Figuring the Odds On Whether a Deal Will Succeed: A newly devised model allows executives to calculate the probability of merger success up front, based on the deal’s most important drivers.

The worldwide volume of mergers and acquisitions reached a record level for the fourth consecutive year in 1998, with the value of announced transactions exceeding $2.4 trillion. Mergers and acquisitions are realigning many industries, including telecommunications, technology, pharmaceuticals, banks, financial services, media, automobiles, utilities, and natural resources. Many analysts expect m&a to continue at a record pace in 1999. There are many reasons for the record level of mergers and acquisitions, including a vibrant U.S. economy, competition, globalization, economies of scale, deregulation, technological change, and the pursuit of shareholder wealth maximization. However, only about one-third of mergers and acquisitions historically have been considered successful. The high failure rate was most recently confirmed by Richard Z. Gooding in the September/October 1998 edition of Financial Executive and Joseph C. Krallinger in the September 1998 issue of Managerial Accounting. Both Gooding and Krallinger blamed the failure of the buying firm’s management to thoroughly investigate the seller while Krallinger also asserted that the buyer is too optimistic about the merger and pays too high a premium for the target. Numerous other studies identified the failure to achieve successful postmerger integration as a major cause. Given the myriad reasons for doing mergers and acquisitions and the variety of causes for the low rate of success, we conclude that acquirers need to do a better screening job before approaching their targets and, ideally, to adopt a universal process that calculates the odds of success or failure that can be adapted to different situations. In this article, we introduce a quantitative framework for screening acquisition candidates that is systematic, simple to use, and easy to understand. A distinctive feature of this approach is the estimation of the probability of acquisition success. The approach enables acquirer executives to quantify the risks involved in an acquisition and quickly rank alternative acquisition candidates. This preliminary screen isolates the acquisition candidates that merit more thorough analysis, resulting in a significant savings of time. With that discipline, there will be less of a tendency for the buyer to overpay for the target or to become overly enamored with the acquisition because the probability of merger success adjusts the value of the transaction based on what it can deliver to the combined firm. The approach incorporates multiple criteria, relies on the knowledge and experience of the management, and is directly linked to the firm’s strategic objectives. Typically, executives subjectively estimate the likelihood of merger success. Inconsistency and confusion arise because of the multiple factors that affect this estimation. What is needed is a relatively straightforward method of probability estimation that will leave management reasonably confident in the numerical estimate. Difficult mathematical approaches are to be avoided because they are usually impractical to implement and incomprehensible to most executives. The approach described in this article meets these needs and is an extension of a technique that we developed in 1980 to evaluate the probability of success of research and development projects. Like mergers and acquisitions, R&D projects have low probabilities of success, although the reasons for success or failure differ. Application of the Screen Mergers and acquisitions, like other corporate investments, must be consistent with corporate strategy and increase shareholder wealth. Accordingly, it is necessary to evaluate the target company with respect to the acquirer’s profit, growth, and other corporate objectives. In addition, appraisals of the management, marketing activities, and operations of the target company must be made. Strategic fit often is viewed as a primary corporate objective and a critical factor in merger and acquisition analysis. An example of strategic fit is a horizontal merger in which the target operates in the same line of business as the acquirer and is able to increase the geographical coverage of the combined firm. If the two firms that are merging have complementary resources, and similar management and operating philosophies, production processes, and accounting systems, the merger may be viewed as a good strategic fit. Empirical research indicates that the likelihood of merger success increases when the target company has a dominant market share and modern plant and equipment and its organizational culture is similar to that of the acquiring firm. The knowledge and experience of managers and the retention of managers after the merger often are viewed as key factors in merger success. Mergers and acquisitions in industries in which there are high barriers to entry also are associated with merger success. Accordingly, these market-centered factors may be assigned higher weights in the evaluation process. Compatible organizational cultures are an important component of successful mergers because human conflicts that often arise in postmerger integration can be reduced or avoided. Among the issues that arise are reporting responsibilities, compensation and employee benefits, technology, and physical location of employees and managers in the two merger partners. In the January/February 1988 edition of The Bankers Magazine, Joseph V. Rizzi recommended the formation of special integration task force teams and establishment of clear postmerger standards based on the strategic rationale for the merger to facilitate the postmerger integration. To combine these multiple criteria in a meaningful way, we suggest a weighting system for key corporate elements associated with the merger that are impounded in the Probability Schedule shown in Table 1. The probability of merger success is determined by the following (Equation 1): P = Pc x Pm x Po Where: P = Probability of merger success Pc = Probability of achieving corporate objectives Pm = Probability of marketing success Po = Probability of operational success The equation is applied in the final section of the Probability Schedule table in Probability Estimation, which, as the ultimate step in the screening process, incorporates all prior calculations. Note that the preliminary work focuses on three key criteria for advancing a corporate development project: Corporate Objectives, Operations, and Marketing. In turn, each of these broad measures is subdivided by its most influential components. This multiplicative model assumes that achieving corporate objectives, marketing success, and operational success are independent events but that the process will tie them together to signal an intelligent course of action. This approach provides great flexibility for the acquirer’s management, which presumably is best informed about its own markets and related markets. Management must decide exactly what factors to include in the model and must assign