The comparison of enterprise bankruptcy forecasting method

The enterprise bankruptcy forecasting is vital to manage credit risk, which can be solved through classifying method. There are three typical classifying methods to forecast enterprise bankruptcy: the statistics method, the Artificial Neural Network method and the kernel-based learning method. The paper introduces the first two methods briefly, and then introduces Support Vector Machine (SVM) of the kernel-based learning method, and lastly compares the bankruptcy forecasting accuracies of the three methods by building the corresponding models with the data of China's stock exchange data. From the positive analysis, we can draw a conclusion that the SVM method has a higher adaptability and precision to forecast enterprise bankruptcy.     

Variable selection in proportional hazards cure model with time-varying covariates,application to US bank failures*

From a survival analysis perspective, bank failure data are often characterized by small default rates and heavy censoring. This empirical evidence can be explained by the existence of a subpopulation of banks likely immune from bankruptcy. In this regard, we use a mixture cure model to separate the factors with an influence on the susceptibility to default from the ones affecting the survival time of susceptible banks. In this paper, we extend a semi-parametric proportional hazards cure model to time-varying covariates and we propose a variable selection technique based on its penalized likelihood. By means of a simulation study, we show how this technique performs reasonably well. Finally, we illustrate an application to commercial bank failures in the United States over the period 2006–2016.     

Naive method to test the convergence of simulation and its applications in the computation of bankruptcy probability

We analyse a naive method using sample mean and sample variance to test the convergence of simulation. We find this method is valid for identically, independently distributed samples, as well as correlated samples with correlation disappearing in long period. Our simulation results on the approximation to bankruptcy probability (BP) show the naive method compares well with the Half-Width, Geweke and CUSUM methods in terms of accuracy and time cost. There are clear evidences of variance reduction from tail-distribution sampling for all convergence test methods when the true BP is very low.     

中国商业银行效率：两种测度方法的比较

为了准确反映中国商业银行的效率水平,了解中国商业银行的效率现状,并对各商业银行之间、各类商业银行之间、中国商业银行与国际大银行之间的效率进行比较评价,有必要对中国商业银行效率进行准确的测度。分别从经济（财务）和技术（生产）的角度,采用财务指标分析法和数据包络分析DEA法来测度和评价中国商业银行的效率及排名,并对两种测度方法进行比较分析。     

金融冲击与中央银行的流动性支持政策研究

本文在DSGE框架下引入金融冲击,比较完整地刻画了包含货币政策、宏观审慎政策以及流动性支持政策的央行“政策工具箱”,并对中央银行的流动性支持政策在不同外生冲击下的经济金融稳定效应进行了系统考察。本文分析得到了三个基本结论:一是在各种典型的金融冲击下,中央银行的流动性支持政策均具有良好的宏观稳定效应;二是从政策实施的角度,中央银行的流动性支持政策可考虑盯住银行存款的变动,并在政策操作上体现“及时反应、力度合理”的原则;三是从社会福利增进的角度,中央银行流动性支持政策的效果受到冲击来源的影响,总体上应对金融冲击的效果较好。分析结果初步显示了流动性支持政策作为中央银行“政策工具箱”一种辅助性金融稳定工具的可行性、针对性和有效性。     

国有银行金融交易结构比较与其金融风险状态分析

在传统体制下,我国国有银行是一种行政机关及经济组织的捏合体,垄断信用、统存统贷,并履行结算支付职能,充当“公共簿记”。在社会经济活动有限的债权债务关系中,银行的最终债权人与最终债务人都是国家,银行经营成果及收支最后均由财政统一包揽,事实上并不存在真正...     

Dynamic financial distress prediction based on Kalman filtering

In models for predicting financial distress, ranging from traditional statistical models to artificial intelligence models, scholars have primarily paid attention to improving predictive accuracy as well as the progressivism and intellectualization of the prognostic methods. However, the extant models use static or short-term data rather than time-series data to draw inferences on future financial distress. If financial distress occurs at the end of a progressive process, then omitting time series of historical financial ratios from the analysis ignores the cumulative effect of previous financial ratios on the current consequences. This study incorporated the cumulative characteristics of financial distress by using the characteristics of a state space model that is able to perform long-term forecasts to dynamically predict an enterprise's financial distress. Kalman filtering is used to estimate the model parameters. Thus, the model constructed in this paper is a dynamic financial prediction model that has the benefit of forecasting over the long term. Additionally, current data are used to forecast the future annual financial position and to judge whether the establishment will be in financial distress.     

