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The Bermuda Triangle of Valuation: Bias, Uncertainty and Complexity

August 13, 2015
Read Time: 0 min

Aswath Damodaran

By Aswath Damodaran
Professor of Finance, NYU Stern School of Business

Introduction from Sageworks:

Studies have shown that sizable numbers of business owners look to transfer their ownership interests in the next decade, and a major concern for many is whether they’ll get out of the business what they expect. Gaps in the valuations of businesses are a leading cause in busted deals, which is unsurprising, given that sellers, buyers, bankers and other advisors have their own ways of valuing businesses. As Aswath Damodaran, professor of finance at the Stern School of Business at New York University, explains in this guest column, three problems, in particular, can sink the valuation process.

Webinar: Business owners and valuation professionals alike should honestly and openly discuss them if they seek good valuations, argues Damodaran, who led a webinar on valuations on Aug. 20.

The Bermuda Triangle of Valuation

It is true that valuation, at its core, is simple and should be easy to implement, but it is often mangled in practice. While some valuation malpractice reflects a misunderstanding of the basic principles of valuation, I think that the bigger culprit is the valuation process and three problems that are embedded in it: the biases that analysts bring into their valuations, their unwillingness to grapple and deal with the uncertainties that they face when valuing businesses and the ease of access to data and tools that allow them to build in complexities into valuation models.


In an ideal valuation, you would start with no preconceptions about the companies that you are analyzing and then make your best judgments on how much value to attach to them. In practice, you almost never start with a clean slate, as everything that you have read or heard about a company will have already created perceptions about it that will affect your valuation. Ironically, the more you learn about a company and the more exposure you have to its management, the more entrenched the bias becomes. This bias is made worse, if you are being paid to do the valuation as an appraiser, banker or expert witness, since the client paying for the service often has a “value” in mind that he or she would like to see, and you will feel pressure, either implicitly or explicitly, to deliver that value. At the risk of stating the obvious, all valuations are biased, with the only questions being to what degree and in which direction.

There are many tools to reflect bias in value, and one test of your valuation expertise is in how well you can hide bias in your inputs. Thus, if your intent is to lower (higher) value, you can find ways to under (over) estimate cash flows, skew growth rates (higher), push up discount rates or attach large discounts (premiums) to your end value to arrive at your final number. While bias is perhaps impossible to escape in valuation, analysts often deny its existence, express outrage when confronted about it or try to counter it by getting stamps of approval from accountants or lawyers. It would be much healthier if analysts were more transparent about their motives, open about their biases and worked at minimizing their impact on value, rather than acting like they do not exist.


Uncertainty is a feature in valuation, not a bug, and it rises to the surface, especially when valuing young start-ups and companies in transition. It comes from many sources, from both within and without the firm, and analysts are often paralyzed in its presence, so they seek out mental short cuts (use simple pricing multiples) or try to outsource the problem (call in consultants and experts).

Rather than run away from uncertainty, analysts will be better served if they faced up to it in healthier ways. One technique that has stood me in good stead is to replace the conventional base case valuation, where you use single estimates for each input to derive a base case value, with a simulation, where you estimate probability distributions for the inputs to generate a distribution of values. When all is said and done, though, a little humility will benefit us all. After all, no matter how good your valuation skills are and how well you have brought them to bear when valuing a young start-up, you will be wrong and often dramatically so, through no fault of your own.


There are multiple forces that are converging to make valuations more complex. First, as companies globalize, analysts increasingly have to confront country risk and currency estimation choices when valuing even U.S.-incorporated companies. Second, as accounting standards become more complex and corporate holding structures get more complicated, analysts are facing the spillover effects. Third, easy access to data and to modeling tools allows analysts to build complex models quickly and at low cost.

These complex models, with multiple moving pieces and mysterious acronyms, become black boxes, where analysts suspend common sense and stop taking responsibility for their own valuations. It is time to go back to first principles, accept the proposition that less is more and build more parsimonious models that we understand and can use more effectively.

The Bottom Line

At valuation conferences and in valuation treatises, we spend our time immersed in valuation details including how best to estimate cash flows, discount rates and deal with valuation loose ends, but we seldom talk about the bias, uncertainty and complexity. Valuation practice will be better served if we had an honest and open discussion of how we deal with these structural problems, because they remain the biggest barriers to good valuations.

About the Author: Damodaran teaches corporate finance and valuation to MBAs, executives and practitioners and has written four books on valuation, three on corporate finance and three on portfolio management. His blog, Musings on Markets, was selected by the Times of London in 2012 as one of the top ten stock market blogs in the world. Follow him on Twitter (@AswathDamodaran) and on his website.

About the Author


Raleigh, N.C.-based Sageworks, a leading provider of lending, credit risk, and portfolio risk software that enables banks and credit unions to efficiently grow and improve the borrower experience, was founded in 1998. Using its platform, Sageworks analyzed over 11.5 million loans, aggregated the corresponding loan data, and created the largest

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