基于Logit模型的战略性新兴产业企业财务风险影响因素研究

进入21世纪以来,我国不断优化产业结构,扶持和培育引领国民经济发展的骨干企业。为占领世界经济发展的战略高地,我国制定了战略性新兴产业发展规划。在经济发展的新常态下,我国一些战略性新兴企业出现了严重的库存过剩,以至于企业陷入财务危机。为揭示企业发生财务危机的原因,本文从沪深两市选取757家上市公司,以10个指标为解释变量,建立Logit模型进行研究。同时,本文以Logit模型的回归结果为依据,重点分析、比较了国有企业和民营企业在面对财务危机时的财务举措。实证结果发现,我国战略性新兴企业的固定资产比率和资产负债率对其财务风险有着显著影响,而企业的经营和盈利能力并不是导致其财务危机的主要因素。此外,在面对财务危机时,无论国有企业还是民营营业都会显著地降低资产负债率,国有企业还会显著地降低其固定资产比率,而提高自身的经营和盈利能力并不是战略性新兴企业走出财务困境的首要选择。     

中国外汇风险的识别和动态预警研究

本文采用极值分位数的估计方法和动态Logit预警模型来识别和预测中国的外汇风险。其中用极值分位数的估计方法替代传统的偏离均值若干倍标准差的方法来识别中国的外汇风险,并以此为基础,选取代表宏观经济、金融体系和国外冲击三方面的24个指标构建中国外汇风险动态预警模型。所采用的动态Logit预警模型不仅考虑了基本面对外汇风险的影响,而且加入了动态性因素,即考虑了外汇风险的自回归特性。从实证结果来看,极值分位数的估计方法较客观地识别了外汇风险,动态Logit预警模型无论在样本内还是在样本外的预测能力都比静态Logit模型有了较大的提高,我国的汇率改革没有对外汇风险产生显著影响。     

企业财务危机困境预测模型研究——主成分分析基础上的Logit模型

本文选取沪深两市在2003-2005年度被实行退市风险警示的56家上市公司以及随机选取的56家正常公司进行实证研究,尝试建立主成分分析基础上的 Logit 模型对财务危机进行预测,结果显示,主成分分析基础上的 Logit 模型能够有效预测财务困境。     

行业网络关联度及其对股市下跌风险的影响

采用格兰杰因果网络和信息溢出指数对申万一级行业指数的日收益率数据进行分析,构造了我国股市的行业网络关联度指标,使用Logit模型考察了网络关联度对股市下跌风险的影响,研究结果表明:格兰杰因果网络能够较好地对市场的系统性风险进行判断,对2007年和2015年两次股灾均有信号提示.两个网络关联度指标及有色金属行业的净对外溢出度均对股市下跌风险具有一定的预测能力,且预测能力会受到牛熊市和投资者情绪的影响.结论揭示了网络关联度的上升伴随着系统性风险的累积,从而带来股市下跌风险的增加.     

基于混合神经网络模型的国别风险评估研究

本文基于计量分析处理多指标数据的优势,结合神经网络最优化模型解的特点,构建了评估和预测国别风险的混合神经网络模型。其中相关性分析和Logit回归分析的结果显示,预测国别风险的两个重要指标是商业自由度(EBI)和外汇储备总额与外债总额之比(RED);运用两个指标进行预测,多元感知器神经网络模型的预测准确度达到了100%,与其可相互替代的概率神经网络预测模型的准确度达到了90.91%。两个模型的预测结果相互支撑和验证,在一定程度上证明了混合神经网络模型在国别风险预测上具有较强的适用性和可信性。     

基于功效系数法的超市企业财务危机预警分析

财务危机是指企业明显无力按时偿还到期的无争议的债务的困难与危机,企业管理者可以通过建立财务危机预警系统对财务危机的出现进行预测,发现企业的潜在财务危机,并根据企业生产经营状况及时进行调整,避免企业受到不必要的损失。本文在利用变异系数法确定指标权重的基础上,运用改进的功效系数法建立评价模型,对7家上市超市企业2010-2012年的财务风险状况作出分析评价。     

