We test the empirical validity of a claim that has been playing a central role in debates on corporate governance — the claim that interventions by activist hedge funds have a detrimental effect on the long-term interests of companies and their shareholders. We subject this claim to a comprehensive empirical investigation, examining a long five-year window following activist interventions, and we find that the claim is not supported by the data.
Fraud occurs when there is an incentive and the opportunity to commit fraud and the fraudster can rationalize his behavior. Academic literature in financial economics has investigated incentives and opportunities to commit fraud but has largely ignored the fraudsters' ability to rationalize fraud. We address this gap by positing that economic actors, whose earnings are restricted by regulations, cheat more and rationalize such behavior as an effort to get "out of the hole" that regulations have forced them into.
We develop and test hypotheses regarding the role of social contagion in customer adoption of new sales channels. We examine two aspects of social contagion (local contagion and homophily) and two channels (Internet and bricks-and-mortar store). Drawing on diffusion theory, we propose a conceptual framework that identifies the factors associated with new channel adoption.
We model an electronic limit order book as a multi-class queueing system under fluid dynamics, and formulate and solve a problem of limit and market order placement to optimally buy a block of shares over a short, predetermined time horizon. Using the structure of the optimal execution policy, we identify microstructure variables that affect trading costs over short time horizons and propose a resulting microstructure-based model of market impact costs.
It is becoming increasingly easier for researchers and practitioners to collect eye tracking data during online preference measurement tasks. We develop a dynamic discrete choice model of information search and choice under bounded rationality, that we calibrate using a combination of eye-tracking and choice data. Our model extends the directed cognition model of Gabaix et al. (2006) by capturing fatigue, proximity effects, and imperfect memory encoding and by estimating individual-level parameters and partworths within a likelihood-based, hierarchical Bayesian framework.
This paper studies the competition between Web 2.0 communities in a game theoretic framework. We model three important features of these institutions: (i) firms' content is usually user-generated; (ii) consumers' content preferences are governed by local network effects, and (iii) consumers have strong tendencies to multi-home. Our analyses reveal that ex-ante identical community sites can acquire differentiated market positions that spontaneously emerge from user-generated content.
This study takes advantage of a unique episode in which the SEC distributed securities filings to a small group of investors ahead of their public releases. The random delay time provides a rare natural experiment for examining how markets process new private information. It takes minutes — not seconds — for informed traders to incorporate fundamental information into stock prices. The early-informed convey more information into stock prices when the delay before public release is longer.
Ad Exchanges are emerging Internet markets where advertisers may purchase display ad placements, in real-time and based on specific viewer information, directly from publishers via a simple auction mechanism. Advertisers join these markets with a pre-specified budget and participate in multiple second-price auctions over the length of a campaign. This paper studies the competitive landscape that arises in Ad Exchanges and the implications for publishers' decisions.
We consider a multi-period single product pricing problem with an unknown demand curve. The seller's objective is to adjust prices in each period so as to maximize cumulative expected revenues over a given finite time horizon; in so doing, the seller needs to resolve the tension between learning the unknown demand curve, and earning revenues by solving the dynamic optimization problem.