智能创新生产力理论与中国经济发展深层思考

提出智能创新生产力概念及其理论模型,发现智能演进创新和非守恒规律及其作用原理,研究智能创新的核心生产力,即智能创新克服人的生理认知能力有限性与客观物质(能量)变化无限性之间的悖论,创造利用物质世界的无穷手段和能力,创造时间和空间,解决资源稀缺与社会需求矛盾;在此基础上发现智能创新自认知、自选择和自组织原理,破解著名的智能认知体系结构与优化代理体系结构(这是本文重要发现之一),研究智能创新的系统生产力,即智能经济相对于传统经济可以达到和实现理想的生产过程,使生产活动在开放的资源空间和动态系统中进行,虚拟智能的物理化使经济活动没有时空约束和资源稀缺约束,创新经济和社会的生产方式与组织形态.本文所提出的智能创新生产力理论模型,即智能创新的核心生产与系统生产力概念也为中国经济改革发展提出重要思考.     

高新技术企业财务管理柔性研究

从系统的角度分析了高新技术企业财务管理的特点和不确定性,在此基础上提出了高新技术企业财务管理柔性的概念,构建了财务管理柔性、柔性净效益及其变动幅度的度量方法,运用预控图原理对财务管理柔性进行动态监控。应用实例表明,该方法易于操作,为财务管理柔性评价奠定了基础。     

企业并购初期的组织结构设计与成本优化

本文研究了过渡结构在企业并购中的应用,并证明了过渡结构的应用使得企业并购体管理幅度隐性增大,从而降低组织成本。     

基于递阶生产系统投入产出模型的企业生产经营决策优化应用的研究

本文基于递阶生产系统投入产出模型,以某锦纶厂为应用背景,在建立销售结构优化模型的基础上,对企业销售结构优化、企业内部潜力挖掘和产品定价等几个方面的经营决策优化问题进行了探讨,并进行了成功的实践。     

基于期间收益的企业并购谈判模型

借鉴协商和讨价还价理论,建立了企业并购谈判模型,利用博弈论工具对模型进行了求 解. 同时,针对一些并购谈判需要时间较长的现实情况,对目标企业在谈判期间具有期间收益 的并购谈判模型进行了一些初步的研究.     

技术创新战略投资的实物期权方法综述

实物期权方法比传统的折现现金流法(DCF) 更适合用来分析不确定环境下的投资决策 问题,但仅凭实物期权方法不能对不完全竞争环境下的企业战略投资问题进行准确分析和估 价. 根据是否考虑不完全竞争环境下企业投资决策间的战略互动关系,实物期权方法被分为一 般实物期权方法和战略实物期权方法. 侧重于战略实物期权方法,对企业技术创新投资的实物 期权理论模型及方法进行综述,并指出了对这类问题进一步研究的方向.     

Toyota in the 21st century in Asian countries: Information exchange system and best mix

This article explains the general nature of the business style of Toyota management strategy, its specific performance, and its development in China. It is quite difficult to find specific revolutionary policies and unusual strategies for the success of the Toyota Motor Corporation.     

A Mathematical Model of Service Failure and Recovery Strategies*

Understanding the nature of service failures and their impact on customer responses and designing cost‐effective recovery strategies have been recognized as important issues by both service researchers and practitioners. We first propose a conceptual framework of service failure and recovery strategies. We then transform it into a mathematical model to assist managers in deciding on appropriate resource allocations for outcome and process recovery strategies based on customer risk profiles and the firm's cost structures. Based on this mathematical model we derive optimal recovery strategies, conduct sensitivity analyses of the optimal solutions for different model parameters, and illustrate them through numerical examples. We conclude with a discussion of managerial implications and directions for future research.     

Wavelet‐Based Testing for Serial Correlation of Unknown Form in Panel Models

Wavelet analysis is a new mathematical method developed as a unified field of science over the last decade or so. As a spatially adaptive analytic tool, wavelets are useful for capturing serial correlation where the spectrum has peaks or kinks, as can arise from persistent dependence, seasonality, and other kinds of periodicity. This paper proposes a new class of generally applicable wavelet‐based tests for serial correlation of unknown form in the estimated residuals of a panel regression model, where error components can be one‐way or two‐way, individual and time effects can be fixed or random, and regressors may contain lagged dependent variables or deterministic/stochastic trending variables. Our tests are applicable to unbalanced heterogenous panel data. They have a convenient null limit N(0,1) distribution. No formulation of an alternative model is required, and our tests are consistent against serial correlation of unknown form even in the presence of substantial inhomogeneity in serial correlation across individuals. This is in contrast to existing serial correlation tests for panel models, which ignore inhomogeneity in serial correlation across individuals by assuming a common alternative, and thus have no power against the alternatives where the average of serial correlations among individuals is close to zero. We propose and justify a data‐driven method to choose the smoothing parameter—the finest scale in wavelet spectral estimation, making the tests completely operational in practice. The data‐driven finest scale automatically converges to zero under the null hypothesis of no serial correlation and diverges to infinity as the sample size increases under the alternative, ensuring the consistency of our tests. Simulation shows that our tests perform well in small and finite samples relative to some existing tests.     

基于微观结构视角的非经常性损益噪音研究

以证监会对非经常性损益的修订为事件,选择深市2004年度披露的非经常性损益的上市公司为研究对象,采用非经常性损益的定价误差方法以及加入虚拟变量的日内收益和隔夜收益的回归分析方法,并将非经常性损益区分为非经常性收益和非经常性损失后分组进行分析,从市场微观结构角度对非经常性收益和损失的信息噪音进行研究。研究结果表明,事件日后的非经常性损失定价误差明显降低,其信息噪音降低,而非经常性收益只有最大值组的定价误差明显降低。日间收益和隔夜收益的回归结果表明,由于非经常性收益和损失造成暂时偏离价值的价格会迅速得到修正,非经常性收益和损失的信息噪音降低。证监会对非经常性损益的修订在一定程度上降低了其信息噪音,政策实施后具有一定的效果。