关于社会责任会计的辨析

随着许多社会问题越来越严重,人们要求企业在追求微观利益的同时,也要承担相应的社会责任。社会责任会计应运而生,并成为会计关注的热点问题。本文主要对社会责任会计与传统企业会计、社会会计、责任会计和环境会计之间的关系进行分析,以期为更准确的理解社会责任会计提供参考。     

决策树法在某化工厂扩能改造决策中的应用

基于某化工厂扩能改造的实际需求,提出改造局部工艺来提高产量、大改造来提高产量、解决关键瓶颈设备来提高产量三个备选方案。应用决策树法对备选方案进行分析计算,三个备选方案的益损期望值分别为1520.62万元、1197.48万元、1648.33万元,解决关键瓶颈设备来提高产量为最佳方案。     

论董事会在银行风险管理中的主导作用

当前,全球金融市场危机频发、风险剧增,我国商业银行能否经得起新一轮的考验和冲击．关键在于能否切实提高风险管理能力。银行必须从源头抓起,彻底转变公司治理结构,完善公司治理机制,特别要发挥好董事会的主导作用,从根本上解决风险控制的源动力问题。围绕这一问题,本文探讨了董事会在商业银行风险管理中的战略定位、主要职责、发挥作用的条件以及应采取的基本策略。     

Multidimensional private information,market structure,and insurance markets

We investigate whether selection based on multidimensional private information in risks and risk preferences can, under different market structures, result in a negative correlation between insurance coverage and ex post realization of risk. We show that, under perfect competition, selection based on multidimensional private information does not result in the negative correlation property, unless there is a sufficiently high loading factor. However, it is possible to generate the negative correlation property under monopoly when risk and risk preference types are sufficiently negative dependent. We also clarify the connections between important concepts such as adverse/advantageous selection and positive/negative correlation property.     

Modelling tourist flow association for tourism demand forecasting

The purpose of this study is to examine tourism demand for Singapore from 1995 to 2013 by six major origin countries which belong to three different regions. Unlike prior tourism research, we take into account the dependence relations among the different tourist flows via copula. Copula is a statistical model of dependence and measurement of association. Specifically, we investigate the association between two tourist flows in each region. Based on empirical copula estimation, the Frank function has been identified as the most appropriate to capture the pairwise dependence structures of tourist flows. The copula-based approach combined with econometric models is proposed for tourism demand analysis that can be used to predict tourist arrivals. We apply the copula-ARDL and copula-ECM frameworks to generate joint forecasts of tourist arrivals from three regions. The findings show that the forecast performance of the Frank copula-based model outperforms the benchmark model which corresponds to the independence structure (no association) of tourist flows.     

Estimating emissions based on the integration of microscopic traffic simulation and vehicle dynamics model

This study proposes an integrated simulation approach, which consists of a microscopic traffic simulation model, a vehicle dynamics model, and an emission estimation model, in order to estimate emissions based on more reliable vehicle performance measures. The vehicle performance measures such as engine power and engine speed significantly relate to the amount of emissions, and road curvatures and inclinations are the core inputs affecting these vehicle performance measures. Therefore, providing reliable vehicle performance measures reflecting the road geometric attributes is critical for a reliable emission estimation. This study proposes to use the microscopic traffic simulation model for generating vehicle trajectories, which is advantageous in modeling various traffic situations, and the vehicle dynamics model for producing the vehicle performance measures based on the vehicle trajectories. Finally, the outputs from the vehicle dynamics model are fed into the emission estimation model to compute emission measures. This study conducted a case-study using two road sections, one is a hypothesized road section, including various curvatures and inclinations with regular variations, and the other is a Kesselberg road section, which is an actual geometry in Bayern, Germany. The emission measures are estimated in these case-study road sections using both an existing simulation approach and the proposed integrated simulation approach. The difference between these two emission estimation approaches is discussed in terms of the emission measures, including fuel consumption, nitrogen oxides, and particulate matters.     

Using an analogical thinking model as an instructional tool to improve student cognitive ability in architecture design learning process

Lack of creativity is a problem often plaguing students from design-related departments. Therefore, this study is intended to incorporate analogical thinking in the education of architecture design to enhance students’ learning and their future career performance. First, this study explores the three aspects of architecture design curricula, interaction and students’ cognitive development. Then the study discusses learning based on analogical thinking into two parts: analogical learning and analogical reasoning. The samples in this study were the freshmen of the Department of Architecture, China University of Technology. The model was used in a five-week unit of the course of Architecture Design, a required course for all the freshmen. Questionnaires were given to the students before and after the unit to evaluate the influences of the model in the dimensions of Teaching, Teacher-student Interactions, Student–student Interactions, Cognitive Development, and Overall Learning Performance. The questionnaire results were collected and statistically analyzed. The analysis results indicated that, the students scored significantly higher in each of the dimension after the unit than before the unit. These findings suggested that the use of an analogical thinking model can be helpful in the teaching of architecture design. Hopefully, the findings of this study can provide helpful references for not only teaching of architecture design but also future related research.     

Banking deregulation and corporate tax avoidance

We investigate whether tax avoidance substitutes for external financing. We exploit interstate banking deregulation as a quasi-external shock to examine whether...     

Operational risk quantified with spectral risk measures: a refined closed-form approximation

The quantification of operational risk has become an important issue as a result of the new capital charges required by the Basel Capital Accord (Basel II) to cover the potential losses of this type of risk. In this paper, we investigate second-order approximation of operational risk quantified with spectral risk measures (OpSRMs) within the theory of second-order regular variation (2RV) and second-order subexponentiality. The result shows that asymptotically two cases (the fast convergence case and the slow convergence) arise depending on the range of the second-order parameter. We also show that the second-order approximation under 2RV is asymptotically equivalent to the slow convergence case. A number of Monte Carlo simulations for a range of empirically relevant frequency and severity distributions are employed to illustrate the performance of our second-order results. The simulation results indicate that our second-order approximations tend to reduce the estimation errors to a great degree, especially for the fast convergence case, and are able to capture the sub-extremal behavior of OpSRMs better than the first-order approximation. Our asymptotic results have implications for the regulation of financial institutions, and may provide further insights into the measurement and management of operational risk.     

A Multicriteria Approach for Modeling Small Enterprise Credit Rating: Evidence from China

ABSTRACT

As the engine of China’s economy, small enterprises have been the central to the country’s economic development. However, given the characteristics of the small enterprises loan (i.e., short borrowing period, large volume, small amount and incomplete information), it is extremely challenging for financial institutions to assess their creditworthiness. Thus, it seriously delays and restricts the financing access for small enterprises. In an attempt to relieve the financing difficulty of small enterprises, this article makes use of 687 small wholesale and retail enterprises in a regional commercial bank in China, to establish a credit rating indicator system composed of 17 indicators by using both partial correlation analysis and probit regression. It then utilizes TOPSIS together with fuzzy C-means to score the credit ratings of our sample of small enterprises. With the dual test of default discrimination and ROC curve, the prediction accuracy of the established indicator system has reached 80.10% and 0.917, respectively, indicating the robustness and validity of our credit rating system.