Law Commons^™

Barbarians Inside The Gates: Raiders, Activists, And The Risk Of Mistargeting, Zohar Goshen, Reilly S. Steel

Faculty Scholarship

This Article argues that the conventional wisdom about corporate raiders and activist hedge funds — raiders break things and activists fix them — is wrong. Because activists have a higher risk of mistargeting — mistakenly shaking things up at firms that only appear to be underperforming — they are much more likely than raiders to destroy value and, ultimately, social wealth.

As corporate outsiders who challenge the incompetence or disloyalty of incumbent management, raiders and activists play similar roles in reducing “agency costs” at target firms. The difference between them comes down to a simple observation about their business models: …

Patently Risky: Framing, Innovation And Entrepreneurial Preferences, Elizabeth Hoffman, David L. Schwartz, Matthew L. Spitzer, Eric L. Talley

Faculty Scholarship

An emerging common wisdom holds that courts have made it “too hard” to obtain patent protection in critical industries. The origin of this criticism dates back at least as far as the United States Supreme Court’s 2012 landmark opinion in Mayo Collaborative Services v. Prometheus Laboratories, Inc. which (the argument goes) triggered a chain reaction of judicial opinions rendering patent rights progressively more difficult to secure. Two years later, the Supreme Court decided Alice Corp. v. CLS Bank, another opinion widely viewed as restricting patent rights. And, barely three years after Mayo, the Federal Circuit cited it in …

From Corporate Law To Corporate Governance, Ronald J. Gilson

Faculty Scholarship

In the 1960s and 1970s, corporate law and finance scholars gave up on their traditional approaches. Corporate law had become “towering skyscrapers of rusted girders, internally welded together and containing nothing but wind.” In finance, the theory of the firm was recognized as an “empty box.” This essay tracks how corporate law was reborn as corporate governance through three examples of how we have usefully complicated the inquiry into corporate behavior. Part I frames the first complication, defining governance broadly as the company’s operating system, a braided framework of legal and non-legal elements. Part II adds a second complication by …

A Machine Learning Classifier For Corporate Opportunity Waivers, Gabriel V. Rauterberg, Eric L. Talley

Faculty Scholarship

Rauterberg & Talley (2017) develop a data set of “corporate opportunity waivers” (COWs) – significant contractual modifications of fiduciary duties – sampled from SEC filings. Part of their analysis utilizes a machine learning (ML) classifier to extend their data set beyond the hand-coded sample. Because the ML approach is likely unfamiliar to some readers, and in the light of its great potential across other areas of law and finance research, this note explains the basic components using a simple example, and it demonstrates strategies for calibrating and evaluating the classifier.