Business Commons^™

Deterring Algorithmic Manipulation, Gina-Gail S. Fletcher

Faculty Scholarship

Does the existing anti-manipulation framework effectively deter algorithmic manipulation? With the dual increase of algorithmic trading and the occurrence of “mini-flash crashes” in the market linked to manipulation, this question has become more pressing in recent years. In the past thirty years, the financial markets have undergone a sea change as technological advancements and innovations have fundamentally altered the structure and operation of the markets. Key to this change is the introduction and dominance of trading algorithms. Whereas initial algorithmic trading relied on preset electronic instructions to execute trading strategies, new technology is introducing artificially intelligent (“AI”) trading algorithms that …

Cleaning Corporate Governance, Jens Frankenreiter, Cathy Hwang, Yaron Nili, Eric L. Talley

Faculty Scholarship

Although empirical scholarship dominates the field of law and finance, much of it shares a common vulnerability: an abiding faith in the accuracy and integrity of a small, specialized collection of corporate governance data. In this paper, we unveil a novel collection of three decades’ worth of corporate charters for thousands of public companies, which shows that this faith is misplaced.

We make three principal contributions to the literature. First, we label our corpus for a variety of firm- and state-level governance features. Doing so reveals significant infirmities within the most well-known corporate governance datasets, including an error rate exceeding …