汽车制造业上市公司资本结构与财务绩效关系研究

探讨了我国汽车制造业上市公司资本结构对财务绩效的影响.对汽车制造业上市公司资本结构与财务绩效相关数据进行了描述性统计分析,对资本结构和财务绩效综合因子之间的关系进行了因子分析和回归分析.结果表明:汽车制造业上市公司资产负债率与财务绩效不显著负相关,流动负债率与财务绩效显著负相关,股权集中度与财务绩效不显著正相关.并针对性地提出了对策:合理降低资产负债率和流动负债率、适度提高股权集中度、发展多元融资.     

货币政策下议价能力对企业业绩影响

为了更加真实反映议价能力与企业业绩之间的关系,引入HHI指数法和熵值法度量议价能力,在控制企业规模、上市年限及企业地区金融经济发展差异等干扰因素的情境下,利用2008-2017年A股制造业404家样本公司的相关财务数据,采用多元回归分析方法进行分析.研究发现,议价能力与财务业绩之间是正相关关系,与市场业绩之间呈倒"U"关系且存在一个临界点,当议价能力小于该点时,议价能力与市场业绩正相关,当议价能力大于该点时,议价能力与市场业绩负相关.在引入货币政策作为调节变量的情况下,进一步研究发现,在议价能力对财务业绩的影响中,货币紧缩时期的议价能力对财务业绩的影响小于货币宽松时期的议价能力对财务业绩的影响;而在议价能力对市场业绩的影响中,货币紧缩时期的议价能力对市场业绩的影响大于货币宽松时期的议价能力对市场业绩的影响.     

高科技公司中的高管特征与绩效的实证研究

与传统企业所有权和经营权分离不同的是,人力资本及其控制的无形资产对高科技企业价值的贡献相对于有形资产的重要性大大提高.高技术公司的治理与规范化公司的治理存在很大的差异,甚至是本质上的差异,这种差异主要来自于高技术企业人力资本的价值和团队合作.本文对高科技公司中的高管特征与绩效进行了实证研究.研究表明:在竞争激烈的高科技上市公司中,ROE与托宾Q值呈现显著正相关;高管人员持股比例总和与公司绩效指标(ROE)具有显著正相关关系;高管人员平均薪酬与公司绩效指标(ROE)具有显著正相关关系.建议高科技公司选好经理人,增加高管人员的持股比例及完善高管人员薪酬体制的建设,进而降低代理成本,提高公司绩效.     

我国上市公司资本结构影响因素实证分析

本文选取了可能影响企业资本结构的多个指标变量进行分析 ,利用主成分分析提供的方法将变量综合成彼此互不相关的少数几个主成分。再用主成分 (作为回归自变量 )对企业的 5种资本负债比 (作为回归因变量 )进行多元回归分析 ,得出了影响企业资本结构的主要因素 ,以及这些因素与企业资本结构之间的关系 ,为企业确定资本结构提供参考依据 ,为企业财务决策提供支持。     

基于ZZ杠杆模型的资本结构影响因素研究——来自中国制造业上市公司的经验证据

ZZ杠杆模型理论上解决"定量求解最优资本结构"这一财务领域长期难以解决的问题,由于该模型建立在一系列理想假设之上,有待实证的进一步检验.基于ZZ杠杆模型研究了资本结构的影响因素,从沪市A股制造业上市公司中筛选了200家符合条件的公司为样本,选用其2008年的横截面财务数据进行实证检验.结果表明,ZZ杠杆模型中的变量是显著影响我国上市公司资本结构的因素:即公司价值波动率与资本结构负相关,所得税税率及平均债务期限与资本结构正相关.结论证实了ZZ杠杆模型在中国市场总体上是成立的.     

分位数回归方法在资本结构影响因素分析中的应用

传统的OLSE回归只能得到一组系数估计值,因此无法深入分析不同财务杠杆程度下资本结构的影响因素.为此本文尝试着将分位数回归理论引入到回归模型中,通过对我国上市公司的相关数据进行分位数回归分析后发现,不同资产负债率水平下,企业股权结构、企业产生内部资源的能力对资本结构的影响效果不同:低负债率水平下,两者均与债务水平正相关;而高负债率水平下,又均与债务水平负相关.研究还发现,企业的成长性、企业规模以及财务困境成本与债务水平正相关,而盈利能力、非债务税盾则与债务水平负相关。     

辽宁省制造业规模经济与要素传导机制研究

研究对辽宁省经济发展举足轻重的制造业,要解决两个问题:一是辽宁省制造业的发展是否合理;二是需要对辽宁省制造业做出怎样的要素调整.基于1999年到2009年辽宁省制造业33个子行业的相关数据,运用C-D生产函数模型和VAR模型,结合脉冲响应分析,考察辽宁省制造业的发展与要素传导的动态影响.结果表明,辽宁省制造业存在显著的规模经济,资本和劳动投入对制造业产出具有长期的动态影响,但这种影响并不显著.     