基于功效系数法的超市企业财务危机预警分析

财务危机是指企业明显无力按时偿还到期的无争议的债务的困难与危机,企业管理者可以通过建立财务危机预警系统对财务危机的出现进行预测,发现企业的潜在财务危机,并根据企业生产经营状况及时进行调整,避免企业受到不必要的损失。本文在利用变异系数法确定指标权重的基础上,运用改进的功效系数法建立评价模型,对7家上市超市企业2010-2012年的财务风险状况作出分析评价。     

行业与区域的企业财务危机预警模型比较研究

本文运用多元判别模型、Logit模型、主成分模型,对不同行业和地区的企业进行财务危机预警研究,进而分析判别准确率的差异.同时,对预警模型的指标选择和不同类型危机的预警判别进行了比较分析.本文研究生成的预警模型可以提供给商业银行进行风险度量使用.     

压力测试在我国寿险业中的应用研究——基于中国人寿的实证分析

本文基于Logit转换的宏观压力测试模型,定量评估分析宏观经济因素波动对我国寿险业系统稳定性的影响,并根据历史数据设计合理的压力测试情景对典型寿险公司进行冲击,探讨防范和化解寿险业系统性风险的路径.     

商业银行信用风险评估的生存分析模型及实证研究

企业发生财务危机,不能归还到期贷款是商业银行信贷资产的主要风险来源,商业银行如何构建恰当的信用风险评估模型来预测企业的财务危机,从而避免这类信用风险的出现就显得尤为重要。本文以我国上市公司为研究对象,结合杜邦分析法建立了基于生存分析的信用风险评估模型,模型对于随机选取的预测样本,其提前1年、2年和3年的预测准确率分别达到86%、72%和68%。通过与Altman模型、Ohlson模型预测结果的比较和鲁棒性检验的结果发现,该模型同时具有可以使用时间序列、无需样本配对、中远期预测能力强和高鲁棒性的特点,这些特点特别对于商业银行中长期信贷风险管理具有较高的应用价值。     

系统性金融风险文献综述:现状、发展与展望

防范风险是我国金融业的永恒主题。如何有效应对金融系统的异常波动、缓释国际市场的外部冲击、精准处置重点领域风险,仍将是我国“十四五”期间的重要任务。本文分别基于系统性金融风险的有效测度、传染溢出、驱动因素、前瞻预测,以及其与宏观经济的相互作用关系、风险调控政策与政策的有效性、监管理念发展等不同视角,对272篇国内外顶级(权威)文献进行了全面、深入的梳理与总结,并展望该领域的重点研究方向,从而为我国构建金融风险防控长效机制、完善“双循环”新格局下的金融监管体系提供参考,以牢牢守住不发生系统性金融风险的底线,推动经济社会高质量发展。     

Forecasting Corporate Bankruptcy: Optimizing the Performance of the Mixed Logit Model

In recent studies, Jones and Hensher (2004 , 2005) provide an illustration of the usefulness of advanced probability modelling in the prediction of corporate bankruptcies, insolvencies and takeovers. Mixed logit (or random parameter logit) is the most general of these models and appears to have the greatest promise in terms of underlying behavioural realism, desirable econometric properties and overall predictive performance. It suggests a number of empirical considerations relevant to harnessing the maximum potential from this new model (as well as avoiding some of the more obvious pitfalls associated with its use). Using a three-state failure model, the unconditional triangular distribution for random parameters offers the best population-level predictive performance on a hold-out sample. Further, the optimal performance for a mixed logit model arises when a weighted exogenous sample maximum likelihood (WESML) technique is applied in model estimation. Finally, we suggest an approach for testing the stability of mixed logit models by re-estimating a selected model using varying numbers of Halton intelligent draws. Our results have broad application to users seeking to apply more accurate and reliable forecasting methodologies to explain and predict sources of firm financial distress better.     

