【作者】 孙晓琳；

【导师】 田也壮； 王文彬；

【作者基本信息】 哈尔滨工业大学 ， 企业管理， 2010， 博士

【摘要】 自2007年末,以美国次贷危机引发的全球性金融危机正横扫世界,各国均在寻求医治这一危机的良方,各种救市方案、救援措施纷纷出台。虽然财务危机预警源于公司财务的实证研究领域,但从目前研究成果和应用需求的发展来看,财务危机预警是一种复杂、综合性的动态管理过程,其理论和实践涉及到预警理论、过程控制理论、动态信息技术、数理工具以及模拟技术等多学科知识。企业的财务状况具有持续性特点和累积效应,公司出现财务危机是有一个逐渐演变的过程;并且企业财务状况暂时的偏离正常值不应被归为危机公司。而传统的静态财务危机预警模型以单期截面数据预测财务危机,忽略了历史的时间序列数据对结果的影响,以及建模方法中的固有缺陷致使其难于在公司财务管理中推广应用。因此,我们有必要对财务危机预警的机理作深入的研究,建立适用于实际情况的预警模型,就企业的财务状况进行跟踪、监控和预警,及早发现财务危机信号,预测企业可能的财务失败,避免和减少对企业的破坏程度。本文从上市公司发生财务危机是财务状况、经营质量和治理效能共同作用的结果入手,归纳出包括财务指标,经营质量指标和治理指标三个方面的综合预警指标体系。并且考察公司绩效的动态过程,侦测出公司由好向坏转变,由轻至重的两个阈值分割点,由此建立了基于Kalman滤波的财务危机动态预警模型,观测到公司财务状况由健康运营、到财务风险、财务危机、直到企业破产等一系列企业失败的过程演变的轨迹。同时,本文根据公司治理指标的静态性特点,建立了基于BP神经网络的财务危机预警模型,从而在时间序列和横截面两个维度完善了对财务危机预警问题的研究,突显出纳入了公司治理指标的预警模型的优越性,检验了“公司治理因素变量能够帮助企业预测财务危机”这样一个问题。本文采用大样本、多变量、长时间序列的数据进行实证研究,两种预警模型都显示出了良好的稳健性和预测能力。首先,上市公司财务危机动态预警理论的研究。通过分析企业发生财务危机的动态性特征,梳理出动态预警的相关理论,指出财务状况恶化是导致财务危机的直接动因,经营不善是引发财务危机的内在基础,而公司治理的弱化是发生财务危机的内生动力。由此从财务状况、经营质量和公司治理三个维度分析对上市公司发生财务危机的影响,初步选取了30个财务指标和11个公司治理指标,建立了基于上市公司这一微观层面的财务危机动态预警综合指标体系,着重解决了建立财务危机预警指标体系的理论依据和完整性的问题。其次,上市公司财务危机预警数据的分析与检验。本文从CCER数据库中选取264家危机公司和健康公司作为研究样本,包括41个综合预警指标,时间跨度为1994-2008年,合计2874个年度观测数据进行截面和时序的分析与检验。通过对样本数据的描述性统计和噪声预处理、非参数检验、相关性分析、标准正态性转换和全局主成分分析,以此克服了国内外截面分析中存在的逻辑问题,简明扼要地把握了定量数据的持续性变化和累积效果的动态规律。然后,基于Kalman滤波构建企业财务危机动态预警模型。通过对Kalman滤波在财务危机预警中的适用性进行分析,建立了随机滤波模型,以企业连续多年的观测量作为滤波器的输入,以企业的状态或估计参数作为滤波器的输出,由计算机实现实时递推算法,通过对误差与数据间的处理把时间更新和观测更新算法联系在一起,不断更正模型参数,组建出最优滤波方程。本研究应用MATLAB编写M程序,实现财务危机预警的Kalman滤波器的计算及可视化。并应用极大似然估计对模型进行参数辨识,得出最合适的参数集。同时,根据企业绩效的动态过程,把企业的财务状况分为三个阶段,提取出公司由好向坏转变,由轻至重的两个阈值分割点。把应用滤波模型所得到的预测结果与真实状态值进行比较,从预测精度、敏感性和专一性等几个角度识别模型的检验能力。实证结果表明,基于Kalman滤波的动态预警模型具有良好的远期预测能力和动态校正的功能,是优于现有判别技术的预警模型。最后,公司治理视角下的BP神经网络的财务危机预警模型的研究。时间序列和横截面作为研究财务危机预警问题的两个客观维度,可以弥补彼此的不足。由于动态预警模型所需的时间序列数据非常大,并且公司治理数据的趋势性并不明显,但考虑到作为引发财务危机的深层次原因,由此建立了基于BP神经网络的财务危机预警模型,它由两组是否纳入公司治理变量的样本所组成。通过构建的22个输入节点、3个输出节点的13层神经网络,并且对其加以训练和仿真,通过对模型的学习和仿真,得到了纳入公司治理变量的综合模型正确率达到了99.18%的理想效果,突显出纳入了公司治理变量的预警模型的优越性和模型本身在分类识别能力上的精确性能。本文对企业财务危机预警的理论和模型方法的研究,拓广了财务危机动态预警评价的理论,是一个十分重要的决策辅助工具,能够为上市公司、证券市场和相关利益人提供理论指导和技术支持,具有重要的理论和实际意义。更多还原