weights (w’s) to those factors based on their importance to the buyer. Relatively more important factors would be assigned higher weights under that system. Notice that the weights add up to 1.0 in each case. The entire process not only helps in valuing and pricing the target based on its strengths and weaknesses and what it can contribute to the buyer but also gets the acquirer’s managers focused on what is really important in the deal. The raw score attached to each factor should be based on the informed subjective judgment of the managers. These raw scores are assumed to be normally distributed, which means that they have a symmetric bell-shape so that there is an equal chance of a positive raw score or a negative raw score. In Table 1, the assigned raw scores vary from +2.0 to -2.0. A +2.0 score implies that the merger makes the greatest contribution to achieving a particular factor. Similarly, a merger that makes no contribution for a particular factor is assigned a -2.0. The conversion of net scores to probability estimates requires the use of a table of the cumulative normal distribution. An abridged version of this table is shown in Table 2. The net scores in Table 1 represent Z values from Table 2. For example, a net score of 1.0 on the probability of achieving corporate objectives corresponds to a success probability of approximately 84% while a net score of 2.0 on the probability of achieving marketing success corresponds to a success probability of approximately 98%. These probabilities represent areas under a normal curve. Notice in Table 2 that a net score of 0 yields a probability of 50%, which means that there is only a 50-50 chance of reaching the goals on a relevant factor. The ability to identify the key factors and assign them proper weights provides a great degree of depth in all phases of the screening, including the comparison of the candidates on a list of prospects. For example, assume that a company is considering two different targets and is greatly concerned with Operational Efficiency. Target A has state-of-the-art manufacturing facilities and the highest rate of operating efficiency in its industry while Target B has one of the lowest levels of operating efficiency, suggesting, among other things, that substantial follow-on investment will be needed if B is selected. The evaluator then might assign Target A a raw score of +2.0 and Target B a raw score of -2.0 for this factor. Connecting to Merger Valuation Another key step in the screening process is to correlate the probability outcomes with a merger valuation that can be made by using standard investment appraisal techniques. The weighted average cost of capital method or the flows-to-equity method described by Stephen A. Ross, Randolph W. Westerfield, and Jeffrey Jaffe in the 1999 edition of their book, Corporate Finance, is one example. Alternatively, if the merger is of a size and nature that is expected to significantly change the weighted average cost of capital of the combined firm, then the merger valuation framework developed by J. Fred Weston, Kwang S. Chung, and Susan E. Hoag in their 1990 book, Mergers, Restructuring, and Corporate Control, is recommended. The gain or benefit from the merger is defined as the difference between the present value of the cash flows for merged firms and the sum of their values if they do not merge, as shown in the following (Equation 2): Gain = Vab (Va + Vb) Where Gain = gain, or synergy, from the merger Vab = Value of the combined firm, if the merger is successful Va = Value of firm A Vb = Value of firm B To adjust the results of the valuation calculations for the probability of merger success, the following modification, which represents a risk adjustment, is made (Equation 3): Adjusted Gain = P x Gain Where P = the probability of merger success (from Equation 1) Gain = increase in value from the merger (from Equation 2) The merger is recommended if the adjusted gain exceeds the cost of the merger, which includes the premium paid to the target company. This risk adjustment lowers the expected value of the merger and makes it less likely that the acquiring firm will pay too high a premium for the target company. The acquirer’s executives will have a reasonable basis to screen alternative acquisition candidates, based on their adjusted values, and conduct price negotiations with the target company eventually selected for acquisition. To illustrate, assume that an acquirer is considering two alternative acquisition candidates, Targets A and B. The estimated gains from acquiring A and B are $20.2 million and $15.6 million, respectively. Based on this comparison, Target A would be recommended. However, if the probability of merger success is estimated as 55% for Target A and 82% for Target B, then the adjusted gains emerging from Equation 3 are $11.11 million for A and $12.79 million for B. The results indicate that Target B is superior because it has a higher probability of merger success and a higher adjusted gain. Further, the acquiring firm will be less likely to overpay for Target B because the adjusted gain is $2.81 million less than the unadjusted gain. If the acquiring firm is considering many alternative merger candidates, as is usually the case, estimating merger gains would require considerable time, effort, and money. Instead, the acquiring firm can use estimates of the probabilities of merger success as a preliminary screen that will facilitate a narrowing of merger candidates without estimating merger gains. Decision Time Indeed, the acquirer looking at an extensive list of targets may never have to go to the merger valuation stage for those that don’t pass the probability-of-success screen. The final score can be used to accept or reject candidates, rank order the targets, and negotiate an acquisition price with the candidate that eventually is chosen. Suppose the acquirer was considering five candidates and ranked the probability of success for each as follows: Clearly A and B are the standouts, and the evaluator may decide that only firms with scores of 70% or higher will make the cut and be run through more intensive due diligence and valuation. Their scores are multiplied by the merger gain (Equation 3) to produce what each target is worth to the acquirer so that the acquirer does not pay an excessive premium. In this article, we describe a practical approach to screening acquisition candidates that is based on a quantitative framework for estimating the probability of merger success. This approach explicitly considers the uncertainty inherent in the merger evaluation process, relies on the informed judgment of the acquirer’s management team, and incorporates multiple criteria. The probability of merger success is defined as the multiple of the probabilities of achieving corporate objectives, marketing success, and operational success. The evaluation technique involves multiplying the value created by the merger by the probability of merger success to obtain a risk-adjusted value. The risk-adjusted value can be used for accept-reject decisions, to compare alternative merger candidates, and to negotiate a price with the target firm.