Enhancing Management Insight into Mergers & Acquisitions Using Probabilistic Financial Analysis
Abstract Between 2011 and 2022 greater than 50% of global E&P merger and acquisition (M&A) transactions destroyed shareholder value with only 25% generating approximately 60% of total excess returns, per a 2024 paper from McKinsey & Co.1 Upgrading the financial analysis approach to provide better insights for M&A decision-making professionals is therefore increasingly important to enhance their probability of success. This paper analyzes the 2021 merger of Cabot Oil & Gas with Cimarex Energy (the Transaction) to understand the distinct advantages gained from applying a probabilistic approach to the financial analysis of mergers, acquisitions and divestitures. Traditional deterministic valuation and transaction analyses, which rely on a static set of assumptions, are compared to a probabilistic approach based on Monte Carlo simulation using a readily available Excel add-in. This paper reviews and utilizes the additional insights from the probabilistic output, generated from the input probability distribution functions and correlation coefficients chosen, unavailable from a traditional, deterministic financial approach. The probabilistic approach provides an enhanced transaction analysis process that produces the critical metrics company Boards and shareholders increasingly require in determining if a transaction should be undertaken. This paper highlights how a probabilistic approach to the M&A process: (i) is straightforward to implement; (ii) facilitates a greater use of real-world data; (iii) generates outcome probabilities for key transaction metrics unavailable with a deterministic approach; and (iv) provides unique risk insights into those transaction metrics. This probabilistic approach to the M&A process represents a significant, practical improvement to management's ability to evaluate specific transactions and enhance their likelihood of success.
Authors
- Michael J. Orlando
- B. J. Klimley
- L. Hughson
Institutions
- University of Colorado Denver (US)
Publication Details
- Journal
- SPE Annual Technical Conference and Exhibition
- Published
- 2024-09-20
- DOI
- https://doi.org/10.2118/221033-ms
- Primary Topic
- Stock Market Forecasting Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00