基于房地产市场的我国系统性金融风险测度与预警研究

本文阐释了基于房地产市场的系统性金融风险形成机制,据此建立了分阶段、跨部门的房地产市场的系统性金融风险网络模型,并运用2006-2017年16家上市银行数据,分析和测度了我国房价大幅下跌所引发的系统性金融风险水平和结构,构建了基于房地产市场的系统性金融风险预警指标并进行测算。研究发现:在房价下跌30%的压力情景下,我国金融体系的潜在总损失总体呈级数式上升,年均增长22.70%;基于房地产市场的系统性金融风险值(SR)呈现先上升后波动下降的总体趋势;系统性金融风险(SR)的脆弱性指标(FLI)整体呈现波浪式振荡变化,且与房地产贷款/权益整体呈反向变动,系统性金融风险(SR)的传染性指标(CTI)在2012-2017年呈持续下降趋势,且与金融市场压力指数、金融机构间资产占总资产比重呈现出高度的一致性变化趋势。最后,基于房地产市场的系统性金融风险预警指标(SRWI)值呈收敛式振荡走势,表明基于房地产市场的系统性金融风险总体可控且呈收敛式下降。     

中国金融部门间系统性风险溢出的监测预警研究——基于下行和上行ΔCoES指标的实现与优化

为了对我国金融部门间的系统性风险溢出进行实时监测和有效预警,本文基于Adrian and Brunnermeier (2016) 的CoES指标构想,在左尾视角的基础上进一步引入右尾视角,构建下行和上行ΔCoES分别作为系统性风险的同期度量指标和前瞻预警指标,并提出了更为有效合理且同时适用于下行和上行ΔCoES的计算方法。本文一方面采用下行和上行ΔCoES对我国银行、证券、保险三个金融部门间的系统性风险溢出进行监测预警研究,另一方面还基于我国的经验数据检验上行和下行ΔCoES的性质。研究结果显示,我国金融部门间具有显著的系统性风险溢出效应,且三个部门间的风险溢出存在非对称性,银行部门是系统性风险的主要发送者,证券部门是系统性风险的主要接收者;三个部门两两间的风险溢出水平表现出明显的协同性和周期性,且上行的风险溢出水平高于下行。同时,基于我国的经验数据发现,上行ΔCoES对下行ΔCoES具有显著的先导性、前瞻性,上行ΔCoES可以作为系统性风险的前瞻预警指标。此外,下行ΔCoES能够引领ΔCoVaR和基于MES估计方法计算的短期ΔCoES指标,表明本文构建的下行ΔCoES实时性更强,更适合作为系统性风险的实时监测指标。     

Predicting Corporate Bankruptcy: An Evaluation of Alternative Statistical Frameworks

Corporate bankruptcy prediction has attracted significant research attention from business academics, regulators and financial economists over the past five decades. However, much of this literature has relied on quite simplistic classifiers such as logistic regression and linear discriminant analysis (LDA). Based on a large sample of US corporate bankruptcies, we examine the predictive performance of 16 classifiers, ranging from the most restrictive classifiers (such as logit, probit and linear discriminant analysis) to more advanced techniques such as neural networks, support vector machines (SVMs) and “new age” statistical learning models including generalised boosting, AdaBoost and random forests. Consistent with the findings of Jones et al. ( 2015 ), we show that quite simple classifiers such as logit and LDA perform reasonably well in bankruptcy prediction. However, we recommend the use of “new age” classifiers in corporate bankruptcy modelling because: (1) they predict significantly better than all other classifiers on both the cross‐sectional and longitudinal test samples; (2) the models may have considerable practical appeal because they are relatively easy to estimate and implement (for instance, they require minimal researcher intervention for data preparation, variable selection and model architecture specification); and (3) while the underlying model structures can be very complex, we demonstrate that “new age” classifiers have a reasonably good level of interpretability through such metrics as relative variable importances (RVIs).     

