Forecast challenges
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In a volatile market, how would you overcome the forecast challenges during the Financial DD especially when you don’t have enough historical data?
Agreed, you should construct models based on various scenarios (base, high, low) but ultimately you can never eliminate risk from poor forecasting. With limited historical data, commercial due diligence takes on an even greater role as it’s the only form of DD that is future looking.
I am facing that problem right now with COVID. It’s hard to believe any forecast given the changing nature of COVID and its impact on some industries. Volumes looked like they were recovering until Omicrom hit. I think buyers have a lot more execution risk right now and determining value is hard. Might be a short term problem but think some long term trends are starting to emerge. Think we need more scenarios!
I agree that modelling scenarios will help. In addition, if it comes to a point where it is too challenging to factor in various assumptions or if there are too many nuances to consider, perhaps it would also be good to apply an overall discount or to structure the transaction in stages.
In addition to model different scenarios. I would like to hear good explanations from management, preferably by numbers, why the actuals deviates from the forecast.
Putting scenarios is essential to understand how things will move in each possibility. Another key factor is bring in a third party experienced team and have their inputs with that regard. These inputs shall be validated internally by experienced personals as well.
Having different scenarios is important. Knowing the targeted customer list, strategy, tactic, action plan, assumptions and others behind each scenarios would help in increasing the forecast confidence level.
Besides trying to understand the forecast and the data itself, it is as well essential to understand how the forecast hase been done in the past. Forecast doen not only reflect the market trends and opportunity, but often is a political document signed of by top managemnet. Once understood, who really has the experience of the market and understands trends well, you will create a forecast that comes close to reality. Further, keep always ideas for quick expansion in mind to backup a plan, once it becomes to volatile.
In my opinion to reduce uncertainty in M&A, scenario analysis should be critically conducted for different outplays in the DD process.
Essentially Legal and Environmental DDs are two areas that need to be keenly looked at during the DD process. The target company should be analyzed of any impending legal liabilities that might force it to payout future settlements. Legal liabilities such high litigation expenses and large settlement payouts can affect financial position of target company and may even force it into bankruptcy.
It is therefore imperative for different scenario analysis in the DD process to separate current and future liabilities from assets of target company through a divisive merger to mitigate for these uncertainties.Sensitivity analysis is certainly a requirement.
In addition, historic performance during good and volatile years is an important factor. One might have to go back 5-10 years rather than just the customary 3 years to truly assess the performance of the company. Another important factor is the industry in which the company operates. Is it a historically recession proof industry or does it fluctuate.For example, if you are looking to buy an automotive supplier you have to be aware that they are highly cyclical and depend on the macro-economy.
I believe a comprehensive approach needs to be done to estimate the forecast:
1) market situation: supply /demand
2) cost curves: in comparison to competitors
3) local vs. export sales
4) geopolitics: ADD, sanctions
5) customer relations
6) supply situation; utilities; raw materials
7) financial optimisation
8) strategy of the company: to start and to conclude with itPandemic or another unpredictable event is a challenge in a M&A report. However is an untypical or one-time event that needs to be reflected in our historical financial analysis and in our financial forecast assumptions till the objective year in which the company expects to return to normal operations and performance. To identify the “normal performance” it is needed to evaluate the historical performance 5 years ago and obtain a historical average of the company’s main drivers.
Albeit historical financial data are useful to understand the evolution of a target financial performance and the root-causes fo such performance relatively to peers and in consideration of a macro-economical context; we should always bear in mind that past performance does not guarantee future results. Therefore, the forecast exercise needs to be constructed with an optimum exploitation of DD findings, notably the ones arising from Commercial and Financial sections. Such exercice should reflect a mix of assumptions ideally collected via a bottom-up process involving key protagonists who will be in charge of running the business once integrated, after validation of the management who will ultimately be responsible in front of the shareholders. Assumptions structuring the forecasting equation could be modeled under 3 main sequential thematics:
1/Topline: with a focus on market size, sale mix, couple volume-price
2/Operational excellence: with a focus on profitability, efficiency and subsequent implications on cost structure
3/Cash conversion: with a focus on elements affecting cash generation, namely Invested capital evolution that could be further broken down into short-term with operational Working capital variation and more long-term items such as Capital Expenditures requirement.
It is also a best practice to stress case such assumptions and simulate sensitivity to key factors upward/downward changes.Including optimistic, base and conservative scenarios is a good option. As an extra layer of prudence you might also advocate for using slightly different discount rates (or valuation multiples) along with the different scenarios in your valuation framework. For example, add 25 or 50 bps to your discount rate for the conservative scenario. The risk to doing this is that you over-penalize the asset value to the downside in the conservative case (and over-estimate valuation to the upside in the positive case.)
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