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ASC 718-10-55-11 permits companies to select the option-pricing or equity valuation model that best fits their unique circumstances as long as the valuation technique:
  • is applied in a manner consistent with the fair value measurement objectives and other requirements of ASC 718,
  • is based upon established principles of financial theory, and
  • reflects all of the substantive terms and conditions of the award.

As a result, for most employee stock options and other employee equity instruments, companies will have flexibility in selecting the option-pricing or equity valuation model used to estimate the fair value of their stock-based compensation awards.
The Black-Scholes model is relatively simple to use and well understood in the financial community. Its relative simplicity stems, in part, from the fact that when estimating the fair value of an employee option, all expected employee exercise behavior and post-vesting cancellation activity is reduced to a single average expected term assumption.
The principal advantage of lattice models, on the other hand, is that they can accommodate a wider range of assumptions about employees’ future exercise patterns, as well as accommodate other assumptions that may change over time. This approach may yield a more refined estimate of fair value.
A Monte Carlo model simulates a very large number (as many as 1,000,000) of potential stock price scenarios over time and incorporates varied assumptions about volatility and exercise behavior for those various scenarios. A fair value is determined for each potential outcome. The grant date fair value of the award is the average of the fair values calculated for each potential outcome.
For awards with typical service conditions and performance conditions, the Black-Scholes model will generally produce a reasonable estimate of fair value. Monte Carlo simulation and lattice models result in a more refined estimate of fair value. Additionally, companies that issue awards with market conditions or payoff conditions that limit exercisability should use either a Monte Carlo simulation model or a lattice model because those models can better incorporate assumptions about exercisability in relation to the price movements of the underlying stock and/or potential payoff outcomes related to achievement of market conditions.
Companies will need to weigh the advantages and disadvantages of each model in order to choose a model that fits their particular circumstances. In deciding which model is most appropriate, some factors to consider are:
  • Compensation plan design: The specific terms of awards granted by a company may have an impact on which option-pricing or equity valuation model it selects. For example, it is generally appropriate (and common practice) for most "plain vanilla" stock options to be valued using the Black-Scholes model. However, lattice models are sometimes used for other awards, including options that are in-the-money, awards with market conditions, and awards with payoff functions limited in certain ways (such as maximum value options, as discussed in SC 10.3). Furthermore, it is common practice for a Monte Carlo simulation model to be used when valuing awards containing a market condition.
  • Data availability: The principal advantage offered by Monte Carlo simulation and lattice models is that they can accommodate a wider range of assumptions; however, this poses certain challenges. Companies may need to analyze a significant amount of detailed historical employee exercise behavior in order to develop appropriate assumptions required by a lattice model or a Monte Carlo simulation model. Many companies may not have the necessary historical data, or may conclude that their history is not relevant in making assumptions about future exercise patterns. Thus, the Black-Scholes model may be more practical, assuming it is appropriate for the type of award. Additionally, SAB Topic 14 provides a simplified approach, subject to certain conditions, for developing an expected term assumption for "plain vanilla" options, making the continued use of the Black-Scholes model significantly less difficult and time consuming. ASC 718-10-30-20A through ASC 718-10-30-20B provides a similar option for nonpublic companies.
  • Cost-benefit analysis: Although the Monte Carlo simulation and lattice models may provide a more refined estimate of fair value for some award types, companies should weigh the costs involved before switching from the Black-Scholes model. Some companies may determine that the costs of applying a Monte Carlo simulation or lattice model outweigh the benefits of a more refined fair value estimate.

Companies may decide to change from one option-pricing or equity valuation model to a different one (e.g., from Black-Scholes to a lattice model). A change in option-pricing model is not a change in accounting principle—the underlying objective of estimating the fair value of the award is the same—and therefore does not require justification of preferability (or a preferability letter in the case of an SEC registrant). However, changes in valuation models should generally only be made when the new model will result in an improved estimate of fair value. Additionally, companies may use one model for certain awards and another model for different types of awards. For example, the fair value of a "plain vanilla" option could be estimated using the Black-Scholes model while a Monte Carlo simulation is used for an option with a market condition.
SAB Topic 14 requires companies to disclose any changes to the option-pricing model they use and the reasons for the change. Because Monte Carlo simulations and lattice models are generally considered to provide more refined estimates of fair value than the Black-Scholes model, we believe that once a company adopts a Monte Carlo simulation or lattice model to value a particular type of award, although this is not a change in policy that would require preferability, it would likely be difficult to support switching back to the Black-Scholes model to value that same type of award.
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