Capital injections with negative surplus and delays: models and analysis

This work develops a new model to deal with the scenario that some companies can still run business even the surplus falls below zero temporarily. With such a scenario in mind, we allow the surplus process to continue in this negative-surplus period, during which capital injections will be ordered to assist in the stabilization of financial structure, until the financial status becomes severe enough to file bankruptcy. The capital injections will be modeled as impulse controls. By introducing the capital injections with time delays, optimal dividend payment and capital injection policies are considered. Using the dynamic programming approach, the value function obeys a quasi-variational inequality. With delays in capital injections, the company will be exposed to the risk of bankruptcy during the delay period. In addition, the optimal dividend payment and capital injection strategies should balance the expected cost of the possible capital injections and the time value of the delay periods. This gives rise to a stochastic control problem with mixed singular and delayed impulse controls. Under general assumptions, the lower capital injection barrier is determined, where bankruptcy occurs. The closed-form solution to the value function and corresponding optimal policies are obtained.     

Asset Price Anomalies under Bounded Rationality

We analyze the classical asset pricing model assuming non fully rationalagents.Agents forecast future prices cum dividend through an adaptive learning rule.This assumption provides an explanation of some anomalies encounteredin the empirical analysis of asset prices under full rationality:returns are serially correlated(positively over a short horizon and negatively over a longer horizon)and the dividend yield predicts future returns (positive correlation).Considering the continuous time limit process,the same regularities are established analytically for price increments.     

Using genetic algorithm based knowledge refinement model for dividend policy forecasting

Dividend policy is one of most important managerial decisions affecting the firm value. Although there are many studies regarding decision-making problems, such as credit policy decisions through bankruptcy prediction and credit scoring, there is no research, to our knowledge, about dividend prediction or dividend policy forecasting using machine learning approaches in spite of the significance of dividends. For dealing with the problems involved in literature, we suggest a knowledge refinement model that can refine the multiple rules extracted through rule-based algorithms from dividend data sets by utilizing genetic algorithm (GA). The new technique, called “GAKR (genetic algorithm knowledge refinement)”, aims to combine the advantages of both knowledge consolidation and GA. The main result of the cross-validation procedure is the average accuracy rate of prediction in the five sets over the five iterations. The experiments show that GAKR model always outperforms other models in the performance of dividend policy prediction; we can predict future dividend policy more correctly than any other models. The major advantages of GAKR model can be summarized as follows: (1) Classification process of GAKR can be very fast with a compact set of rules. In other words, fast training mechanism of GAKR is possible regardless of data set sizes. (2) Multiple rules extracted by GAKR development process are much simpler and easier to understand. Moreover, GAKR model can discriminate redundant rules and inconsistent rules.     

Combined MCDM techniques for exploring stock selection based on Gordon model

Basing on the Gordon model perspective and applying multiple criteria decision making (MCDM), this research explores the influential factors and relative weight of dividend, discount rate, and dividend growth rate. The purpose is to establish an investment decision model and provides investors with a reference/selection of stocks most suitable for investing effects to achieve the greatest returns. Taking full consideration of the interrelation effect among variables of the decision model, this paper introduced analytical network process (ANP) and examined leading electronics companies spanning the hottest sectors of lens, solar, and handset by experts. Empirical findings indicated that dividend was affected by industry outlook, earnings, operating cash flow, and dividend payout rate; discount rate was affected by market β and risk-free rate; and dividend growth rate was affected by earnings growth rate and dividend payout growth rate. Also, according to literatures, discount rate possessed a self-effect relationship. Among the eight evaluation criteria, market β was the most important factor influencing investment decisions, followed by dividend growth rate and risk-free rate. In stock evaluations, leadership companies in the solar industry outperformed those in handset and lens, becoming investors’ favorite stock group at the time that this research was conducted.     

Empirical regularities from interacting long- and short-memoryinvestors in an agent-based stock market

This paper explores some of the empirical features generated in an agent-based computational stock market with market participants adapting and evolving over time. Investors view differing lengths of past information as being relevant to their investment decision-making process. The interaction of these memory lengths in determining market prices creates a kind of market ecology in which it is difficult for the more stable longer horizon agents to take over the market. What occurs is a dynamically changing market in which different types of agents arrive and depart depending on their current relative performance. This paper analyzes several key time series features of such a market. It is calibrated to the variability and growth of dividend payments in the United States. The market generates some features that are remarkably similar to those from actual data. These include magnifying the volatility from the dividend process, inducing persistence in volatility and volume, and generating fat-tailed return distributions     

Estimating the demographic dividend: Evidence from Indian states

Using decadal data for 1961–2011, the paper explores the demographic dividend of major Indian states. Accordingly, we first explore Zipf’s law and thereafter, examine Gibrat’s law for the growth of state population. Based on these findings, we investigate the demographic dividend of state population. The results appear to suggest that the demographic dividend varies across states and these “dividends” are in fact, large in magnitude, especially for the laggard states.     

