区域生产性服务业发展主导产业选择模型研究

研究了区域生产性服务业发展主导产业选择模型。通过分析和总结主导产业选择的准则、指标和方法,提出了基于钻石理论的区域生产性服务业主导产业选择的指标体系,利用整体有效的DEA模型来反映各行业绩效,利用AHP反映政府在产业选择过程中起到的作用,构建了区域生产性服务业主导产业选择模型,并通过实证分析,证实了该模型的可行性。     

A novel fuzzy neural approach to data reconstruction and failure prediction

Bank failure prediction is of great importance to a bank's clients, policy-makers and regulators. Various traditional models have been employed to study bank failures. Unfortunately, their performances are unsatisfactory. In this paper, the pseudo-outer product fuzzy neural network using the compositional rule of inference and singleton fuzzifier (POPFNN-CRI(S))-based bank failure prediction model is proposed. It employs computational bank failure analysis techniques coupled with reconstruction of missing financial data in financial covariates that are available from publicly available financial statements as inputs. The performance of the proposed model is assessed through the classification rate of 3636 US banks observed over a 21-year period. The effects of missing data reconstruction are investigated. Copyright © 2009 John Wiley & Sons, Ltd.     

Value at risk methodology of international index portfolio under soft conditions (fuzzy-stochastic approach)

The approach to modelling uncertainty of the international index portfolio by the value at risk (VAR) methodology under soft conditions by fuzzy-stochastic methodology is described in the paper. The generalised term uncertainty is understood to have two aspects: risk modelled by probability (stochastic methodology) and vagueness sometimes called impreciseness, ambiguity, softness is modelled by fuzzy methodology. Thus, hybrid model is called fuzzy-stochastic model. Input data for a stochastic model are unique distribution functions and crisp (real) data. Input data for fuzzy model are fuzzy numbers and crisp (real) data. Input data for hybrid model are fuzzy probability distribution functions, unique distribution functions, and crisp (real) data. Softly defined VAR model is constructed as hybrid model because it is supposed that the input data are difficult to determine as crisp numbers or as some unique distribution functions. Risk is modelled by stochastic methodology on the VAR basis and vagueness is modelled through the fuzzy numbers. The analytical delta normal VAR methodology for international index portfolio under soft conditions is described including illustrative example. It is shown, that methodology described could be considered to be generalised sensitivity analysis.     

我国政府和企业投资重点、时点及地点组合研究

目前我国在政府和企业投资的研究领域中,只是单纯考虑投资重点、投资时点和投资地点中某一项或两项因素的影响指标,很少将三者组合起来进行分析,但是三者均对投资起着至关重要的作用,本文运用焦点问题法,以评价指标体系为切入点,通过分析每个点的影响指标,从而建立一套相对完善的投资选择综合评价指标体系,并在分析量化相关指标的基础上,确立了模糊综合评价的方法对投资中重点、时点、地点进行评价,对于我国政府和企业投资决策的全面性起到了关键作用,并为投资决策提供了良好的判断依据.     

Early-warning analysis for currency crises in emerging markets: A revisit with fuzzy clustering

Currency crises, also often called balance-of-payment crises, occur when massive capital outflows force a country to devalue or float its currency. The world-wide integration of capital markets since the 1980s and 1990s has increased the degree of capital mobility, which also determined a substantial turbulence in foreign exchange markets and frequent currency crises. In this paper, we explore advanced supporting instruments for predicting currency crises, based on an empirical study of the currency crisis episodes in 23 emerging markets around the world during the second half of last century. More specifically, we investigate the usefulness of prediction models built based on the fuzzy c-means method. First we build clustering models that partition data into a certain number of overlapping natural groups. Thereafter, we classify the data clusters into early-warning clusters and tranquil clusters. We compare the performance of our models with a conventional c-means clustering model and a benchmark probit model. The results show that the proposed models achieve a similar level of out-of-sample performance as the probit model and c-means model. The fuzzy approach also introduces additional explanatory advantages into the early-warning analysis process. Copyright © 2010 John Wiley & Sons, Ltd.     