An Early Warning System of Financial Distress Using Multinomial Logit Models and a Bootstrapping Approach

This study adopts multinomial logit models to separately measure the extent to which financial ratios and corporate governance signal the likelihood of "slight distress events" and "reorganization and bankruptcy." The results show that corporate governance variables are closely related to the occurrence of "slight distress events." The estimated misclassification costs of the 1,000 resamples generated through bootstrapping procedures are statistically lower for a model that makes use of corporate governance (CG model) than one without corporate governance (non-CG model) at all cutoff points in 2009, and cutoff points from 0.11 to 0.27 in 2008. Since corporate governance is incrementally useful in predicting financial distress, the CG model's predictive ability improves as two corporate governance factors are considered: ownership ratio of insiders and pledge-ownership ratio of insiders.     

Market Discipline and the Use of Stock Market Data to Predict Bank Financial Distress

We assess the extent to which stock market information can be used to estimate leading indicators of bank financial distress. We specify a logit early warning model, designed for European banks, which tests if market based indicators add predictive value to models relying on accounting data. We also study the robustness of the link between market information and financial downgrading in the light of the safety net and asymmetric information hypotheses. Some of our results support the use of market-related indicators. Other results show that the accuracy of the predictive power depends on the extent to which bank liabilities are market traded.      

基于房地产市场的我国系统性金融风险测度与预警研究

本文阐释了基于房地产市场的系统性金融风险形成机制,据此建立了分阶段、跨部门的房地产市场的系统性金融风险网络模型,并运用2006-2017年16家上市银行数据,分析和测度了我国房价大幅下跌所引发的系统性金融风险水平和结构,构建了基于房地产市场的系统性金融风险预警指标并进行测算。研究发现:在房价下跌30%的压力情景下,我国金融体系的潜在总损失总体呈级数式上升,年均增长22.70%;基于房地产市场的系统性金融风险值(SR)呈现先上升后波动下降的总体趋势;系统性金融风险(SR)的脆弱性指标(FLI)整体呈现波浪式振荡变化,且与房地产贷款/权益整体呈反向变动,系统性金融风险(SR)的传染性指标(CTI)在2012-2017年呈持续下降趋势,且与金融市场压力指数、金融机构间资产占总资产比重呈现出高度的一致性变化趋势。最后,基于房地产市场的系统性金融风险预警指标(SRWI)值呈收敛式振荡走势,表明基于房地产市场的系统性金融风险总体可控且呈收敛式下降。     

Investment opportunities and bankruptcy prediction

A firm's mix of growth options and assets in place is an important determinant of its optimal default strategy. Our simple model shows that shareholders of a firm with valuable investment opportunities would be able/willing to wait longer before defaulting on their contractual debt obligations than shareholders of an otherwise identical firm without such opportunities. More importantly, we show empirically using a dataset of recent corporate bankruptcies that measures of investment opportunities are significantly related to the likelihood of bankruptcy. Augmenting existing bankruptcy prediction models by these measures improves their out-of-sample forecasting ability.     

Impaired bank health and default risk

Empirical studies in corporate finance have long been focused on the role of banks in reducing the costs of financial distress. The environment and events in Japan provide a “natural experiment” that allows such empirical studies. The number of bankruptcies steadily increased throughout the 1990s, and peaked in 2000. During this period, Japan's banking sector, in contrast, faced considerable problems regarding the disposal of their bad loans. The purpose of this paper is to investigate how various measures of bank health and how defaults of major trading partners affected the probability of bankruptcy among medium-size firms in Japan. Using probit models, we examine the causes of bankruptcy for unlisted Japanese companies in the late 1990s and early 2000s. We find that several measures of bank-specific financial health have had significant impacts on a borrower's probability of bankruptcy, even when observable characteristics relating to these borrower's financial variables are controlled. In particular, a close bank–firm relationship—which usually reduces the probability of bankruptcy—exacerbates the impacts of a financial crisis, which substantially damages other bank health measures as well.