引入破产时间的EVA企业价值评估模型研究

现行基于EVA理论的企业价值评估模型存在较大缺陷,如永续经营假设以及未充分考虑企业资产规模等。对EVA理论的企业永续经营假设提出质疑,提出以企业预期破产时间为企业终止运营期限的观点来测算企业预期EVA现值,从而为解决企业永续经营假设与现实企业周期运营的矛盾提供了一种思路。首先基于企业资产因素采取单因子高斯Copula模型预测企业破产概率,然后通过蒙特卡洛模拟计算企业预期破产时间,以使不同企业资产规模因素对企业价值的影响可以更为有效地体现在企业价值评估模型中。     

金融市场收益率方向预测模型研究———基于文本大数据方法

金融市场的发展关系着一国的经济命脉,而股票市场作为金融市场的重要组成部分,对其收益率的研究也一直都是学术界的热点。财经新闻常被认为蕴含着丰富的信息,其中所包含的情感信息作为影响投资者投资决策的重要因素之一,对股票收益率也具有一定的影响。故本文构建了适用于金融投资领域的财经新闻情感词典来对财经新闻进行文本分析,同时构造了新的预测模型:将财经新闻文本中所含的情感量化为情绪指数并与时变密度函数相结合,得到时变加权密度模型。并在此基础上以模型评分为权重组合多个预测模型构建出评分加权模型用于股票收益率预测。结果显示,加入情绪指数能有效提高模型预测能力,而评分加权模型的预测能力则在此基础上更进一步,在准确率以及评分规则上基本达到双重最优。     

基于模糊分析的中国商业银行战略联盟伙伴选择研究

随着金融业务的国际化，战略联盟已经成为银行提高自身竞争力的一个有效手段。研究确定商业银行联盟伙伴选择的原则与标准，根据这些标准，运用模糊聚类分析法对银行潜在的联盟伙伴的选择进行分析，使商业银行有针对性的与一些联盟伙伴发展合作伙伴关系，减少交易成本和搜寻成本，从而提高银行的运作效率。     

城市经济发展与金融支持的关系研究——以陕西省渭南市为例

城市发展联系着产业发展的微观面与国家战略的宏观面,是经济增长的中观桥梁。采用对数平均迪氏指数法分析渭南银行贷款对城市经济的结构化效率,并将其与产业结构联系起来,搭建起金融支持与实体经济发展之间的桥梁。基于以上认识,研究以银行贷款为主要形式的渭南金融资源是否推动了渭南市经济的发展。同时,研究渭南金融资源是通过何种途径支持渭南市经济发展的问题。研究结果显示,渭南市银行业贷款投放所产生的活动效应对GDP变动总效应的贡献最大,效率效应次之,结构效应则对GDP变动效应作用不明显。     

Variable Selection for Support Vector Machines

Consider using values of variables X ₁, X ₂,…, X _p to classify entities into one of two classes. Kernel-based procedures such as support vector machines (SVMs) are well suited for this task. In general, the classification accuracy of SVMs can be substantially improved if instead of all p candidate variables, a smaller subset of (say m) variables is used. A new two-step approach to variable selection for SVMs is therefore proposed: best variable subsets of size k = 1,2,…, p are first identified, and then a new data-dependent criterion is used to determine a value for m. The new approach is evaluated in a Monte Carlo simulation study, and on a sample of data sets.     

金融核算疑难问题辨析

自１９９３年联合国重新修订、颁布《国民经济核算体系（ＳＮＡ）》以来,理论界对于金融核算的有关方法论问题普遍有了较为深入的认识。但是,由于金融核算本身在理论和实践上的复杂性,仍然存在若干疑难问题有待澄清和解决。本文拟在文献［４］有关研究的基础上,进一步...     

