我国汽车制造业上市公司财务风险评价

汽车制造业是我国国民经济的支柱性产业,其财务风险关乎着公司的可持续发展。本文选取32个汽车制造业上市公司为样本,从五个方面共15项财务指标对2017年汽车制造业上市公司的财务风险进行评价。利用因子分析法识别影响我国汽车制造公司财务风险的主要因素,构建财务风险评价模型,根据因子得分模型进行评分与排序;再通过聚类分析将32家公司按照综合财务风险的大小分为五大类。针对存在的各种风险问题,提出策略,以期使汽车制造业有更长远的发展。     

财务困境上市公司财务报告舞弊预警模型研究

本文选择2004～2012年间被证监会处罚的48家处于财务困境中的财务报告舞弊上市公司和48家正常公司为样本,以舞弊前三年到舞弊年度为研究区间,利用配对样本T检验和Wilcoxon符号秩检验在选取的31个财务指标中筛选存在显著差异的指标,通过Logistic回归技术构建基于财务困境的上市公司财务报告舞弊预警模型.模型对舞弊公司预警的有效性高达91.7％,综合有效性达到88.5％,取得了显著的预警效果.     

上市公司财务指标比较及预警分析——来自房地产行业的实证

本文通过对深市房地产板块上市公司2001-2003年财务指标比较,对其ST公司的财务困境可能表现出来的财务指标征兆进行了分析,从而得出如何通过单个财务指标对房地产企业财务困境进行预警,并且提出了一个简单的综合预警模型。     

引入非财务变量的上市公司财务困境预警

本文选取沪深两市A股2010~2013年52家首次ST公司和52家从未被ST公司作为研究样本,引入盈利能力、股东获利能力、营运能力、发展能力、风险水平和偿债能力六个方面的财务变量,以及股权结构、公司治理、重大事项、人力资本4个方面的非财务变量,使用Fisher判别与Logistic回归两种方法建立ST前3年的财务困境预警模型。实证研究表明,加入非财务指标可以提高财务困境预警的准确率,且Logistic回归方法建立的预警模型的预测效果更好。     

基于因子分析的纺织行业上市公司财务绩效评价研究

本文利用SPSS16.0计算分析软件,采用因子分析法,以纺织行业上市公司2010年的年报数据为样本,选取了盈利能力、偿债能力、营运能力、发展能力和股东获利能力等方面的财务指标,通过建立财务绩效评价模型,得出了纺织行业上市公司的财务绩效的综合评价得分,并对企业改进其财务绩效提出了建议。     

EVA视角的上市公司财务预警实证研究

本文以我国A股上市公司为研究对象,选取了65家处于财务困境的公司和75家财务正常的公司为样本,在传统财务指标的基础上引入EVA,运用主成分分析法构建了财务困境模型。结果表明,相对于传统的财务指标,EVA对企业的财务困境具有较强的判别能力,模型具有超前的预测能力。     

财务困境预警模型分析及比较

财务预警实证研究是以企业现有财务指标为基础，建立数学模型来预测企业陷入财务困境的可能性，为企业内外利益相关者了解、预测企业财务状况提供一种简便有效的分析工具。相应的，财务预警模型就是利用企业一系列财务指标和非财务指标来识别企业财务状况的有效工具。     

基于费米分布函数的财务预警模型实证研究——以我国服务性行业上市公司为例

通过引入一种具有统计学原理的费米分布模型对企业财务状况进行了预警实证研究,结果发现:当赋予费米分布模型在财务预警领域的物理内涵后,一定程度上能够对输入的企业综合财务得分值E进行准确预测,其准确性主要决定于研究样本费米面E_F的选择。E_F越接近样本的实际值,则预警准确率会显著提高;同时,获得具有正、负相关性的E值对该模型的财务预警准确率至关重要。采用因子分析法和正、负相关性财务指标算术和的方法,分别对获取的E值输入费米分布模型进行研究,表明采用因子分析法所得的E值由于考虑的企业财务指标过多过杂,对因子分析法所建模型和费米分布模型的预警准确率均产生了一定干扰。相比之下,采用正、负相关性财务指标算术和得到的E值能够有效提高费米分布模型的二进制预警准确率。     

