307 research outputs found
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
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