Academic journal article Stanford Journal of Law, Business & Finance

A New Theoretically-Grounded Microstructure Trading Model* for Calculating Damages in Shareholder Class Action Litigation?

Academic journal article Stanford Journal of Law, Business & Finance

A New Theoretically-Grounded Microstructure Trading Model* for Calculating Damages in Shareholder Class Action Litigation?

Article excerpt

Current practice in class action litigation entails a series of arbitrary assumptions about fundamental parameters that may not meet Daubert standards of scientific evidence. This paper proposes a new model (denoted the Theoretically-grounded Microstructure Trading Model or TMTM*) to estimate retained shares for use in damages calculations for securities fraud cases that is grounded in the well-established theory of market microstructure, thereby complying with Daubert standards. Rather than merely assuming critical parameter values (as in the extant approaches), the TMTM estimates parameters using objective financial data. In particular, we utilize models that classify trades as "buys" or "sells," as well as estimate trading intensity using the bid-ask spread. The model is estimated and compared to the Proportional Trading Models (PTM) and the Two Trader Model (TTM) and found to yield reasonable estimates of retained shares.

Introduction

Financial markets can function effectively only if they are fed a steady diet consisting of full and accurate information disclosure. Recent scandals regarding failure to disclose material information, or the misleading and deceptive disclosures of information (e.g., Enron, Computer Associates, WorldCom), have led to a proliferation of fraud-on-the-market litigation. Damages assessed in these cases act as the economic penalties that, ex ante, inhibit behavior that undermines the integrity of global financial markets. In order to act as an effective deterrent on bad behavior, damages must be properly assessed. There are two inputs required to accurately determine damages in fraud-on-the-market cases: (1) an estimate of the price inflation that was caused by the fraudulent disclosures;1 and (2) an estimate of the number of damaged shares. The measure of damages is then obtained by multiplying the price inflation per share times the number of damaged shares.

However, this is easier said than done. Whereas an event study based on a theoretical market model can be used to estimate the extent of the share price inflation using daily stock prices and the public record of relevant events, there is no such theoretical basis currently used to calculate the number of damaged shares. Moreover, detailed shareholding and transaction data are not available to implement a purely empirical approach to calculation of the number of damaged shares. The current approach relies on arbitrary assumptions that invariably result in dueling experts because they cannot be validated by either theory or empirical investigation.

The problem is complicated by the fact that damage estimates may precede discovery and therefore must be performed using publicly available data. However, even if damages are calculated with the benefit of the discovery process, it is virtually impossible to obtain on discovery the detailed transaction data required to exactly count the number of damaged shares. This is because damages do not apply to any shares that are bought and subsequently sold outside of the Class Period, or to shares that are both bought and sold within the Class Period (the "in and out shares").2 Indeed, the objective for the damages expert is to determine the number of retained shares that were bought at some date during the Class Period and only sold after the end of the Class Period, thereby focusing on those shares that were bought at inflated prices during the Class Period and only sold after the stock price decline upon revelation of the fraud. A damages expert must therefore use a trading model to eliminate the in and out shares so as to arrive at an estimate of retained shares to be used in the damage calculation.

For a model to be useful it must have three major attributes: (1) it must be tractable-i.e., can be estimated using readily available data; (2) it must not be perceived as arbitrary with respect to critical parameter values and assumptions; and (3) it must be based on generally accepted financial theory and validated using empirical data. …

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