Evaluating Investments in Advanced Manufacturing Technology: A Fuzzy Set Theory Approach

In this paper, a model for the evaluation of investments in advanced manufacturing technology is developed. Many authors have called for an integration of financial and non-financial factors in such evaluations, and this paper demonstrates that it is conceptually possible to do this using the mathematics of the analytic hierarchy process and fuzzy set theory. The development of the model has certain distinguishing features. First, it is based on a conceptual framework that combines the three dimensions of risk, financial return and non-financial factors. The empirical basis for this has been investigated and previously reported by the authors. Second, models previously developed and reported in the literature are shown to suffer from certain flaws relating to the use of linguistic scales, the ranking of fuzzy performance indicators and partiality in the treatment of investment decision variables. These issues are addressed through the development of simpler linguistic scales based on the analytic hierarchy, a revised procedure for ranking fuzzy numbers and an attempt to build a comprehensive model through the three dimensions described above. Triangular fuzzy numbers are used throughout in order to make the mathematics tractable and relatively easy to understand, and to facilitate presentation of a worked example. However, so that the reader is not misled, attention is drawn to some of the complexities in fuzzy arithmetic, especially the important distinction between subtraction/division and deconvolution of fuzzy numbers.     

基于多层次模糊综合评价的中小企业信用风险评估

引入多层次模糊评价方法,设计包括行业状况、上下游状况、产品状况、管理水平、财务状况和资信状况的指标体系,构建多层次模糊综合评价的中小企业信用风险评估模型。算例分析表明该方法综合了定性因素和定量因素,能有效地评价中小企业的信用风险。     

Integrating sustainability performance measurement into logistics and supply networks: A multi-methodological approach

This paper seeks to address the way in which economic and environmental performance can be measured simultaneously, taking a multi-methodological approach to the logistics and supply chain management field in order to address sustainability challenges. The multi-methodological approach relies on the merits of different methodologies, provides more flexibility in tackling problems under investigation, and tolerates inaccurate estimation of parameters during the process. An illustrative case study (Westgate Ports) is undertaken in Australia in order to examine the ways in which the multi-methodological approach is applied, and how it assists during the decision making process in the adoption of green practices for freight transport logistics. The case validates the applicability and usefulness of the approach and highlights comparative outputs of costs and carbon emissions in freight transport logistics. Rail transport is identified as giving the opportunity to study the short distance container and freight distribution network, although initially this does not appear to be the most cost-effective option. This study finds that it is better to simultaneously consider performance indicators from different perspectives and to integrate them into one model of system measurement in order for corporations to improve their sustainability performance.     

Accounting towards sustainability in production and supply chains

Growing interest in sustainability and corporate supply chains accompanies increased globalisation across developed and developing countries, a stronger focus on the logistics of procurement behind international trade, and information flows between parties about corporate economic, social and environmental performance. Accounting provides information to oil the wheels of supply chain relationships. The purpose of this paper is to consider what an accounting for sustainability of production and supply chains might look like. An overview is provided of the issues associated with a broadening of accounting needed for sustainable supply chains. The paper highlights: ongoing problems of scope and terminology, lack of a broad sustainability focus because of complexity which stunts the impact on decision makers, and the need for transdisciplinary teams to increase connectedness and performance of the supply chain. The need for further research relating to three issues is identified. First, who undertakes the accounting for supply chains; second, why should a business function account for supply chain involvement; and, third, what information is relevant to different functional managers?     

基于Nelson—Siegel模型的我国利率期限结构的构建

利率问题一直都是经济金融研究中最基础、最核心的问题。利率可以反映出资金的供求状况，并受到物价水平、经济周期和预期等的影响。本文基于中国银行间债券市场的交易数据，利用基于贝叶斯推断的马尔科夫链蒙特卡罗模拟（MCMC）方法估计Hautsch＆Ou（2008）提出的动态的Nelson—Siegel模型，以构建我国的利率期限结构模型。     

Interpretability and mean‐square error performance of fuzzy inference systems For Data Mining

Over the years, many methods have become available for designing fuzzy inference systems from data. Their efficiency is usually characterized by a numerical index, the mean‐square error. However, for human–computer cooperation, another criterion is needed; the rule of interpretability. This paper analyses two kinds of fuzzy inference system: fuzzy clustering algorithms to organize and categorize data in homogeneous groups, and grid partitioning (generated from data or given by experts) of the multidimensional space. The methods are compared according to mean‐square error performance and an interpretability criterion. Simulation results carried out on a forecasting problem associated with stock market are included. Copyright © 2005 John Wiley & Sons, Ltd.     

