Study of Merger Highlights Use of Probabilistic Financial Analysis
Authors: Carpenter Chris 
Published: December 2024

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This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 221033, “Enhancing Management Insight Into Mergers and Acquisitions Using Probabilistic Financial Analysis,” by Lachlan Hughson, SPE, 4-D Resources Advisory; Brooks J. Klimley, SPE, Brooks J. Klimley and Associates; and Michael J. Orlando, University of Colorado Denver. The paper has not been peer reviewed.

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The complete paper analyzes the 2021 merger of Cabot Oil and Gas with Cimarex Energy to understand the distinct advantages gained from applying a probabilistic approach to the financial analysis of mergers and acquisitions (M&A) and divestitures. Traditional deterministic valuation and transaction analyses, which rely on a static set of assumptions, are compared with a probabilistic approach based on Monte Carlo simulation using a readily available Excel add‑in.​​​​​

Introduction
On 24 May 2021, Cabot Oil and Gas Corporation and Cimarex Energy Company announced a $17 billion, all‑stock merger of equals whereby Cabot would issue 4.0146 of its own shares for each Cimarex share. Per the joint proxy statement/prospectus filed with the Securities and Exchange Commission on 23 August 2021, the exchange ratio was deemed “fair, from a financial point of view” by Cabot’s financial adviser, J.P. Morgan Securities (JPM), and Cimarex’s financial adviser, Tudor, Pickering, Holt and Co. (TPH), based on their respective deterministic analyses. This paper reviews and compares these deterministic analyses to a probabilistic analysis based on publicly available data.
The financial advisers are industry‑leading, with a wealth of experience and insight into undertaking a fairness opinion for a merger transaction of this type. This paper does not question their analysis or conclusions. Instead, it seeks to analyze the deterministic data used by the advisers to highlight how a probabilistic approach can significantly upgrade the valuation exercise and enhance the M&A analysis process.

Probabilistic Approach Overview
A probabilistic approach to financial modeling simply means that, instead of including one input value per cell in an Excel model and then running multiple iterations based on selectively changing the single input cells, as done with a deterministic approach, a specific range of values is incorporated into a particular cell for an input that experiences variability. A probabilistic model, therefore, generates a range of output values for a specific calculation in a particular cell, defined by their associated frequency of occurrence instead of merely one value with no sense for its particular probability.
Importantly, a probabilistic approach allows users to integrate significantly more commercial data into their financial models using both probability distribution and time‑series functions to best incorporate the variability of the actual inputs. These probabilistic input distributions can be estimated from historical data or through choosing the likely input range based on commercial experience. The user does not have to decide which individual data points to include to best represent the scenario being considered or run multiple scenarios to mimic the functionality of the Monte Carlo simulation underpinning a probabilistic approach.