CEO horizon problem and characteristics of board of directors and compensation committee

Extant research finds inconclusive evidence about the CEO horizon problem. One possible explanation is that board of directors, especially compensation committees, intervene to mitigate the CEO horizon problem. In this study, we examine whether the characteristics of board of directors and compensation committee affect their effectiveness in mitigating the CEO horizon problem. We find that retiring CEOs are more likely to reduce R&D expenditures when CEOs have more power, and director tenure is longer; retiring CEOs in firms with large board of directors and compensation committee are less likely to manage accruals.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163492/1/jcaf22446.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163492/2/jcaf22446_am.pd

The Shape of the Blade with the highest power generation efficiency

The purpose of our wind turbine blade design research is to find the most efficient blade shape for power production. Our plan was to first study the size and structure of existing blades, then use the AutoDesk Inventor 3D program to design a suitable blade profile. After 3D printing the desired blade profile it would be tested using the existing wind turbine simulation equipment in the lab. The Inventor software allowed us to model, and repeatedly debug, to ensure that it our mount fit into the hub of the turbine holding the fixed blade. After repeated changes, the printer finally produced three sets of fan blades of different shapes. The wind simulation equipment still must be used be to complete our testing. By adjusting the wind speed and measuring the recorded output power, it will eventually be converted into an experimental model: a certain shape of blade generates higher electrical power

Why Non-accelerated Filers Voluntarily Comply with SOX 404b?

This paper investigates the managers’ incentives to voluntarily comply with SOX 404b and the determinants of firms who voluntarily disclose SOX 404b internal control over financial reporting assessment. We find that voluntary SOX 404b reporting non-accelerated filers are more likely to receive effective internal control over financial reporting opinion than accelerated filers and large accelerated filers. We find that voluntary SOX 404b reporting non-accelerated filers are more likely to hire Big Four as independent auditors than non-SOX 404b reporting non-accelerated filers. We also predict and found substantially sufficient cases where non-accelerated filers which used to be, or ex-post became accelerated filers or large accelerated filers, and non-accelerated filers with parent companies complying with SOX 404b are motivated to voluntarily comply with SOX 404b

Dataset Distillation: A Comprehensive Review

Recent success of deep learning is largely attributed to the sheer amount of data used for training deep neural networks.Despite the unprecedented success, the massive data, unfortunately, significantly increases the burden on storage and transmission and further gives rise to a cumbersome model training process. Besides, relying on the raw data for training \emph{per se} yields concerns about privacy and copyright. To alleviate these shortcomings, dataset distillation~(DD), also known as dataset condensation (DC), was introduced and has recently attracted much research attention in the community. Given an original dataset, DD aims to derive a much smaller dataset containing synthetic samples, based on which the trained models yield performance comparable with those trained on the original dataset. In this paper, we give a comprehensive review and summary of recent advances in DD and its application. We first introduce the task formally and propose an overall algorithmic framework followed by all existing DD methods. Next, we provide a systematic taxonomy of current methodologies in this area, and discuss their theoretical interconnections. We also present current challenges in DD through extensive experiments and envision possible directions for future works.Comment: 23 pages, 168 references, 8 figures, under revie

CFO Promotion-based Incentives and Earnings Management

This study examines whether CFO promotion-based incentives induce opportunistic reporting activities. We find that CFO promotion-based incentives, measured by the pay gap between the CEO and the CFO, are positively associated with accruals management and accounting misconduct in the pre-SOX period and the probability of meeting or beating analysts’ forecasts in both the pre- and post-SOX periods. Further analysis shows that CFO promotion-based incentives are negatively associated with real earnings management in both the pre- and post-SOX periods. In addition, we find some evidence that the association between CFO promotion-based incentives and opportunistic reporting activities is stronger before CEO turnovers. We also document that CFOs engage in more opportunistic financial reporting when the pay gap between the CFO and other VPs is greater. Overall, our findings suggest that CFO promotion-based incentives may encourage CFOs to engage in opportunistic reporting activities but mitigate real earnings management

Theoretically Guaranteed Policy Improvement Distilled from Model-Based Planning

Model-based reinforcement learning (RL) has demonstrated remarkable successes on a range of continuous control tasks due to its high sample efficiency. To save the computation cost of conducting planning online, recent practices tend to distill optimized action sequences into an RL policy during the training phase. Although the distillation can incorporate both the foresight of planning and the exploration ability of RL policies, the theoretical understanding of these methods is yet unclear. In this paper, we extend the policy improvement step of Soft Actor-Critic (SAC) by developing an approach to distill from model-based planning to the policy. We then demonstrate that such an approach of policy improvement has a theoretical guarantee of monotonic improvement and convergence to the maximum value defined in SAC. We discuss effective design choices and implement our theory as a practical algorithm -- Model-based Planning Distilled to Policy (MPDP) -- that updates the policy jointly over multiple future time steps. Extensive experiments show that MPDP achieves better sample efficiency and asymptotic performance than both model-free and model-based planning algorithms on six continuous control benchmark tasks in MuJoCo