不完全信息下的供应链风险评估方法研究

本文使用多属性决策方法进行供应链风险评估,在风险属性权重信息不完全的情况下,建立非线性规划模型求得各风险属性的权重值,并通过求与理想解之间的加权欧氏距离来评估风险值的大小,解决了评估专家由于知识、经验和偏好不完全相同而意见难以统一的困难,最后用一个例子说明了该方法的有效性和实用性。     

基于模糊综合评价法的社会企业合作伙伴选择研究

社会企业是一种受社会创业活动热潮推动产生的创新组织形式,其行为逐渐成为整个社会创新的助推力。基于组织间协同性、社会影响力、持续发展能力及组织外部环境等4因素构建社会企业非营利组织类合作伙伴选择评价指标体系,结合Theil不均衡指数和模糊综合评价法建立社会企业合作伙伴选择评价模型,通过实例应用既验证了社会企业非营利组织类合作伙伴选择评价指标体系的科学性和合理性,也检验了合作伙伴评价模型的有效性与可操作性。     

寿险公司理赔经理业绩评价模型研究

如何对理赔经理的业绩进行客观的评价目前越来越引起各寿险公司的关注,本文针对寿险公司理赔经理业绩评价运用客户关系图建立相应的KPI评价指标体系,并应用层次分析法确定各指标的权重,尝试将360度业绩评价法与模糊综合评价法相结合构建360度模糊综合评价模型。该模型有利于寿险公司更加客观地对理赔经理进行业绩评价。     

An integrated approach for green design: Life-cycle,fuzzy AHP and environmental management accounting

The growing awareness of environmental issues has made the design of eco-friendly products a critical task for modern businesses. Almost all the costs and the environmental performance of a product over its life-cycle are determined in its design and development phase. The selection of alternative green designs is, however, a major challenge in today's competitive environment. The increasing pressure on time-to-market conflicts with the analytical approach typically required when using conventional environmental management accounting (EMA) tools such as Life-Cycle Assessment (LCA) and Life-Cycle Costing (LCC). This paper introduces a comprehensive method that integrates the LCA and EMA concepts, fuzzy logic and Analytical Hierarchical Process (AHP), to measure the environmental and organisational performance of different designs. We propose a screening model to help designers reduce their reliance on LCA and present a case study to demonstrate that this approach provides a systematic method of evaluating alternative designs and identifying product design improvement options. The measurement approach presented in this research can help companies reduce development lead time by screening out undesirable design options. More importantly, the approach can be modelled with the mere use of an Excel spreadsheet, which means limited resources are needed to implement the proposed method.     

基于动态模糊评价的通信运营商合作伙伴选择模型及实证研究

基于三网融合背景下通信运营商合作伙伴选择影响因素的多样性和差异性,建立备选企业的共性指标和特性指标,结合BP神经网络、模糊评价和组合赋权的方法,提出基于BP神经网络的动态模糊综合评价模型,从现状和未来发展趋势两个方面对合作伙伴进行综合评价选择。以湖南移动推进城市光网建设为例,展示了合作伙伴选择的原理和过程。模型可以为通信运营商企业客观、全面、动态地评价和选择合作伙伴提供有效的决策支持。     

绿色供应链管理对制造企业绩效的影响

运用结构方程模型,依据广州、深圳两地共236家制造企业数据,考量环境协同、环境监督与企业绩效之间的关系。结果表明：绿色供应链管理将社会、环境责任信息公开和公众参与融入链式管理体系中,能协同上、下游企业环境管理活动,监督环境违规行为,实现环境绩效与财务绩效的双赢。     

Mutual fund performance: false discoveries, bias, and power

We analyze the performance of mutual funds from a multiple inference perspective. When the number of funds is large, random fluctuations will cause some funds falsely to appear to outperform the rest. To account for such “false discoveries,” a multiple inference approach is necessary. Performance evaluation measures are unlikely to be independent across mutual funds. At the same time, the data are typically not sufficient to estimate the dependence structure of performance measures. In addition, the performance evaluation model can be misspecified. We contribute to the existing literature by applying an empirical Bayes approach that offers a possible way to take these factors into account. We also look into the question of statistical power of the performance evaluation model, which has received little attention in mutual fund studies. We find that the assumption of independence of performance evaluation measures results in significant bias, such as over-estimating the number of outperforming mutual funds. Adjusting for the mutual fund investment objective is helpful, but it still does not result in the discovery of a significant number of successful funds. A detailed analysis reveals a very low power of the study. Even if outperformers are present in the sample, they might not be recognized as such and/or too many years of data might be required to single them out.     

Positive and normative judgments implicit in U.S. tax policy,and the costs of unequal growth and recessions

Calculating the welfare implications of changes to economic policy or shocks requires economists to decide on a normative criterion. One approach is to elicit the relevant moral criteria from real-world policy choices, converting a normative decision into a positive inference, as in the recent surge of “inverse-optimum” research. We find that capitalizing on the potential of this approach is not as straightforward as we might hope. We perform the inverse-optimum inference on U.S. tax policy from 1979 through 2010 and argue that the results either undermine the normative relevance of the approach or challenge conventional assumptions upon which economists routinely rely when performing welfare evaluations.