基于Logit模型的A股上市公司财务困境预警研究

本文采用2008到2010年的财务数据,选取有代表性的财务指标,对A股上市公司中面临财务困境的47家公司和没有面临财务困境的47家公司利用Logit模型进行估计和预测分析。分析结果表明,Logit模型的预测是有效的,具有良好的精度,是一种有效的财务困境分析和预测工具。     

国有上市公司财务危机综合预警实证研究

本文采用多分类方法将样本公司分为财务危机公司、财务状况不稳定公司和财务健康公司,再利用2005~2010年的国有上市公司数据,运用主成分分析、因子分析和多项Logistic回归构建了综合财务危机预警模型。模型综合考量了财务指标、公司治理和金融生态环境三方面变量对于公司财务风险的影响,使综合预警取得了满意的预测精度。     

The recurrence of financial distress: A survival analysis

Companies often suffer periods of financial distress before filing for bankruptcy. Unlike one-off bankruptcies, financial distress can occur repeatedly within the same individual firm. This paper is focused on the recurrence of financial distress and studies the Chinese stock market, where Special Treatment – an official indicator of financial distress – can be repeatedly applied to a listed company. We employ a stratified hazard model to predict the probability of subsequent distress with variables, including duration dependency, event-based factors, institutional variables, financial ratios, market-based variables and macroeconomic conditions. Our empirical results show that accounting and market-based variables have limited power in predicting the recurrence of distress, whereas the duration of recovery, restructuring events and their interaction terms with the accounting and macroeconomic factors affect the recurrent risk significantly. Tested on out-of-time samples, our proposed hazard models show a robust performance in the prediction of recurrent risk over time.     

财务困境预测中BP神经网络模型应用综述

金融危机席卷全球。处于金融市场之中的企业随时面,临着陷入财务困境的可能,财务困境预测模型的建立可以使公司提前预测到困境的发生,从而及早避免投资损失。随着信息技术的发展,人工神经网络预测模型开始兴起,本文重点介绍了BP神经网络模型在财务困境预测中的应用情况,并将BP神经网络模型与传统统计方法进行了比较分析。     

公司财务风险评价指标体系的确立

本文运用AHP法确立了公司财务风险评价指标体系。该指标体系不仅包含了财务性指标,而且引入了非财务性指标,使该指标体系能够在更广泛的范围上反映公司财务的风险状况。     

TrAffic LIght system for systemic Stress: TALIS3

For the purposes of financial stability, identifying financial institutions that, when in distress, could have a significant adverse impact on financial markets is important. A TrAffic LIght System for Systemic Stress (TALIS-cube) is proposed that provides a comprehensive color-based classification for grouping companies according to both the stress reaction level of the system when the company is in distress and the company’s stress level. TALIS³ can integrate multiple signals from the interaction between different risk metrics. Starting from specific risk indicators, companies are classified by combining two loss functions—one for the system and one for each company—evaluated over time and as a cross section. An aggregated index is also obtained from the color-based classification of companies. TALIS³ can be used to enhance the performance and robustness of existing systemic risk measures. An empirical analysis of the U.S. market is also provided.     

Nowcasting and forecasting global financial sector stress and credit market dislocation

We introduce a new international model for the systematic distress risk of financial institutions from the US, the European Union, and the Asia-Pacific region. Our proposed dynamic factor model can be represented as a nonlinear, non-Gaussian state space model with parameters that we estimate using Monte Carlo maximum likelihood methods. We construct measures of global financial sector risk and of credit market dislocation, where credit market dislocation is defined as a significant and persistent decoupling of the credit risk cycle from macro-financial fundamentals in one or more regions. We show that, in the past, such decoupling has preceded episodes of systemic financial distress. Our new measure provides a risk-based indicator of credit conditions, and as such, complements earlier quantity-based indicators from the literature. In an extensive comparison with such quantity-based systemic risk indicators, we find that the behaviour of the new indicator is competitive with that of the best quantity-based indicators.     

