PlumX Metrics
SSRN
Embed PlumX Metrics

Vicarious Liability for Fraud on Securities Markets: Theory and Evidence

University of Illinois Law Review, 1992
2012
  • 6
    Citations
  • 2,498
    Usage
  • 4
    Captures
  • 0
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    6
    • Citation Indexes
      6
  • Usage
    2,498
    • Abstract Views
      2,042
    • Downloads
      456
  • Captures
    4
    • Readers
      4
      • SSRN
        4
  • Ratings
    • Download Rank
      127,298

Paper Description

This article examines the efficiency of vicarious liability for Fraud on the Market. Standard analysis of vicarious liability argues that this rule is efficient. Professors Arlen and Carney argue, however, that in Fraud on the Market cases vicarious liability does not serve the goals of optimal deterrence or optimal risk spreading, nor does it promote optimal loss spreading. Breaking with traditional vicarious liability literature, the authors examine monitoring costs in detail, and posit a model that predicts that Fraud on the Market generally occurs when agents fear themselves to be in their last period of employment. An empirical study of Fraud on the Market cases demonstrates that these frauds generally are the product of last period agency costs. The article also shows who pays for securities fraud under a rule of vicarious liability. The empirical study shows that an issuer's potential liability for the fraud of its agents is enormous, representing a substantial share of the equity of the firm. Imposing liability on the firm results in a large wealth transfer from one group of innocent investors to another similar group. Because this transfer neither deters fraud nor spreads losses, it performs no useful social function. The authors conclude that a rule of agent liability, supplemented with criminal enforcement, is preferable.

Bibliographic Details

Provide Feedback

Have ideas for a new metric? Would you like to see something else here?Let us know