articleClient Story

Fortune 500 Life Sciences Leader with an Option Exchange Awards Program

A Fortune 500 life sciences company set up a value-for-value underwater option exchange (trading options for restricted stock). It was one of the largest option exchange programs in five years.

As part of the program, the company chose to extend the vesting on the new restricted stock. This required an accounting policy decision about whether to use the Bifurcated Method or Pooled Method. Both methods are hard to implement. But the company went with the Bifurcated Method.

The Bifurcated Method is complicated by the fact that stock administration data is updated to reflect only the modified award terms so as not to confuse plan participants. The problem with this is that the accounting rules require consideration and reference of both the original and modified award terms.

What’s more, because stock plan administration systems are generally designed from a participant perspective, financial reporting processes tied to those systems lack the ability to seamlessly but selectively reference the pre-exchange data. Even maintaining the original grants in a parent-child fashion will result in errors.

Our Role

Equity Methods’ solution was to build an adjusted dataset (called the “EM Adjusted Dataset”). The EM Adjusted Dataset mimicked selective characteristics of both the pre-exchange and post-exchange grants in order to achieve the correct financial reporting values. For example, given the floor provision as it related to vesting extensions, the EM Adjusted Dataset reflected the original vesting schedule. But it also reflected the post-exchange strike price.

Importantly, the EM Adjusted Dataset didn’t require any changes to the core administration data. It was based on that data, but never appended back into the administration system. This meant the administration data—along with the associated data entry and maintenance processes—were never affected.

Results

The combined dataset flowed seamlessly through our algorithms to generate correct financial statement values for the life sciences company. On top of that, because we formulated the EM Adjusted Dataset in such a controlled way, tie-out to the core administration data and audit of the downstream financial reports were both straightforward for the firm.