【Abstract】 The global financial crisis triggered by the United States sub-prime crisis has been swep the world since the end of 2007.Countries were seeking remedy to cure this crisis and various rescue measures have been proposed. While the financial distress (FD) early-warning is from the company’s financial empirical research, the FD early-warning is a complex, integrated dynamic management process from the current research and applications development, and its theory and practice related to early-warning theory, progress control, dynamic science tools, simulation technology and many other academic knowledge.The companies’financial state is of an ongoing character and has the cumulative effect. It is a gradual process of evolution that company has a financial distress. Also a temporary deviation from the normal should not be classified as a crisis company. The traditional static financial distress early-warning model is based on one-period cross-sectional data and predicts the financial distress, which ignored the historical time series data’s the impact on the results, as well as the inherent flaws to the modeling approach resulted that the methodology is hard to promote the use of financial management. Therefore, we need research on the financial distress early-warning mechanism deeply, and develop an early-warning model attempt to the actual financial state which can track, control and early warning, also predict financial distress signals, and prevent or reduce the damage to the business.In this paper, we conclude that a listed company occurs a financial distress is the outcome of financial state, management quality and governance performance. Hence the comprehensive early warning indicator system includes financial indicators, management quality indicators and governance indicators. And we detect that the dynamic process of corporate performance, and observe the company’s changes from good to bad, from light to heavy two-point threshold. Thus we establish a financial distress dynamic early warning model based on Kalman filtering. At the same time, we established financial distress static early warning model based on BP neural network since corporate governance indicators has static characteristics. Hence we improved the financial distress early warning research from two dimensions including time-series and cross-sectional. It highlights the superiority of early-warning model including corporate governance indicators. In this empirical research we use large sample, multi-variate, time series data, and both early-warning models have shown good stability and predictability.First of all, the research on the theory of the listed companies’ financial distress early-warning indicators. Through deep analysis of the characteristics and causes of financial distress, we conclude that the deteriorating financial situation led to the financial distress is its immediate causes; its poor management is the internal basis triggered by financial distress, also the weakened corporate governance is the endogenous motivation which makes financial distress occurred. We selected 30 fiancial ratios and 11 corporate governance ratios to build a comprehensive indictor system, which can solve the theoretical basis and integrity issues of the financial distress early-warning indicator system.Second, we analyse and test the listed companies’financial distress early-waring data. We use CCER database and select 264 China listed companies as study samples. We chose 30 financial ratios from 1994 to 2008, including 2727 year-observation data to empirical study. Though descriptive statistics, noise pre-processing, non-parametric tests, correlation analysis, the standard normality conversion and principal component analysis, it can overcome the logic problems of.the existing domestic and international cross-section analysis.Then, we build a financial distress dynamic early-warning model based on Kalman filtering. Through its applicability for financial distress early-warning, stochastic filter model is established, which is real-time recursive algorithm by computer. The companies’observation data is as filter input and the companies’real state and parameters is as filter output. It links the time series update and observation update algorithm, and correct the model parameters, finanly formate the optimal filtering equation. In this study, MATLAB M program is used to achieve financial distress dynamic early warning model for Kalman filtering calculation and visualization. Maximum likelihood estimation is used for parameter identification. Meanwhile, according to the dynamic process of companies’performance, the financial state of a company is divided into three phases, we extract the company’s transformation from good to bad, from light to heavy two-point thresholds. Comparing the real state and prediction, we identify the model’ability though prediction accuracy, sensitivity and specificity. The empirical results showed that the dynamics early-warning model based on Kalman filtering has long-term early warning capacities and dynamic correction function, which is superior to the existing financial distress early-warning models.Finally, we research on financial distress static early warning model based on BP neural network. Time-series and cross-sectional studies are as the two objective dimensions on research financial distress early warning, which can make up for each other’s deficiencies. As the dynamic early-warning model require large time series data, and the trend of corporate governance data is not obvious, but as a deep reason trigger the financial distress, thus we establish a BP neural network model to predict financial distress early-warning, which consists of two sets of variables, whether include corporate governance measurement system. By building the 22 input nodes, 3 output nodes of the 13-layer neural network, its training and simulation, it concluded that the model’correct rate of 99.18% including corporate governance variables, which highlights the superiority of having corporate governance variables and model itself on the ability to identify the exact static performance.更多还原