Abstract

Most leading securities regulation scholars argue that compensating securities fraud victims is inefficient. They maintain that because diversified investors that trade frequently are as likely to gain from trading in fraud-tainted stocks as they are to suffer harm from doing so, these investors should have no expected net losses from fraud over the long term. This assertion, which analogizes trading in fraud-tainted stocks to participating in a coin toss game in which players win $1 on heads and lose $1 on tails, is problematic for a number of reasons. First, even if we accept this analogy, probability theory holds that as the number of trials (in this context, purchases and sales of fraud-tainted stock) increases, the lower the probability of being break-even. Second, though true that with increased trials, the likelihood of the proportion of gains and losses being roughly equal will increase, investors generally do not engage in enough trading activity to have a reasonable degree of certainty of reaching the expected proportion of equal gains and losses. Finally, given the variation in fraud-related gains and losses in each stock trade (i.e., the payoffs are not constant), the coin toss analogy is inappropriate. This study, using observational data and computer simulated trading data on 14 investor prototypes, sets out to test the conventional wisdom and reveals not only that undiversified individual investors can suffer significant net losses from securities fraud over a 10-year period, but also that large numbers of diversified institutional investors can, as well. These results refute the claims of fraud compensation opponents who assert that a diversified investor that engages in active trading should suffer little or no fraud-related net harm over the long term and that individual investors can protect themselves fully from fraud-related net harm by investing through mutual funds or other intermediaries.

Disciplines

Business Organizations Law | Securities Law

Date of this Version

October 2010

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