This article on the valuation process is taken from our quarterly newsletter, Canadian Overview, published by the Canadian member firms of Moore North America. The articles in our newsletter are part of our mission to become the partner of choice for your success by keeping you up to date.
A valuation is often seen as a mathematical exercise in which readily available data and assumptions are stripped away. In reality, it usually involves numerous analyses and hypotheses, as well as choices that are often subjective. They are therefore likely to bias the results of an evaluation. Moreover, the underlying objective of the assessment is often what influences its outcome. If the lens is not properly controlled, the usefulness of the result may be limited.
Common sources of bias in an assessment process
Forecasts. A forecast or projection of a company’s future cash flows is a key element of a valuation model based on future cash flows. When drawing up a forecast, there are many potential sources of bias. For example, we sometimes rely too much on personal experience or intuition, at the expense of independent information and data, to estimate revenue growth rates and profitability measures. Even if objective information is used in the analysis, confirmation bias may give greater weight to information that confirms existing optimistic beliefs, which may be optimistic or pessimistic. Moreover, when estimating a company’s profitability, historical returns are often given pride of place. However, these historical results are often subject to adjustments designed to normalize the results, which can then be selectively included or excluded.
The data. In addition to forecasting assumptions, a valuation model takes other factors into account. These include working capital and capital expenditure requirements, determination of redundant assets, discount rates and terminal value adjustments. For example, discount rates should reflect the risk associated with the realization of expected future cash flows. However, there are a number of alternative ways of developing discount rates that are subject to bias. Any of these inputs, if misapplied or selected without any objective basis, can lead to significant variations in evaluation results.
Evaluation multiples. Market participants may rely on a relative valuation or a market approach as their primary or secondary valuation method. It can be difficult to obtain relevant data from genuinely comparable publicly-traded companies. Thus, it can be very tempting to base comparisons on companies that are in fact not comparable due to their size, product range, end markets, etc. When selecting valuation multiples from open market transactions, the transactions chosen may be too old or irrelevant, for the same reasons as for transactions involving listed companies. In addition, certain valuation multiples of comparable companies may be included or omitted to achieve the valuation objectives, while those that conflict with the objectives are minimized.
Application of discounts or premiums. The use of discounts such as illiquidity premiums, minority discounts or control premiums are more typical in valuations of private companies carried out in the case of shareholder disputes or other types of litigation, particularly with regard to taxes. As there is little objective information on discounts and premiums, the final result of a valuation can be reduced or increased subjectively, using equally subjective adjustments for different situations.
Responses to bias
Corroborate data used to make forecasts. Elements such as industry growth rates and market shares must be taken into account when estimating revenue growth rates. Conflicting independent information should not be ignored. On the contrary, they should be used to test forecasts. For example, a company can be expected to grow faster than the industry average in the short to medium term. But in the long term, corporate growth tends to return to near-average levels. Wherever possible, historical profit margins as a percentage of revenues should be corroborated with independent industry evidence to establish their reasonableness. This is particularly true when it comes to overcoming the bias of normalized financial results, or when the history of operations is limited.
Corroborate the data used to carry out the assessment. As far as possible, the other data used to perform the valuation and which have a significant impact on it should be based on objective and verifiable information. This is the case, for example, with historical information relating to data such as working capital and capital expenditure requirements, as well as market-based information relating to the calculation and selection of discount rates. Company-specific historical data generally provides solid evidence to support data such as working capital and capital expenditure. That said, industry data should also be used when historical evidence or data available within a start-up company is limited.
Cross-check results. If possible, a secondary valuation method should be used to ensure that the conclusions of the primary valuation (usually a cash flow-based method) are reasonable and consistent. In this way, a secondary evaluation method supports the data and assumptions used in the primary evaluation method. This is usually done by comparing a company’s valuation multiples with those of other comparable companies, or with previous transactions in the company’s shares. This analysis, if carried out correctly, can help test the primary evaluation method.
Valuation range. Any value obtained for a company is first and foremost an estimate. As such, it must be quantified as a range of estimates to take account of the associated margin of error. This can be done by applying several scenarios, or by presenting a best-case (high) and worst-case (low) value estimate. In the final analysis, the result presented must reflect the estimates of value and the uncertainty inherent in them.
Conclusion
Biases in the evaluation process cannot be completely eliminated. Indeed, the forward-looking nature of the estimates and the many assumptions used mean that there will always be some uncertainty surrounding the estimates themselves. However, the development of better valuation models that make effective use of available objective and independent information represents an effective means of addressing the bias and uncertainty arising from macroeconomic, industrial and company-specific conditions.
Written by Michael Frost, CPA, CA, EEE of Mowbrey Gil. This document was written as part of our quarterly overview of Canadian news, a newsletter published by the Canadian member firms of Moore North America.