国际资本结构动态决定因素的实证研究——基于制造业上市公司的面板数据

选取12个国家地区制造业上市公司2004-2008年的公司特征数据和国家宏观经济指标,研究国际间资本结构动态决定的宏观与微观因素的协整作用关系.主要发现有:一是发达国家对于现代资本结构理论假设条件和关系诠释比发展中国家更为贴近,发展中国家公司特征因素与目标资本结构的关系异象根源于特定经济制度背景;二是克服截面数据POOL回归分析法忽视国别差异因素的缺陷,通过国别哑变量和沃尔德F统计量检验得出,不同国家的公司资本结构微观特征决定因素存在定量上的差异;三是选用能够克服异方差问题的国别哑变量系数标准化为权重的加权最小二乘法回归表明,国家宏观经济法律因素直接或者通过公司特征指标间接作用于国际资本结构决策.     

基于Translog生产函数的中国汽车工业规模经济与替代弹性及价格弹性研究

使用迭代似无关回归(ISUR)方法估计了以生产工人、非生产工人和资本为投入要素的中国汽车工业Translog生产函数及中国汽车工业规模报酬的阶数,利用获得的估计计算了投入要素之间的替代弹性并通过替代弹性计算了要素需求的价格弹性。在此基础上,对中国汽车工业规模经济的状况、投入要素之间的替代或互补关系和要素价格变化对要素需求的影响进行了分析.结果显示:在所研究的期间内,中国汽车工业处于规模报酬不变阶段;生产工人和资本之间以及非生产工人和资本之间是替代关系,而生产工人和非生产工人之间是互补关系;对中国汽车工业而言,三个要素需求缺乏弹性。     

高技术服务业与制造业互动发展实证研究

在基于高技术服务业与制造业之间存在互动发展的理论假设前提下,从组织生态学、空间结构和价值链的角度探讨二者互动关系的内在机理,同时选取2008-2018年我国高技术服务业与制造业相关数据,构建VAR模型进行实证分析,通过平稳性检验、协整检验和格兰杰因果检验进行验证,利用脉冲响应和方差分解技术分析二者动态影响规律.实证研究显示,我国高技术服务业与制造业存在协同发展的长期稳定关系,呈显著正相关,且高技术服务业对制造业转型升级推动作用更为突出.基于此研究结果,从国家、产业及企业层面提出对策建议,促进二者互动发展的良性循环.     

初中学生心理测量的统计分析

本文用相关分析说明了〈中学生素质特点分类培养〉项目研究中所用各种心理诊断量表整体组合运用的合理性 ;用因子分析简化了测试项目的指标体系 ,选定了适当的公共主因子 ,并对公因子给予了合理的解释 ;用聚类分析依据因子得分对学生实施心理素质特点分类 ;根据学生的心理素质特点类型提出了相应的宏观培养策略     

统计方法在研究微积分与后续课程相关性中的应用与实证分析

着眼于本科生后期专业课程的学习效果,探讨微积分课程学习效果的有效性.以北京理工大学某经管类专业全部学生大一大二两学年的学习成绩为依据,分析微积分课程学习与后续理科课程学习的相关性,提出评估微积分课程学习效果的量化指标θ值的概念,并基于回归分析和相关性分析给出其算法.在实例分析中,通过研究,发现学生微积分课程学习效果的好坏会影响他们对后续相关课程的学习,同时也发现学生将微积分知识运用到间接相关科目的能力比运用到直接相关的科目的能力薄弱.     

Enabling e-transactions with multi-attribute preference models

This paper describes potential applications of multi-attribute preference models (MAPM) in e-commerce and offers some guidelines for their implementation. MAPM are methodologies for modeling complex preferences that depend on more than one attribute or criterion, and include multi-attribute utility theory, conjoint analysis, and the Analytic Hierarchy Process. There are numerous examples of applications in e-commerce that would benefit from the acquisition of information regarding the preferences of a consumer, a customer, an advice seeker, or a decision maker. Here, the focus is on applications of MAPM models in B2C and B2B websites, where preferences of consumers are assessed for the purpose of identifying products or services that closely match their needs.     

