Do Heads Roll? An Empirical Analysis of CEO Turnover and Pay When the Corporation is Federally Prosecuted

Does the criminal prosecution of a corporation affect the CEO? Or do criminal actions directed at the organization itself pose few consequences for the individuals at the top, and the CEO in particular? While CEOs are rarely themselves prosecuted, organizations could discipline CEOs through paycuts or outright replacing the CEO in response to a criminal prosecution. We sought to examine whether and how that occurs. We focus our analysis on a dataset of public companies that settled criminal cases brought by federal prosecutors from 2000-2014. We compared those companies to the larger set of companies in the Execucomp database of S&P 1500 firms, focusing on CEO compensation and turnover during the same time period. We examined the time period before and after prosecution, and the year that the company resolved the criminal charges against the company. We found that in the year that the company settled its prosecution, through a guilty plea or a deferred or non-prosecution agreement, there was a significantly higher level of CEO turnover. However, we do not find evidence of CEO pay cut. Second, for the prosecuted firms that did not have CEO turnover after prosecution, there is no evidence of a reduction in compensation. Indeed, we observed a spike in CEO bonuses in the year of prosecution—confirming concerns expressed by judges, prosecutors, lawmakers, and academics that corporate prosecutions do not sufficiently impact high-level decision-makers like CEOs. For the prosecuted firms that did have CEO turnover after prosecution, there is some evidence of a pay cut, both to salary and bonus, prior to the replacement of the CEO. These results raise larger questions whether federal prosecutors targeting the most serious corporate crimes sufficiently incentivize accountability at the top

Energy-based Structure Prediction for d(Al70Co20Ni10)

We use energy minimization principles to predict the structure of a decagonal quasicrystal - d(AlCoNi) - in the Cobalt-rich phase. Monte Carlo methods are then used to explore configurations while relaxation and molecular dynamics are used to obtain a more realistic structure once a low energy configuration has been found. We find five-fold symmetric decagons 12.8 A in diameter as the characteristic formation of this composition, along with smaller pseudo-five-fold symmetric clusters filling the spaces between the decagons. We use our method to make comparisons with a recent experimental approximant structure model from Sugiyama et al (2002).Comment: 10pp, 2 figure

Least-squares finite elements for Stokes problem

A least-squares method based on the first-order velocity-pressure-vorticity formulation for the Stokes problem is proposed. This method leads to a minimization problem rather than to a saddle-point problem. The choice of the combinations of elements is thus not subject to the Ladyzhenskaya-Babuska-Brezzi (LBB) condition. Numerical results are given for the optimal rate of convergence for equal-order interpolations

P11 Resonances with Dubna-Mainz-Taipei Dynamical Model for pi-N Scattering and Pion Electromagnetic Production

We present the results on P11 resonances obtained with Dubna-Mainz-Taipei (DMT) dynamical model for pion-nucleon scattering and pion electromagnetic production. The extracted values agree well, in general, with PDG values. One pole is found corresponding to the Roper resonance and two more resonances are definitely needed in DMT model. We further find indication for a narrow P11 resonance at around 1700 MeV with a width of around 50 MeV in both pi-N and gamma-pi reactions.Comment: Contribution to the Proceedings of NSTAR 2011 - The 8th International Workshop on the Physics of Excited Nucleons, May 17-20, 2011, Thomas Jefferson National Accelerator Facility, Newport News, Virginia US

Coil combination using linear deconvolution in k-space for phase imaging

Background: The combination of multi-channel data is a critical step for the imaging of phase and susceptibility contrast in magnetic resonance imaging (MRI). Magnitude-weighted phase combination methods often produce noise and aliasing artifacts in the magnitude images at accelerated imaging sceneries. To address this issue, an optimal coil combination method through deconvolution in k-space is proposed in this paper. Methods: The proposed method firstly employs the sum-of-squares and phase aligning method to yield a complex reference coil image which is then used to calculate the coil sensitivity and its Fourier transform. Then, the coil k-space combining weights is computed, taking into account the truncated frequency data of coil sensitivity and the acquired k-space data. Finally, combining the coil k-space data with the acquired weights generates the k-space data of proton distribution, with which both phase and magnitude information can be obtained straightforwardly. Both phantom and in vivo imaging experiments were conducted to evaluate the performance of the proposed method. Results: Compared with magnitude-weighted method and MCPC-C, the proposed method can alleviate the phase cancellation in coil combination, resulting in a less wrapped phase. Conclusions: The proposed method provides an effective and efficient approach to combine multiple coil image in parallel MRI reconstruction, and has potential to benefit routine clinical practice in the future

The Limitations of Optimization from Samples

In this paper we consider the following question: can we optimize objective functions from the training data we use to learn them? We formalize this question through a novel framework we call optimization from samples (OPS). In OPS, we are given sampled values of a function drawn from some distribution and the objective is to optimize the function under some constraint. While there are interesting classes of functions that can be optimized from samples, our main result is an impossibility. We show that there are classes of functions which are statistically learnable and optimizable, but for which no reasonable approximation for optimization from samples is achievable. In particular, our main result shows that there is no constant factor approximation for maximizing coverage functions under a cardinality constraint using polynomially-many samples drawn from any distribution. We also show tight approximation guarantees for maximization under a cardinality constraint of several interesting classes of functions including unit-demand, additive, and general monotone submodular functions, as well as a constant factor approximation for monotone submodular functions with bounded curvature

Uncertainty triggers overreaction: evidence from corporate takeovers

Behavioural finance models suggest that under uncertainty, investors overweight their private information and overreact to it. We test this theoretical prediction in an M&A framework. We find that under high information uncertainty, when investors are more likely to possess firm-specific information, acquiring firms generate highly positive and significant gains following the announcement of private stock and private cash acquisitions (positive news) while the market heavily punishes public stock (negative news) deals. On the other hand, under conditions of low information uncertainty, when investors do not possess private information, the market reaction is complete (i.e. zero abnormal returns) irrespective of the type of acquisition. Overall, we provide empirical evidence that shows that information uncertainty plays a significant role in explaining short-run acquirer abnormal returns