Optimal Contracts for Risk Managers

This study analyzed the principal-agent problem, in which the agent performs risk management tasks, and considered the cost minimization problem of the principal, the objective of which is to design the cheapest contract inducing a target effort. Our results confirm that a one-step bonus contract should be used, which means that a bonus contract is most efficient for the principal in terms of incentive provision. A new condition to justify the first-order approach in our model was also provided

Do Financial Analysts Facilitate Investors’ Assessment Of Earnings?: Evidence From The Korean Stock Market

This paper seeks to enhance our understanding of financial analysts in assisting market investors’ use of accounting earnings in the Korean stock market. We examine whether stock returns differentially reflect earnings information for firms with analyst coverage. We propose that the role of analysts as external monitors as well as information intermediaries enhances the market investors’ valuation of earnings. We find that market valuation of earnings is higher for firms with analyst following. Furthermore, market investors’ valuation of earnings increases (or decreases) with the number of analysts (or with the dispersion of analysts’ forecasts). This suggests that the beneficial effect of analysts arises through the quantity and quality of analysts’ information. This study contributes to the literature by investigating the important role of analysts in emerging market

Optimal Harvesting for an Age-Spatial-Structured Population Dynamic Model with External Mortality

We study an optimal harvesting for a nonlinear age-spatial-structured population dynamic model, where the dynamic system contains an external mortality rate depending on the total population size. The total mortality consists of two types: the natural, and external mortality and the external mortality reflects the effects of external environmental causes. We prove the existence and uniqueness of solutions for the population dynamic model. We also derive a sufficient condition for optimal harvesting and some necessary conditions for optimality in an optimal control problem relating to the population dynamic model. The results may be applied to an optimal harvesting for some realistic biological models

Joint Link Scheduling and Routing for Load Balancing in STDMA Wireless Mesh Networks

In wireless mesh networks, it is known to be effective to use a TDMA based MAC than a contention-based CSMA. In addition, if spatial TDMA is used, network performance can be improved further because of its spatial reuse effect. However this scheme still has a disadvantage in the system performance aspect without a load-balanced routing because the resource of links that are not used is wasted and frequently used links are out of resources. That is, the number of available flows in network is limited because load balancing is not performed. In this paper, we propose joint link scheduling and routing through a cross-layer scheme. For this, we propose a load balancing routing method to maximize available resources under the given traffic pattern and scheduling method for maximizing link utilization on the given route. These two methods are iterated until an optimized solution can be obtained. The proposed algorithm can be formulated using a mathematical LP problem and we show that it is very effective for load balancing compared to simple adoption of IEEE 802.11s which is a standard TDMA protocol in wireless mesh network. If the proposed algorithm is applied to initial design solution such as Smart Grid, the number of available flows can be increased and the load on each link can be balanced

Concentration Separation Prediction Model to Enhance Prediction Accuracy of Particulate Matter

Demand for more accurate particulate matter forecasts is accumulating owing to the increased interest and issues regarding particulate matter. Incredibly low concentration particulate matter, which accounts for most of the overall particulate matter, is often underestimated when a particulate matter prediction model based on machine learning is used. This study proposed a concentration-specific separation prediction model to overcome this shortcoming. Three prediction models based on Deep Neural Network (DNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM), commonly used for performance evaluation of the proposed prediction model, were used as comparative models. Root mean squared error (RMSE), mean absolute percentage error (MAPE), and accuracy were utilized for performance evaluation. The results showed that the prediction accuracy for all Air Quality Index (AQI) segments was more than 80 percent in the entire concentration spectrum. In addition, the study confirmed that the over-prediction phenomenon of single neural network models concentrated in the ‘normal’ AQI region was alleviated

Metal/graphene sheets as p-type transparent conducting electrodes in GaN light emitting diodes

We demonstrate the use of graphene based transparent sheets as a p-type current spreading layer in GaN light emitting diodes (LEDs). Very thin Ni/Au was inserted between graphene and p-type GaN to reduce contact resistance, which reduced contact resistance from similar to 5.5 to similar to 0.6 Omega/ cm(2), with no critical optical loss. As a result, LEDs with metal-graphene provided current spreading and injection into the p-type GaN layer, enabling three times enhanced electroluminescent intensity compared with those with graphene alone. We confirmed very strong blue light emission in a large area of the metal-graphene layer by analyzing image brightness.open281