Fabrication of barrier ribs of PDP via powder injection molding

In this paper, an attempt was made to explore a possibility of powder micro injection molding process in manufacturing ceramic microstructures such as barrier ribs of plasma display panel. The barrier ribs are glass matrix composites with ceramic powder (alumina and/or titania) filler. In this molding process, a thermosetting paste was molded into polydimethlsilosane soft molds prepared by replication of thick film resist (SU-8) molds. The SU-8 mold was patterned with UV-lithography. The effects of powder content in the paste on paste viscosity and sintering characteristics of molded samples were examined. In addition, effect of molding speed on pore trapping in the microstructure was studied. These results indicated that the powder micro injection molding process at ambient temperature has merits of low-pressure injection molding process with superior mold release characteristics.     

Numerical solutions of optimal risk control and dividend optimization policies under a generalized singular control formulation

This paper develops numerical methods for finding optimal dividend pay-out and reinsurance policies. A generalized singular control formulation of surplus and discounted payoff function is introduced, where the surplus is modeled by a regime-switching process subject to both regular and singular controls. To approximate the value function and optimal controls, Markov chain approximation techniques are used to construct a discrete-time controlled Markov chain. The proofs of the convergence of the approximation sequence to the surplus process and the value function are given. Examples of proportional and excess-of-loss reinsurance are presented to illustrate the applicability of numerical methods.     

Knowledge discovery in corporate events by neural network rule extraction

Over the last two decades, artificial neural networks (ANN) have been applied to solve a variety of problems such as pattern classification and function approximation. In many applications, it is desirable to extract knowledge from trained neural networks for the users to gain a better understanding of the network’s solution. In this paper, we use a neural network rule extraction method to extract knowledge from 2222 dividend initiation and resumption events. We find that the positive relation between the short-term price reaction and the ratio of annualized dividend amount to stock price is primarily limited to 96 small firms with high dividend ratios. The results suggest that the degree of short-term stock price underreaction to dividend events may not be as dramatic as previously believed. The results also show that the relations between the stock price response and firm size is different across different types of firms. Thus, drawing the conclusions from the whole dividend event data may leave some important information unexamined. This study shows that neural network rule extraction method can reveal more knowledge from the data.     

Glass barrier ribs for a transparent AC plasma display

Abstract— Transparent glass barrier ribs are proposed for the development of a transparent plasma display. Glass barrier ribs were fabricated via a wet‐etching process with a 20% hydrofluoric acid. Two different etching methods for glass barrier ribs are investigated: a single‐sided etching method and a double‐sided etching method. Simulation and measurement results show that glass barrier ribs formed by the double‐sided etching method are more transparent compared to those formed by a single‐sided etching method. A fabricated transparent AC plasma display with glass barrier ribs showed good transparency and was operated by using a conventional driving scheme.     

一种基于最优匹配的低能耗栅栏修复方法

无线传感器栅栏覆盖通常被应用于室外环境,用来监测特定区域。由于传感器自身原因或外界环境影响,栅栏覆盖易被破坏,高效且低能耗的栅栏修复算法尤为重要。本文提出了一种基于最优匹配的低能耗栅栏修复方法(Low-Power Barrier Coverage Repair Method Based on Optimal Match,BCR-OM),该方法首先遍历栅栏搜索栅栏间隙;然后计算完整修复栅栏间隙时,所需要的可移动传感器节点的最小数量;随后利用传感器节点构建栅栏间隙修复路径;最后利用Hungarian算法得出节点的最佳派遣方案,将节点派遣至对应位置,完成栅栏间隙修复。通过仿真实验验证,当栅栏间隙长度为300 m时,本文方法修复栅栏消耗的能量仅为Optimal算法消耗能量的57%,且栅栏修复率比Optimal算法高出近8%,减少能耗的同时有效提高了栅栏修复率。     

