Agency conflicts in public and negotiated transfers of corporate control

We analyze control transfers in firms with a dominant minority blockholder and otherwise dispersed owners, and show that the transaction mode is important. Negotiated block trades preserve a low level of ownership concentration, inducing more inefficient extraction of private benefits. In contrast, public acquisitions increase ownership concentration, resulting in fewer private benefits and higher firm value. Within our model, the incumbent and new controlling party prefer to trade the block because of the dispersed shareholders' free-riding behavior. We also explore the regulatory implications of this agency problem and its impact on the terms of block trades

Why higher takeover premia protect minority shareholders

Posttakeover moral hazard by the acquirer and free‐riding by the target shareholders lead the former to acquire as few sharcs as necessary to gain control. As moral hazard is most severe under such low ownership concentration, inefficiencies arise in successful takeovers. Moreover, share supply is shown to be upward‐sloping. Rules promoting ownership concentration limit both agency costs and the occurrence of takeovers. Furthermore, higher takeover premia induced by competition translate into higher ownership concen‐tration and are thus beneficial. Finally, one share‐one vote and simple majority are generally not optimal, and socially optimal rules need not emerge through private contracting

Set-Membership Inference Attacks using Data Watermarking

In this work, we propose a set-membership inference attack for generative models using deep image watermarking techniques. In particular, we demonstrate how conditional sampling from a generative model can reveal the watermark that was injected into parts of the training data. Our empirical results demonstrate that the proposed watermarking technique is a principled approach for detecting the non-consensual use of image data in training generative models.Comment: Preliminary wor

Legal investor protection and takeovers

This paper examines the role of legal investor protection for the efficiency of the market for corporate control when bidders are financially constrained. In the model, stronger legal investor protection increases bidders' outside funding capacity. However, absent effective bidding competition, this does not improve efficiency, as the bid price, and thus bidders' need for funds, increases one-for-one with the pledgeable income. In contrast, under effective competition for the target, the increased outside funding capacity improves efficiency by making it less likely that more efficient but less wealthy bidders are outbid by less efficient but wealthier rivals

Legal Investor Protection and Takeovers

We study the role of legal investor protection for the efficiency of the market for corporate control. Stronger legal investor protection limits the ease with which an acquirer, once in control, can extract private benefits at the expense of non-controlling investors. This, in turn, increases the acquirer’s capacity to raise outside funds to finance the takeover. Absent effective competition for the target, the increased outside funding capacity does not make efficient takeovers more likely, however, because the bid price, and thus the acquirer’s need for funds, increase in lockstep with his pledgeable income. In contrast, under effective competition, the increased outside funding capacity makes it less likely that the takeover outcome is determined by the bidders’ financing constraints–and thus by their internal funds–and more likely that it is determined by their ability to create value. Accordingly, stronger legal investor protection can improve the efficiency of the takeover outcome. Taking into account the interaction between legal investor protection and financing constraints also provides new insights into the optimal allocation of voting rights, sales of controlling blocks, and the role of legal investor protection in cross-border M&A.

Analysis, Simulation, and Verification of Knowledge-Based, Rule-Based, and Expert Systems

Mathematically sound techniques are used to view a knowledge-based system (KBS) as a set of processes executing in parallel and being enabled in response to specific rules being fired. The set of processes can be manipulated, examined, analyzed, and used in a simulation. The tool that embodies this technology may warn developers of errors in their rules, but may also highlight rules (or sets of rules) in the system that are underspecified (or overspecified) and need to be corrected for the KBS to operate as intended. The rules embodied in a KBS specify the allowed situations, events, and/or results of the system they describe. In that sense, they provide a very abstract specification of a system. The system is implemented through the combination of the system specification together with an appropriate inference engine, independent of the algorithm used in that inference engine. Viewing the rule base as a major component of the specification, and choosing an appropriate specification notation to represent it, reveals how additional power can be derived from an approach to the knowledge-base system that involves analysis, simulation, and verification. This innovative approach requires no special knowledge of the rules, and allows a general approach where standardized analysis, verification, simulation, and model checking techniques can be applied to the KBS

Developmental stages in dynamic plant growth models

During the growth of red beet plants in a closed environment plant growth chamber, a change in metabolism was observed (decreasing photosynthetic quotient) which was not predicted by a previously developed simple dynamic model of photosynthesis and respiration reactions. The incorporation of developmental stages into the model allowed for the representation of this change in metabolism without adding unnecessary complexity. Developmental stages were implemented by dividing the model into two successive sub-models with independent yields. The transition between the phases was detected based on online measurements. Results showed an accurate prediction of carbon dioxide and oxygen fluxes