Information Asymmetry and the Protection of Investors

55 Pages Posted: 14 Aug 2018

Date Written: July 29, 2018


For decades, the consensus in the legal academy has been that the core modern securities laws do little, if anything, for ordinary investors. As Professors Easterbrook and Fischel memorably put it, the investor-protection rationale “is as unsophisticated as the investors it is supposed to protect.” Many policymakers and dubious commentators have long resisted this thinking. One of their main arguments is that the securities laws protect investors by reducing unfair informational unevenness among stock market participants. The divide continues to this day, with each side often talking past the other. This Article provides a long-overdue close examination of how stock market information asymmetry (“IA”) affects ordinary investors. The examination reveals that IA likely results in both significant costs and benefits for these investors. Moreover, it shows why, for a large portion of the ordinary-investor universe, the latter likely dominate. Remarkably, the reductions in IA provided by disclosure, fraud, and insider trading laws therefore harm many ordinary investors. These theories indicate that each side in the investor-protection divide has overstated its case in some ways, and understated it in others. They consequently pave the way for a more informed dialogue than that which has prevailed to date. Given the resulting ambiguity as to how IA affects ordinary investors as a whole, the theories also provide support to calls for Congress and the Securities and Exchange Commission to abandon their investor focus in favor of a social one.

Keywords: Securities Regulation, Capital Markets Regulation

Suggested Citation

Haeberle, Kevin S., Information Asymmetry and the Protection of Investors (July 29, 2018). Available at SSRN: or

Kevin S. Haeberle (Contact Author)

William & Mary Law School ( email )

613 South Henry St
Williamsburg, VA 23185
United States

Register to save articles to
your library


Paper statistics

Abstract Views
PlumX Metrics