On Automation in the Verification of Software Barriers: Experience Report

We present an experience report on automating the verification of the software barrier synchronization primitive. The informal specification of the primitive is: when a thread calls the software barrier function, the thread halts until all other threads call their instances of the software barrier function. A successful software barrier call ensures that each thread has finished its portion of work before the threads start exchanging the results of these portions of work. While software barriers are widely used in parallel versions of major numerical algorithms and are indispensable in scientific computing, software barrier algorithms and their implementations scarcely have been verified. We improve the state of the art in proving the correctness of the major software barrier algorithms with off-the-shelf automatic verification systems such as Jahob, VCC, Boogie, Spin and Checkfence. We verify a central barrier, a C implementation of a barrier, a static tree barrier, a combining tree barrier, a dissemination barrier, a tournament barrier, a barrier with its client and a barrier on a weak memory model. In the process, we introduce a novel theorem proving method for proving validity of formulas containing cardinalities of comprehensions and improve the capabilities of one of the verification systems. Based on our experience, we propose new challenges in the verification of software barriers.     

Two-Phase Barrier: A Synchronization Primitive for Improving the Processor Utilization

Barrier is widely used for synchronization in parallel programs. Since the process arrived earlier than others should wait at the barrier, the total processor utilization decreases. In this paper, to find the sources of the barrier waiting time, parallel programs are executed on the various grain sizes through execution-driven simulations. In simulation studies, we found that even if approximately equal amounts of work are distributed to each processor, all processes may not arrive at a barrier at the same time. The reasons are that the different numbers of cache misses and instructions within in partitioned grains result in the difference in arrival time of processors at the barrier. In this paper, the two-phased barrier is considered to reduce the blind waiting time in the traditional barrier scheme, which can be simply constructed by dividing one specific stage for the synchronization into two stages. On each stage, processes decide their stall or not, which is dependent on the current execution state of grains running on any given processors. Simulation results show that the reduced barrier waiting times attributed to the two-phased barrier contribute to the performance improvement of parallel programs.     

Valuation of American Continuous-Installment Options

We present three approaches to value American continuous-installment options written on assets without dividends or with continuous dividend yield. In an American continuous-installment option, the premium is paid continuously instead of up-front. At or before maturity, the holder may terminate payments by either exercising the option or stopping the option contract. Under the usual assumptions, we are able to construct an instantaneous riskless dynamic hedging portfolio and derive an inhomogeneous Black–Scholes partial differential equation for the initial value of this option. This key result allows us to derive valuation formulas for American continuous-installment options using the integral representation method and consequently to obtain closed-form formulas by approximating the optimal stopping and exercise boundaries as multipiece exponential functions. This process is compared to the finite difference method to solve the inhomogeneous Black–Scholes PDE and a Monte Carlo approach.     

姿态变化一致有界的姿态稳定控制器设计

针对航天器姿态稳定控制问题, 设计一种迭代学习姿态控制器. 将连续非周期运动的姿态跟踪过程分解为队列重复运动, 采用前一周期的姿态跟踪误差修正后一周期的控制输入, 分别对未知参数和干扰构建有界迭代学习律, 给出航天器姿态稳定控制器, 并从理论上分析了闭环系统的渐近稳定性和姿态跟踪误差的一致有界性. 通过在轨捕获非合作目标过程中航天器姿态跟踪控制问题的数值仿真, 验证了迭代学习控制器的鲁棒性和强抗干扰性.

     

Asset Pricing and Productivity Growth: The Role of Consumption Scenarios

The paper analyzes the performance of asset prices implied by an aggregate macroeconomic growth model under two different consumption hypotheses: overlapping generations of agents with two period lives versus the infinitely lived agent. The production side of the economy is described by a random growth model with a competitive labor market and an exogenously given random dividend payout ratio. For an isoelastic technology with multiplicative production shocks this implies a random dynamical system for the firm’s rate of profit with a unique asymptotically stable random fixed point for a large class of productivity growth and dividend payout ratio processes. Based on an extensive numerical study of stationary solutions we show that the two consumption scenarios imply a limited number of diverse effects regarding equity and bond returns and equity premia.     

Utilizing knowledge links in the implementation of system development methodologies

Developing technical ‘know-how’ is a slow process that can become a barrier in implementing complex administrative technologies such as a software development methodology. To overcome this barrier, organizations often seek knowledge links that can enhance learning and minimize inevitable problems that are encountered in an implementation process. This paper presents the findings of an empirical study that examines the prescribed versus actual use of external consultants, universities and vendors as knowledge links during the implementation of systems development methodologies (SDM). First, the study assesses the need and value of establishing and utilizing links to external sources of expertise for successful SDM implementation. We then identify and analyze a gap that exists between what the links to external knowledge are perceived to be capable of contributing and what the links to external knowledge are actually contributing during SDM implementation. In conclusion, possible reasons for the gap are discussed.