One of the principal problems with negotiations in chapter 11 and in workouts arises from the difficulty in valuing the assets and enterprise value of the debtor. Ten years ago the creators of a new software tool, the Restructuring Toolkit�,¹ argued that ambiguous values created rational bargain zones in chapter 11 and that value is better analyzed as a range in determining acceptable restructuring deals.² Since then, the authors have applied this theory to an intuitive software program designed to assist lawyers, creditors, distressed debt investors and turnaround management practitioners.

By evaluating the impact of different valuation ranges on ultimate recovery to each class of creditors and identifying potentially rational bargains based on ranges of possible outcomes, the Restructuring Toolkit helps insolvency practitioners assess risk levels in restructuring and chapter 11 negotiations. It also helps practitioners understand the impact that the debtor's valuation and its uncertainties have on technical legal rights.

Although the Restructuring Toolkit is quite user-friendly for the insolvency practitioner, the math behind the black box is anything but simple. The user simply fills in blanks for the going-concern value of the company, the probable range of variation in that going concern value (i.e., +15 percent) and the amount of claims in each of the creditor classes. From these inputs, the Restructuring Toolkit calculates the expected return to each class.

Without describing in detail the exact calculations, the Restructuring Toolkit model assumes that the uncertainty with respect to the value of the company is best captured by a lognormal distribution curve. Although this type of probability distribution differs somewhat from the standard bell curve with which many of us are familiar, it ties closely to the theory underlying the Black-Scholes Option Pricing Model, which also can be derived utilizing lognormal probability distributions for the value of the underlying assets. The Black-Scholes model is used by many financial and capital markets institutions in assessing trillions of dollars of risk investments.

Also noteworthy is a feature significantly more difficult for the insolvency practitioner to visualize. If the user wants to simultaneously consider the impact of a potential liquidation alternative value, the probability of the liquidation input set is for a number higher than zero and provides a projected liquidation value together with a range for that projected value. The Restructuring Toolkit will instantaneously calculate the combined risk and expected recoveries for both independent scenarios and consider it as a single scenario. The Restructuring Toolkit also provides a variety of graphic visuals, including point-and-click sensitivity analysis charts, to allow the user to assess the impact on each class's recovery created by changing the user's assumptions. One sample screen is shown below.

For anything from a small business to a mid-cap enterprise, the Toolkit is able to capture the critical valuation issues between the classes. The Toolkit was designed with four classes of creditors (secured, senior unsecured, general unsecured and subordinated) and one class of equity or residual holders. It assumes that (a) the secured creditor class has a lien on substantially all of the assets of the debtor and (b) the senior unsecured class must be paid in full before the subordinated class receives anything. In the authors' experience, these assumptions are reasonably descriptive for smaller businesses up to mid-cap bankruptcies.

However, just because the Restructuring Toolkit uses those labels for the various classes and makes these assumptions does not mean that the user cannot apply Restructuring Toolkit in a bankruptcy where one or more of these assumptions would be technically incorrect. One simple example would limit the amount of an undersecured claim used in the model with a lien on less than all assets to the value of the pledged collateral (as determined by the court in adequate protection hearings or otherwise during the proceeding), and reclassify the secured lender's deficiency claim as a senior or general unsecured claim. In another situation with competing valuations from multiple investment bankers or financial advisors, the user could apply the "liquidation value" entries for the lower valuation case. By adjusting the percentage likelihood of liquidation in the model, the user can more easily determine the terrain of the common ground between the contrasting expert opinions.

For example, the Toolkit could analyze AMR's (American Airlines) equipment trust certificates. After the value range is selected, consider the A tranche as secured debt, the B tranche as senior unsecured debt and the C tranche as subordinated debt (with no general unsecured debt). Capturing a significant amount of the potential interactions and expected relationship as to recoveries between these various classes of ostensibly secured creditors, it further demonstrates how pressure could be brought to bear on the C tranche in any negotiation if AMR files for chapter 11. Of course, this approach is limited by the fact that, to the extent to which the equipment trust certificates are not fully secured, they may have a general unsecured claim for the deficiency in AMR's bankruptcy proceeding. As such, the value and expected recovery of such a certificate should not fall below the value of an unsecured claim against AMR.

Version 2.0, now available, adds a variable administrative priority class and a variable tort claimants class to model mass-tort cases in which the insured administrative cost of defense is difficult to assess and the size of the uninsured tort claim class is hotly disputed. It also adds a fully secured miscellaneous secured class. The authors hope that by providing these exciting new tools to insolvency practitioners, it will improve the efficiency of the bargaining process.

Footnotes

¹ Available without charge at www.macdonaldlaw.com. Return to article

² MacDonald, MacDonald & McLeod, "Chapter 11 As A Dynamic Evolutionary Learning Process in a Market with Fuzzy Values," 1993 Am Survey Bankr. Law pp. 1, 40-50. Return to article