典型事实、混合Copula函数与金融市场相依结构研究

运用ARFIMA-FIAPARCH-skst模型对沪深300指数和香港恒生指数建立收益-波动模型, 然后结合估计的参数对模型进行修正以确立最终模型, 排除金融市场典型事实对相依关系的影响, 进而运用由Clayton、Frank和Gumbel组成的混合copula模型对相依结构进行建模。研究结果表明:内地市场和香港市场均未观察到显著的杠杆效应;由Clayton、Frank和Gumbel组成的混合Copula模型能够准确地描述两个市场之间的相依结构, 且两个市场下尾相依关系要强于上尾的相依关系, 通过动态混合copula也验证了这一明显的非对称关系。     

Tobin Q反映了企业价值吗——基于市场投机性的视角

Tobin Q被国内外财务学界广泛应用于企业价值的相关研究.本文提出,尽管Tobin Q与企业价值的内在关系得到了国外理论模型和经验证据的支持,但是我国暂时不具备全面应用Tobin Q的条件.对2002-2005年我国上市公司的实证研究表明,Tobin Q未能对全体上市公司的市场价值提供可靠度量,这一结论在控制了亏损的影响后没有发生改变.进一步,本文基于对我国资本市场现实背景的分析提出,股票市场的投机性可能对Tobin Q的应用存在一定影响.实证结果发现,投机性对Tobin Q的适用性问题提供了一定的解释.     

基于企业学习策略的集群持续创新机制及实证研究

由于集群网络的复杂性,集群创新机制的研究目前较为缺乏,有限的研究也往往假设集群企业具有同质性.本文提出,集群企业依据其在集群网络中的位置采用不同的学习策略,领导型企业更加侧重于探索式学习,跟随型企业更加侧重于利用式学习,两者的分工和异质性互动构成了集群持续创新的内在机理.本文在个体和企业层次双重学习研究的基础上,结合集群特有的组织条件,提出集群双重学习的机制,据此将集群企业进行分类,并建立集群中企业学习的理论模型,接着选取国内某地化工产业集群的236家企业作为样本,运用结构方程模型对集群中企业进行分类检验和比较研究.研究结果表明,集群中不同类型企业的学习策略存在差异,集群网络的作用是产生这一差异的可能原因;集群中不同类型企业的学习策略具有互补性,有利于企业间互动和集群的持续创新.     

信息环境、年报披露时间选择与下年盈余管理

年报披露时间是否有助于投资者合理估计下年盈余管理程度,不同信息环境对这种估计又有什么影响?本文利用2004-2006年间的3451家公司样本对此进行了检验.结果表明:年报披露时间与下年盈余管理负相关,年报披露时间能向市场传递有效的未来信号;在标准审计意见和有较高质量经营现金流量支持的信息环境下,年报披露时间选择对下年盈余管理的信号作用更强.这些结论对理解公司年报披露时间安排有一定积极意义.     

公司信息透明度与大股东资金占用研究

本文以2002-2006年非金融类的中国A股上市公司为研究对象,分析了公司信息透明度与大股东资金占用之间的关系.研究发现,在控制其它影响因素条件下,公司信息透明度越差,大股东资金占用问题越严重.该研究结论表明,提高公司信息透明度将有助于改善大股东与中小股东之间的信息不对称,缓解二者之间的代理冲突,减少大股东资金占用的可能性.     

大股东存在下的独立董事对公司业绩的影响——基于内生视角的审视

我国独立董事对公司业绩的治理效果一直是学者们感兴趣的话题,但此前的研究一直忽视了独立董事的内生性问题.本文使用四种模型检验了当大股东存在时独立董事对公司业绩的影响.研究发现,在排除内生性之后,独立董事对业绩的影响并不显著.本文认为,造成这种现象的原因在于当公司存在大股东的时候,公司董事会的构成会受到大股东的控制,从而使得独立董事制度流于形式.     

A successive approximation algorithm for the multiple knapsack problem

It is well-known that the multiple knapsack problem is NP-hard, and does not admit an FPTAS even for the case of two identical knapsacks. Whereas the 0-1 knapsack problem with only one knapsack has been intensively studied, and some effective exact or approximation algorithms exist. A natural approach for the multiple knapsack problem is to pack the knapsacks successively by using an effective algorithm for the 0-1 knapsack problem. This paper considers such an approximation algorithm that packs the knapsacks in the nondecreasing order of their capacities. We analyze this algorithm for 2 and 3 knapsack problems by the worst-case analysis method and give all their error bounds.     

Designing Logistic Information Platform to Fostering Development Trend in China Automobile Manufacturing Group

The logistics resource in China's automobile group can't be optimized and allocated because of the "information island" problem in the group's logistics information platform in the whole group. In addition, it is the development trend of China's automobile group LIN to build a logistics e - market in the range of the group.     

Inventory Management for Customers with Alternative Lead Times

It is common for suppliers operating in batch‐production mode to deal with patient and impatient customers. This paper considers inventory models in which a supplier provides alternative lead times to its customers: a short or a long lead time. Orders from patient customers can be taken by the supplier and included in the next production cycle, while orders from impatient customers have to be satisfied from the on‐hand inventory. We denote the action to commit one unit of on‐hand inventory to patient or impatient customers as the inventory‐commitment decision, and the initial inventory stocking as the inventory‐replenishment decision. We first characterize the optimal inventory‐commitment policy as a threshold type, and then prove that the optimal inventory‐replenishment policy is a base‐stock type. Then, we extend our analysis to models to consider cases of a multi‐cycle setting, a supply‐capacity constraint, and the on‐line charged inventory‐holding cost. We also evaluate and compare the performances of the optimal inventory‐commitment policy and the inventory‐rationing policy. Finally, to further investigate the benefits and pitfalls of introducing an alternative lead‐time choice, we use the customer‐choice model to study the demand gains and losses, known as demand‐induction and demand‐cannibalization effects, respectively.     

Structural Nonparametric Cointegrating Regression

Nonparametric estimation of a structural cointegrating regression model is studied. As in the standard linear cointegrating regression model, the regressor and the dependent variable are jointly dependent and contemporaneously correlated. In nonparametric estimation problems, joint dependence is known to be a major complication that affects identification, induces bias in conventional kernel estimates, and frequently leads to ill‐posed inverse problems. In functional cointegrating regressions where the regressor is an integrated or near‐integrated time series, it is shown here that inverse and ill‐posed inverse problems do not arise. Instead, simple nonparametric kernel estimation of a structural nonparametric cointegrating regression is consistent and the limit distribution theory is mixed normal, giving straightforward asymptotics that are useable in practical work. It is further shown that use of augmented regression, as is common in linear cointegration modeling to address endogeneity, does not lead to bias reduction in nonparametric regression, but there is an asymptotic gain in variance reduction. The results provide a convenient basis for inference in structural nonparametric regression with nonstationary time series when there is a single integrated or near‐integrated regressor. The methods may be applied to a range of empirical models where functional estimation of cointegrating relations is required.