse skill set to stay competitive. Precision in data inputs, well-founded assumptions, and rigorous scenario analysis are essential for assessing deals with confidence.
Focus on structuring models that are both dynamic and intuitive — clarity and adaptability matter as much as accuracy. Always stress-test key assumptions, as even the best models are only as good as the inputs behind them.
P.S. – Explore our Premium Resources, including downloadable M&A model Excel templates and advanced financial modelling programs, to enhance your skills further.