What drives value creation in investment projects? An application of sensitivity analysis to project finance transactions

Evaluating the economic attractiveness of large projects often requires the development of large and complex financial models. Model complexity can prevent management from obtaining crucial information, with the risk of a suboptimal exploitation of the modelling efforts. We propose a methodology based on the so-called “differential importance measure (D)$(D)$” to enhance the managerial insights obtained from financial models. We illustrate our methodology by applying it to a project finance case study. We show that the additivity property of D grants analysts and managers full flexibility in combining parameters into any group and at the desired aggregation level. We analyze investment criteria related to both the investors’s and lenders’ perspectives. Results indicate that exogenous factors affect investors (sponsors and lenders) in different ways, whether exogenous variables are considered individually or by groups.     

A sensitivity analysis algorithm for hierarchical decision models

In this paper, a comprehensive algorithm is developed to analyze the sensitivity of hierarchical decision models (HDM), including the analytic hierarchy process and its variants, to single and multiple changes in the local contribution matrices at any level of the decision hierarchy. The algorithm is applicable to all HDM that use an additive function to derive the overall contribution vector. It is independent of pairwise comparison scales, judgment quantification techniques and group opinion combining methods. The allowable range/region of perturbations, contribution tolerance, operating point sensitivity coefficient, total sensitivity coefficient and the most critical decision element at a certain level are identified in the HDM SA algorithm. An example is given to demonstrate the application of the algorithm and show that HDM SA can reveal information more significant and useful than simply knowing the rank order of the decision alternatives.     

我国工业污染分布状况研究

环境污染越来越受到我国的重视,而工业污染是造成环境污染的重要原因.为了对我国的环境污染分布状况进行深入了解,从总量出发,利用聚类分析和因子分析法分析了我国工业污染的分布情况,并探讨了各类地区工业污染差异的原因,为我国进行侧重点治理环境污染提出可行性建议.     

Composite multilinearity,epistemic uncertainty and risk achievement worth

Risk achievement worth is one of the most widely utilized importance measures. RAW is defined as the ratio of the risk metric value attained when a component has failed over the base case value of the risk metric. Traditionally, both the numerator and denominator are point estimates. Relevant literature has shown that inclusion of epistemic uncertainty (i) induces notable variability in the point estimate ranking and (ii) causes the expected value of the risk metric to differ from its nominal value. We investigate the conditions under which the equality of the nominal and expected values of a reliability risk metric holds. We then study how the presence of epistemic uncertainty affects RAW and the associated ranking. We propose an extension of RAW (called ERAW) which allows one to obtain a ranking robust to epistemic uncertainty. We discuss the properties of ERAW and the conditions under which it coincides with RAW. We apply our findings to a probabilistic risk assessment model developed for the safety analysis of NASA lunar space missions.     

When does data envelopment analysis outperform a naïve efficiency measurement model?

This paper compares the results from data envelopment analysis (DEA) to a naïve efficiency measurement model, which generates a scalar efficiency score by averaging all output–input ratios. Random data and real-life data are used to test the relative performance of the naïve model against various DEA models. The results suggest that the proposed the naïve model replicates DEA efficiency scores almost perfectly for constant return-to-scales and low heterogeneity in output–input data. It is therefore concluded that heterogeneity in output–input data is important to take advantage of the capability of DEA. It is also shown that heterogeneity is more relevant to efficiency measurement than the number of dimensions.     

Singular Value Decomposition and Its Visualization

Singular value decomposition (SVD) is a useful tool in functional data analysis (FDA). Compared to principal component analysis (PCA), SVD is more fundamental, because SVD simultaneously provides the PCAs in both row and column spaces. We compare SVD and PCA from the FDA view point, and extend the usual SVD to variations by considering different centerings. A generalized scree plot is proposed to select an appropriate centering in practice. Several useful matrix views of the SVD components are introduced to explore different features in data, including SVD surface plots, image plots, curve movies, and rotation movies. These methods visualize both column and row information of a two-way matrix simultaneously, relate the matrix to relevant curves, show local variations, and highlight interactions between columns and rows. Several toy examples are designed to compare the different variations of SVD, and real data examples are used to illustrate the usefulness of the visualization methods.