Using Auctions to Price Employee Stock Options: The Case of Zions Bancorporation ESOARS

The first empirical analysis of Zions Bancorporation’s 2006 and 2007 auctions of Employee Stock Option Appreciation Rights Securities (ESOARS), this study examined (1) the impact of auction rules on bidding strategies and allocations, (2) the efficiency of the auction clearing prices, and (3) the “value gap” between ESOARS auction clearing prices and various model-based estimates. It found that the design features of the auctions (e.g., ending rules) significantly affected bidding strategies but not final allocations, which remained highly concentrated. It also found that the clearing prices (1) displayed high price elasticities of demand, suggesting efficient price discovery, and (2) were low relative to some, but not all, model-based estimates. These findings suggest design improvements that might benefit auctions of other illiquid derivatives (e.g., banks’ “troubled assets”) currently being considered by the U.S. government. Under Financial Accounting Standard (FAS) No. 123R, companies must report their employee stock option (ESO) grants’ costs. Issuers usually estimate their nontradable ESOs’ fair values by using a permissible model. Different modeling assumptions and parameters, however, yield different model-based valuations. Zions Bancorporation has advanced a different, market-based approach, which the U.S. SEC has approved. Under Zions’ approach, a reference ESO’s fair value is determined by the clearing price of its tracking security (Employee Stock Option Appreciation Rights Securities [ESOARS]) in an auction to outside investors. In this article, which is the first empirical analysis of Zions’ 2006 and 2007 ESOARS auctions, we examine (1) the impact of auction rules on bidding strategies and allocations, (2) the efficiency of auction clearing prices, and (3) the “value gap” between ESOARS auction clearing prices and various model-based estimates.The valuation difference between the auction clearing price and commonly used model-based estimates in the first auction decreased significantly in the second auction. This outcome may be attributable, in part, to the changes made in the second auction’s design (i.e., starting the auction on the reference ESOs’ grant date; adjusting the ESOARS payment for prevesting forfeiture, as FAS No. 123R mandates; and extending the auction’s end time) and to the fact that investors had become better informed about ESOARS auctions, given the SEC’s well-publicized comments on the first auction.We believe that Zions’ market-based approach for deriving the fair value of reference ESOs is a novel and potentially helpful complement to current model-based approaches for valuing ESOs for reporting purposes under FAS No. 123R. Although the ESOARS auction’s design leaves room for improvement, Zions’ market-based approach provides a useful valuation benchmark, especially for valuing ESOs granted by relatively young companies for which historical exercise data may be scarce.Absent historical data, bidders could use proxy data from other companies to calibrate their bids in an ESOARS auction hosted by a relatively young company. Although each bid might be noisy, given such imperfect data, the auction’s clearing price would reflect an unbiased consensus value estimate, assuming that the offering was large and bidder entry was unrestricted. In contrast, even if the company itself used the same proxy data to estimate its ESO’s value under a permissible model, it would remain subject to the criticism that such a model-based value reflects only the company’s own (potentially downward-biased) value estimate rather than a consensus view.We found that the design features of the auctions, such as ending rules, significantly affected bidding strategy but not final allocations, which remained highly concentrated. We also found that the clearing prices (1) displayed high price elasticities of demand, suggesting efficient price discovery, and (2) were low relative to some, but not all, model-based value estimates. Such a value gap could be the result of modeling assumptions, as well as auction design flaws that other researchers have noted, including misaligned seller incentives, restricted size of auction, limited bidder participation, and secondary market illiquidity. Our findings suggest design improvements that might benefit auctions of other illiquid derivatives (e.g., banks’ “troubled assets”) currently being considered by the U.S. government.