上市公司财务危机预警“Z”值区域研究与分析

本文以上市公司作为研究对象,将公司因财务状况异常而被特别处理(ST)作为企业陷入财务困境的标志,利用奥特曼的Z记分模型作多元判别分析,测试符合我国上市公司实际情况的Z值,并将其作为我国上市公司财务危机预警的指标值。实证结果显示,采用多元判别分析可以得到判别财务危机公司与非财务危机公司的Z值区域,并且可以保证较高的判别精确度。同时也发现,相对于主营业务收入指标,现金流量指标为更好的警兆指标。     

从现金流的角度预测上市公司的财务困境

在众多企业都热衷于通过各种手段快速扩张的今天,企业往往容易忽视其财务上的风险.因此企业是否有进入财务困境的风险,怎样预测企业的财务困境,已经成为投资者、债权人等相关利益主体越来越关注的问题.本文从投资者最易获得信息之一现金流量表的角度,来分析企业在进入财务困境之前现金流量表相关项目所可能出现的异常情况,以帮助投资者更好的把握企业的财务状况。     

基于经济学的财务治理目标建构

本文在对现有理论观点及其研究逻辑予以回顾的基础上,提出公司财务治理目标理论研究的新范式:企业理论是财务治理目标研究的理论渊源,财务治理本质理论以及财务管理目标理论,是财务治理目标研究的直接依据;然后论证了财务治理的目标是优化公司财务治理机制以实现代理成本的最小化,并将实现该目标的条件予以正式化;最后提出了在该目标模式下公司财务治理机制选择与优化的基本逻辑。     

Does a central clearing counterparty reduce liquidity needs?

This study investigates whether and how central clearing influences the overall liquidity needs in a network of financial obligations. Utilizing the approach of flow network theory, we show that the effect of adding a central clearing counterparty (CCP) is decomposed into two effects: central routing, and central netting effects. Each effect can produce different liquidity needs according to different liquidity scenarios. The analysis indicates that adding a CCP in times of financial distress successfully reduces the overall liquidity needs if and only if the netting efficiency of the CCP is sufficiently high. Furthermore, once the economy is no longer in financial distress, higher netting efficiency of the CCP could conversely increase the overall liquidity needs. The results have implications for the effectiveness of CCPs in mitigating systemic risk in times of financial distress, and their operating costs once the distress has passed.     

The dynamic financial distress prediction method of EBW-VSTW-SVM

Financial distress prediction (FDP) takes important role in corporate financial risk management. Most of former researches in this field tried to construct effective static FDP (SFDP) models that are difficult to be embedded into enterprise information systems, because they are based on horizontal data-sets collected outside the modelling enterprise by defining the financial distress as the absolute conditions such as bankruptcy or insolvency. This paper attempts to propose an approach for dynamic evaluation and prediction of financial distress based on the entropy-based weighting (EBW), the support vector machine (SVM) and an enterprise’s vertical sliding time window (VSTW). The dynamic FDP (DFDP) method is named EBW-VSTW-SVM, which keeps updating the FDP model dynamically with time goes on and only needs the historic financial data of the modelling enterprise itself and thus is easier to be embedded into enterprise information systems. The DFDP method of EBW-VSTW-SVM consists of four steps, namely evaluation of vertical relative financial distress (VRFD) based on EBW, construction of training data-set for DFDP modelling according to VSTW, training of DFDP model based on SVM and DFDP for the future time point. We carry out case studies for two listed pharmaceutical companies and experimental analysis for some other companies to simulate the sliding of enterprise vertical time window. The results indicated that the proposed approach was feasible and efficient to help managers improve corporate financial management.