经济人的“再生”:对一种新综合的探讨与辩护

本文力图对经济学帝国主义现象中复兴的新经济人提供一个较明确的描述,纠正倡导者和批评者共通的某些误解。本文的重点是:通过把社会偏好、个人社会价值等概念引入经济人模型的讨论之中,给能纳入这一分析范式的行为(特别是利他行为)确定某些标准;重新界定经济人在不同层次上的理性行为特征,以说明赋予经济人的理性程度将取决于所研究的主题的具体情形;进而,讨论经济人的标准原型及其变型在理论分析中各自适用的范围和局限性。     

关系取向对员工满意影响的实证研究

在现有文献中,有关关系取向对员工满意影响的研究尚属空白.(1)通过15位服务人员的深度访谈,对关系取向的内涵进行定性探索;(2)预调查对量表的信度和效度进行评价并修改部分问项;(3)对天津连锁药店的86位店长和217位销售人员的正式问卷调查表明:关系取向、关系交换与员工满意正相关;个体文化差异对关系交换有负向影响,而对关系取向有正向影响.研究结果可以指导经理们提高上下级关系管理的有效性.     

埃尔斯伯格悖论评析

主观期望效用理论认为个体的概率分布可以由行为的偏好导出,并且理性人的行为选择应该遵循主观期望效用理论,但是,埃尔斯伯格悖论显示了没有一个主观的概率测度,而且实际行为的选择并不总是和该理论的结果相一致。因此,围绕埃尔斯伯格悖论,展开了行为决策理论应该是规范的还是描述的争论。     

IMF新决议的突破：双边汇率监督范围之厘定

明确地划定双边汇率监督覆盖的范围是IMF新决议的重大突破。新决议以外部稳定作为统领原则和核心，借助临近原则将对外部稳定具有直接和重要影响的会员国政策纳入汇率监督的范围。新决议还对国内政策、国内稳定与外部稳定的关系做了廓清，认为国内政策通过促进国内稳定来促进外部稳定，同时对经济增长与外部稳定的关系进行了明确。新决议对我国具有重要影响，我国应重视但不应不适当地放大新决议的影响，最重要的是利用新决议的特点和有利规定为人民币汇率主动、可控和渐进性改革保驾护航。     

区位决策视角下的集群生命周期分析

本文在对当前产业集群生命周期研究述评的基础上,将集群周期划分为成长、成熟、衰退、再发展四个阶段,并从企业区位决策的视角对集群生命周期的不同阶段进行分析。然后通过考察企业区位决策因子的动态变化和集群区核心产业的区外出口量增长率、主导产业的企业数量、修正后的空间基尼系数来对生命周期阶段进行划分,分析不同阶段下政府的决策行为。     

工程项目投标阶段的风险决策方法研究

文章根据工程项目投标阶段风险决策的内容和特点，在考虑纯风险和投机风险的基础上，从单项风险入手，运用区间数和区间概率的性质，先描述各个风险要素，然后进行单项风险对策间的对比择优，并将其结果对应于方案、进度、成本费用及投标报价总风险补偿费的确定，最终实现承包商的风险成本最小化。     

城区特征对房贷的影响

城区特征通过多种途径影响商业银行住房抵押贷款决策.商业银行可以根据城区特征判断一个城区的住房抵押贷款违约风险,进而确定对该城区的最低首付款比例或拒贷率.通过分析国外城区特征对商业银行住房抵押贷款决策的影响机制,联系我国实际情况,指出我国商业银行根据城区特征进行住房抵押贷款决策具有合理性,建议商业银行密切关注城区住房价格波动率,并建议政府限制中低收入城区商业银行的数量.     

On the informational content of advice: a theoretical and experimental study

This paper examines the market for advice and the underlying perception that advice is useful and informative. We do this by first providing a theoretical examination of the informational content of advice and then by setting up a series of experimental markets where this advice is sold. In these markets we provide bidders with a demographic profile of the “experts” offering advice.The results of our experiment generate several interesting findings. The raw bid data suggest that subjects bid significantly more for data than they do for advice. Second, in the market for advice there appears to be no consensus as to who are the best advisors although on average economists demand the highest mean price and women suffer a discount. In addition, we find that whether a subject suffers from a representativeness bias in the way he or she processes data has an impact on how he or she bids for advice and on his or her willingness to follow it once offered. Finally, we find that on average people impute a low level of informativeness onto advice, consistent with their bidding behavior for data versus advice.This work was done under grant number SES-0425118 of the National Science Foundation. The authors would like to recognize the Center for Experimental Social Science at New York University for its additional support. We also acknowledge the help of Elizabeth Potamites for her research assistance.     

上市公司如何在非理性世界中确定股权回报率

确定股权回报率是进行长期投资决策的关键步骤,但在现实非理性世界中难以直接运用传统财务理论所推荐的资本资产定价模型来确定股权回报率。针对FAR与NEER方法,本文首先探讨这两类方法的理论依据并揭示其本质内涵,然后说明:在非理性世界中,如果上市公司管理者追求公司价值最大化目标,那么就应该基于公司项目的基本风险来确定股权回报率;相反地,如果管理者追求近期股价最大化,那么就应该基于市场投资者的预期来确定股权回报率。     

An effective going concern prediction model for the sustainability of enterprises and capital market development

The purpose of this study is to construct a two-stage effective and innovative going concern prediction model to predict going concern doubt for the sustainability of enterprises and capital market development. Samples of this study are the companies listed on the Taiwan Stock Exchange or the Taipei Exchange, totalling 196 companies and including 49 companies with going concern doubt and 147 normal companies (with no going concern doubt). The data are taken from the Taiwan Economic Journal (TEJ) and the Market Observation Post System during the period from 2001 to 2016 (totalling 16 years). This study adopts a two-stage way to construct the going concern prediction models. In Stage I, the traditional statistical method of stepwise regression (SR) and the data mining technique artificial neural network (ANN) are applied to select the important variables. In Stage II, two decision tree algorithms (data mining techniques): classification and regression tree (CART) and C5.0 are used to establish the prediction models. The results show that the SR + CART model has the highest going concern prediction accuracy, with an overall accuracy of 87.42%.