Avoiding costly mistakes: reviewing financial models in M&A transactions

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A decision whether to buy or sell a company involves a number of process steps and considerations on the part of the seller and the buyer. Aside from emotional reasons, most actors will make their decisions based on financial aspects.

 

      

For this purpose, both parties strive to map the respective target using an integrated and forward-looking financial model in order to present the historical and projected future data on the financial position, net assets and results of operations as transparently as possible and to map their own ideas and expectations, such as synergies and implications of financing alternatives. In general, this process is referred to as "financial modelling", which serves as the basis for ongoing negotiations, discussions and decisions in committees (supervisory board, investment committee), fundamental strategic considerations, including the planning of subsequent integration ("post-merger integration"), as well as for lending decisions by financing banks. The vast number of users shows that a financial model must be error-free in order to avert financial losses and loss of reputation for the parties involved.
 


Advantages of a financial model as quantitative decision-making basis

The advantage of a Microsoft Excel-based financial model is that it allows mapping all parameters relevant to the transaction using one integrated model, which, additionally, can adequately map interdependencies that occur between them. With an appropriate system, the models therefore offer a high degree of flexibility and allow analysing implications of changes in parameters transparently. This results in a large number of possible scenarios for the final decision template, which can be included in the decision-making process.
 
In practice, when creating financial models, errors often occur, which leads to incorrect results and can have serious financial consequences for the respective party. For example, this can result in a purchase price that is too high or too low or in providing a potential buyer with incorrect information that leads to a legal dispute after the transaction. There are numerous reasons for this:

  • lack of experience in creating complex models;
  • lack of coordination between people working on a model at the same time;
  • changes are entered inaccurately under time pressure or assumptions and formulas simply contain incorrect entries;
 
But how can buyers and sellers ensure that errors are avoided already when developing the decision template and that their assessment is thus not based on an erroneous foundation?

 

A systematic check for detecting errors step by step

The remedy is to perform a structured review of the financial model for possible errors, weaknesses or inconsistencies. In practice, this is referred to as "model review", which is ideally carried out by an unbiased third party. The review can be divided into three sub-areas:

 

TECHNICAL REVIEW

The technical review of a financial model is the basis of any review process. The first step is to ensure that the links and shortcuts used within a financial model are set correctly. This mainly concerns references to input data used in the calculations to ensure that, for example, the data of a respective year are also used.
 
Another area to focus on is the analysis and evaluation of the formulas applied within the financial model for possible errors, e.g. wrong signs, false arguments within a function, or for the possibility of reducing complexity of the formula syntax.
 
Since complex financial models usually require more than standard Microsoft Excel functions, professional model review software is also used in practice for the technical review. This allows analysing and colour-mapping complex model structures, making it easier to identify broken formulas or manual entries in the calculation structure.
 

STRUCTURAL REVIEW

The structural review examines whether the financial model is structured and built according to the IPO principle (Input → Processing → Output). In addition to selecting the suitable model structure at the stage of preparing the model concept, this also requires correctly designating all spreadsheets as well as the tables they contain.
 
Next, the data flow is reviewed. In this process, it is checked whether the data flow was  stringently adhered to on all spreadsheets within the model. A unified data flow keeps the model transparent to the user and easily adaptable to future changes without sacrificing reliability and flexibility.
 
If developed, also the model's documentation and its consistency with the actual model is reviewed. Such documentation is typically developed when a model is to be used many times in the future and by different users. It usually describes the purpose of the model, input parameters and the basic calculation logic.
 

LOGICAL REVIEW

This focuses on reviewing the results returned by the model and its calculations. On the one hand, this process comprises the semantic interpretation of the results and their classification in the overall context of the original purpose of the model. On the other hand, it is checked whether changes in the assumptions have the expected effect on the results or whether any implausible results indicate that the model or the calculation logic are incorrect.
 
In addition, the process allows checking the plausibility of the assumptions made within the model. This includes, for example, the technical review of complex accounting issues, benchmarking of growth rates against market estimates or of interest rates against latest capital market data.

 

A structured model review minimises the risk of wrong decisions

Depending on the complexity of the model to be examined, an experienced model reviewer needs between a few hours to a few days to fully review all its aspects. In the overall context of an M&A transaction, this is not a heavy workload. The model review offers all parties involved a high added value as well as increased security and often saves them from making costly wrong decisions, as, thanks to the structured procedure, it allows reducing possible errors in the assessment or valuation of the target to an absolute minimum.

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Andreas van Bebber

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Christoph Hirt

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