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Reprinted with permission of RIA

Model Risk and Valuation
By Susan M. Mangiero

Reprinted with permission of RIA.
This article first appeared in the March/April 2003 issue of Valuation Strategies (RIA)


Because the stakes are large when valuation model issues end up in court, valuation professionals should pay close attention to model risk.

Models are used in business all the time and for a variety of reasons. Without models, it would be hard to forecast earnings, simulate cash flows, measure risk, assess competitors, analyze economic conditions, evaluate management effectiveness, determine value of an ownership stake or an entire company, price an individual security, determine optimal capital structure, and so on. Models clearly play a big role in everyday commerce,
but perhaps never more so than now. There are many reasons for this, not the least of which is a clamor for
added financial transparency. People are tired of seeing the markets gyrate in response to one headline after another about fraud, corporate excess, and hidden risks. Shareholders, lenders, regulators, and policy-makers want change now and are no longer willing to accept, without scrutiny, sweet-sounding reassurances from senior management.

As shown in Exhibit 1, the mandate for better numbers comes from several places. Major exchanges support improved corporate governance and they recently asked that listed companies get shareholder approval before management can implement or change stock option plans. Laudable and long overdue, this plan calls for informed shareholders, owners who understand what an option represents, alternative valuation models, and the dynamic relationship between plan characteristics and the bottom line. The exchanges are not alone. Business valuators must similarly understand how an option plan affects a company’s worth and be able to clearly and concisely explain this to interested parties. On a broader front, accountants are revisiting existing standards, many of which involve valuation models. Auditors and financial statement users alike must comprehend how model choice affects the quality of published information.

The Congressional response includes the Sarbanes-Oxley Act of 2002, ordering executives to certify that reported numbers “fairly present in all material respects the financial condition and results of operations of the issuer…”1 The prospect of stiff penalties for falsification should encourage a greater focus on the models used to derive published financial data. Regulators like the SEC are also advocates of enhanced disclosure. Witness its recent proposal to have companies provide additional information about offbalance sheet items and various contingencies, including derivatives, “to the extent that the fair value thereof is not fully reflected as a liability or asset in the financial statements.”2 Combined with FAS 133, Accounting for Derivative Instruments and Hedging Activities, modeling experts are in demand because valuation models are a critical part of determining what, how much, and when valuation changes hit earnings.

Banks have known for some time that modeling is a big deal, as they gear up for changes in capital adequacy standards that are directly tied to valuation. Model choice, good or bad, will determine the size of pledged reserves for loans and traded assets. Corporations and individuals who borrow from banks could feel the pinch in the form of higher fees if banks get it wrong.3 Expert witnesses encounter model risk in the courtroom as unhappy shareholders and lenders, beset with losses, cry foul, alleging improper valuation in the form of inflated purchase prices. For securities
that seldom trade or are part of an investment pool about which is little known, calculation methodology takes on an altogether different meaning with respect to assessment of damages.4 Bad or inappropriately used models affect
legal outcomes in yet another way if they fail to meet the standards set out in Daubert v.Merrell Dow Pharmaceuticals, Inc.5 and testimony is excluded.6

Without a doubt, model-related issues are relevant as never before. Anyone using a financial model must be prepared to defend it, warts and all. No one can afford to look at output alone. Valuation professionals will be under even more pressure to explain what goes into the black box, how it gets assembled, and whether the output makes sense.

Anatomy of a Financial Model

As shown in Exhibit 2, all good models share certain characteristics, starting with a set of generalized assumptions that reflect economic reality most of the time. Moreover, a model must be able to be tested to discern whether the output makes sense, falling within the range of expected values. A stock valuation model that spits out negative prices makes no sense. A model that generates a company value that falls significantly below the sum of prices for fungible assets merits inspection.

Assuming tests on the model reflect accuracy, repeated runs with different sets of data should generate consistent results. Extreme data points should not lead to wildly different numbers. Generally speaking, the model should be relatively insensitive to the variation of inputs. Otherwise, data quality dominates the integrity of the model selection process. Business valuators when choosing from a variety of vendors will want to know something about each provider’s data-generation methodology. Ignorance is not bliss. Inappropriately used data or use of inappropriate data costs time, money, and reputations later on. The model must be cost-effective to use or it will remain on the shelf, collecting dust. Finally, the model must be easy enough to explain to others. Brilliant models that cost a fortune in processing time or cannot be explained to a client, judge, regulator, or programmer are bad news.

Model Risk

There is no perfect model; all have problems, some worse than others. Stated another way, model risk is a fact of life. Though experts disagree on a precise formal definition, model risk occurs in situations such as:
• Inappropriate use of an otherwise
valid model.
• Bad data.
• Hard-to-obtain data.
• Incorrect form of data.
• Computational trouble.
• Incomplete or over-specified model.
It is necessary to recognize model risk before there can be any chance of improvement. Some common examples are shown in Exhibit 3.

Importantly, model risk may exist when applied in one way but not another.7 For instance, a single-variable regression is seldom the best way to





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