In the field of finance and corporate strategies, merger and acquisition (M&A) has been considered as a popular corporate strategy to enhance a market share of a product or service of a corporate entity, increase research and development (R&D) benefits, improve profitability and revenue, optimize cost, obtain synergies in technology, and improve processes and expand geographical reach.
Existing M&A models measure success of deals by parameters such as share price, market capitalization, increase/decrease in sales and revenue, post-integration issues and impact on public and technology synchronization. Survey and interviews have been employed to compare aforementioned parameters pre and post M&A. However, several changes usually occur during, post M&A. For instance, a change in a leadership team of a target legal entity may result in unexpected results. Further, data associated with majority of aforementioned parameters may be unavailable prior to the M&A, thereby leaving little scope for an accurate quantitative analysis of the probability of success or a success rate of the M&A. Furthermore, there may be less linkage between theoretical prescriptions and practitioners’ views.
Hence, there is a need for an alternative system and method that measures and identifies gaps in the M&A process and predicts the probability of success of the M&A with reasonable accuracy. The alternative scoring model method should provide an opportunity to effect counter-measures to increase the success rate based on identified gaps. Thus, to provide an alternate solution for evaluating a financial strategy such as the M&A primary survey by the M&A professionals is undertaken. Analysis of the respondent’s filled survey was analyzed to throw some significant insights into the probability of success of past M&A deals.
A set of five synergy factors along with sub-factors associated to the corporate M&A strategy were identified. For example, the set of synergy factors include finance, technology, management, geography, and macro-economic variables. Further, one can identify a set of sub- factors for each synergy factor.
For example, the sub-factors identified for the finance synergy factor may be Multiple - Enterprise Value divided by Earnings Before Interest, Tax, Depreciation and Amortization (EV/EBITDA), current profit or loss, deal value, share price, Sales, Revenue, Customers/clients, Suppliers, and a Shareholding Pattern. In another synergy factor, the sub-factors identified for the technology synergy factor may include current technology, new technology, adaptability, patent, Research and Development (R&D), expenses required, replacement cost, suitability, environment factors, and a lifespan of technology. The sub-factors identified for the management parameters synergy factor may include current leadership, new leadership, strategy sync, compensation sync, process sync, policy integration, and culture sync. The sub-factors identified for the geography synergy factor may include expansion in existing geography, prospective geography, market share seeking, regulatory incentives seeking, cost synergy, and manpower synergy. Likewise, sub-factors identified for the macro-economic variables synergy factor may include inflation, interest rate, exchange rate, industry prospects, government policies, employment, and standard of living.
Each synergy factor and sub-factors are assigned maximum weights. Based on an algorithm, the synergy-wise scores are computed with total score. Actual score is compared with maximum score for each synergy factor and this provides insights into the probability of success of the transaction. On the basis of the score using the simple model, one can identify the positive and negative synergies and try to choose from various options like whether to go ahead with the deal, drop the deal or recalibrate the synergy factors to improve the probability of success. This model has also been back-tested to provide insights.