递归预测器神经网络在中国金融市场的实证研究

金融时间序列预测是金融理论领域的研究热点之一。以金融市场中普遍存在的弱混沌为基础,对递归预测器神经网络在中国金融市场的预测应用进行实证研究。在网络训练上,提出用遗传算法优化网络的阈值、权值以及激发函数的幅值和斜率。对国内股票、期货和黄金市场中几个有代表性的品种进行实证检验,计算了预测均方根误差（RMSE）和预测精度（PA）,并和两种典型的神经网络预测模型——BP神经网络、径向基函数神经网络做了比较,结果表明该模型有较好的预测效果。     

银行信贷对经济结构影响的一般均衡效应分析：厦门案例

 本文研究金融结构变化对宏观经济结构和产业结构的影响，并以厦门为例，以银行信贷规模为切入点，通过研究银行信贷及其产业投向的变动对于厦门市经济发展与结构变迁的影响，从而洞察金融结构转换对厦门市经济发展与结构变迁的影响问题。     

市场竞争与银行流动性创造——基于分支机构的银行竞争指标构建

市场竞争对于金融稳定是一把“双刃剑”,监管部门为维持银行业稳定需要考虑竞争政策的平衡性问题。充足的流动性创造有助于为经济活动提供资金,促进经济主体之间的交易,而过剩或 缺乏流动性均会迫使银行业陷入困境甚至引发流动性危机。本文使用2003—2017年我国176家商业银行数据,构建了一种新的基于分支机构和引力模型的银行竞争指标,来识别市场竞争与银行流动性创造之间的关系。结果发现,市场竞争对银行流动性创造具有促进作用,其中对银行资产端流动性创造的正向影响较大,对负债端流动性创造的影响较小,对资产负债表表外流动性创造没有影响。同时,受到正向影响的主体是城市商业银行、农村商业银行等地方性银行和外资银行,对国有大型银行、股份制银行等全国性银行则没有影响或具有负向影响。上市银行、系统重要性银行和处在危机时期、经济繁荣时期或货币政策宽松时期的银行,随着市场竞争加剧均会创造更多流动性。中介效应分析发现,市场竞争通过刺激金融创新、提高经营效率和扩大流动性信贷等中介机制促进了银行流动性创造。本文不仅丰富了银行竞争策略的经济效果分析,也为银行流动性创造研究提供了重要补充。     

基于首末位质量因子的BP神经网络财务风险预警模型

上市公司往往存在粉饰财务数据来美化企业经营状况的动机,这会降低财务风险预警模型预测的准确性。文章利用Benford律和Myer指数两种数据质量评估方法,构建Benford和Myer质量因子,引入BP神经网络模型,构造BM-BP神经网络财务风险预警模型;并进一步利用2000—2019年中国A股上市公司数据,评价数据质量因子对财务风险预警模型预测准确性的影响,分析新模型预测准确性的稳定性。实证分析结果显示:Benford和Myer质量因子提高了BP神经网络财务风险预警模型预测的准确性;在不同质量因子的比较结果中,包含评选指标Benford和Myer质量因子的BP神经网络财务风险预警模型具有较高的预测准确率和较低的二类误判率,稳定性良好;利用决策树算法筛选指标有效提高了新模型的预测准确性。     

中国上市金融机构关联性度量及影响因素分析

本文结合非对称斜率模型与单指标分位数回归,构建了中国上市金融机构的尾部风险网络,从而刻画中国金融市场尾部风险关联性的时变特征,并基于金融机构层面探讨尾部风险关联性的影响因素。研究表明,中国金融系统的总关联性与部门内关联性在金融危机与股灾期间显著上升,其中保险的部门内关联性为银行、保险、证券三部门中最高,而部门间关联性远小于部门内关联性,部门间的风险传染效应较为微弱。研究还发现投资活动是金融机构形成尾部风险关联的重要渠道,投资业务占比过高的机构应予以更多监管。     

Higher Moments and Prediction‐Based Estimation for the COGARCH(1,1) Model

COGARCH models are continuous time versions of the well‐known GARCH models of financial returns. The first aim of this paper is to show how the method of prediction‐based estimating functions can be applied to draw statistical inference from observations of a COGARCH(1,1) model if the higher‐order structure of the process is clarified. A second aim of the paper is to provide recursive expressions for the joint moments of any fixed order of the process. Asymptotic results are given, and a simulation study shows that the method of prediction‐based estimating function outperforms the other available estimation methods.     

Bayesian Estimation and Prediction of Stochastic Volatility Models via INLA

In this article, we assess Bayesian estimation and prediction using integrated Laplace approximation (INLA) on a stochastic volatility (SV) model. This was performed through a Monte Carlo study with 1,000 simulated time series. To evaluate the estimation method, two criteria were considered: the bias and square root of the mean square error (smse). The criteria used for prediction are the one step ahead forecast of volatility and the one day Value at Risk (VaR). The main findings are that the INLA approximations are fairly accurate and relatively robust to the choice of prior distribution on the persistence parameter. Additionally, VaR estimates are computed and compared for three financial time series returns indexes.