基于LR RF XGBoost的债券违约风险预警

随着市场经济的迅猛发展,各国的债券市场也相继成长,并趋向于多元化发展.然而,在这一发展过程中,中国的债券违约事件屡见不鲜且愈演愈烈,极大地阻碍了市场活力.以发行企业债券、公司债券、短期融资债券以及中期债券的公司为研究主体,提出LR RF XGBoost债券违约预警模型,该模型基于软投票法将逻辑回归（Logistic Regression）、随机森林（Random Forest）、极端梯度提升算法（Extreme Gradient Boosting）相融合,对样本的财务指标及非财务指标数据进行研究.研究结果发现:LR RF XGBoost融合模型相比于其他单一预警模型泛化能力更强,准确率高达95.3%.该方法有利于为投资者以及债券市场监督部门提供可靠的预测信息,帮助企业及早识别风险,为债券市场的健康发展提供保障.     

基于遗传算法KMV模型的公司债券违约风险度量研究

基于遗传算法对KMV模型进行了修正,并运用修正的KMV模型对样本债券在2017—2018期间的违约风险进行度量。结果表明:基于遗传算法改进的KMV模型在预测公司债券违约风险方面有着不错的表现,拟合正确率远高于改进前的原模型;并且公司债券所属行业的不同会影响模型违约点的选择,从而影响KMV模型度量违约风险的效果。     

考虑违约风险市场价格的期权定价

介绍信用风险的模型有两个主要类型:一类是基于公司价值基础上的违约模型,另一类是简化模型.在不完全市场下定价一个有违约可能的欧式期权,用强度遵从均值回复过程的重随机的poission过程来描述违约过程并且采用公司价值模型的补偿率,在假定违约过程与标的资产,企业价值都相关的情况下,将原始概率调整为风险调节后的概率,利用资产定价理论中的定价核的概念,可以得到风险调节后的惟一的等价鞅测度,并利用随机贴现因子模型导出违约风险市场价格的计算表达式及相应的期权价格公式,使得确定惟一的合理的期权价格成为可能.     

基于类别不平衡的企业信用风险违约测度探索——以制造业上市公司为例

针对所获取的类别不平衡的深沪A股制造业上市公司财务数据,为了预测制造业上市公司信用违约情况,提出基于欠采样改进的Lasso-Logistic模型;首先通过计算WOE和IV值,剔除风险识别能力和稳定性较差的变量,接着从"数据"层面对现有的Lasso-Logistic模型进行批量欠采样处理,最后结合"算法"层面对Lasso-Logistic子模型的预测概率进行简单平均集成来研究模型的改进效果;结果表明,从模型整体效果的测度指标AUC值和区分度指标KS值来看,基于欠采样改进的带有变量筛选能力的Batch-US-LLR模型能有效提升企业信用风险违约测度的效果,对完善企业风险预警机制,提升违约风险识别能力具有可行性和有效性。     

修正的KMV模型对中国中小企业信用风险的度量

利用KMV模型对14家（7家ST和7家非ST）公司进行信用风险度量分析,结合中国股票的实际情况,对KMV模型中的股权资产价值和股权资产价值波动率进行了修正,再利用修正后的模型进行实证分析.分析结果表明,ST公司比非ST公司有更高的信用风险,且对于陷入困境的ST公司,KMV模型也提供了一种全新的角度来度量其信用风险.     

上市公司信用风险的评价——基于KMV模型视角

如何度量信用风险是从事金融活动的各方迫切关心的重要问题。本文以2009年ST公司及与之配对的非ST公司为研究样本,在对KMV模型的求解过程进行解析的基础上,用KMV模型对其信用风险进行识别与评价,研究结果表明,ST公司的违约距离显著不同于配对样本,其信用风险明显较高,这说明KMV模型对存在不良贷款的违约公司具有较强的风险识别和预警能力,是一种有效的风险管理工具。     

基于Logit和KMV的我国上市公司信用风险的比较研究

利用Logit回归模型和KMV模型分别对选取的29家我国上市公司样本的信用风险进行实证分析和比较。结果表明,两个模型基本上都能反映上市公司的信用状况,虽然KMV模型对非违约样本组的预测结果要优于Logit模型,但是在违约公司样本组的预测上,Logit模型要远比KMV模型准确。从总体效果来看,Logit模型的预测准确性要高于KMV模型。     

非均衡数据的债券违约预警研究

将上海交易所和深证交易所发行的30只违约债券和468只未违约债券作为研究样本,将债券是否违约设定为一个二分类问题进行识别分析,针对该问题构建了基于SVM的ADm R-AdaboostSVM分类模型;从企业资本结构、盈利能力、现金流量、偿债能力4个评估因素中筛选16个预警指标,运用ADASYN方法进行过采样合成新样本点,将特征提取m RMR方法引入债券违约领域,得出长期负债率、资本收益率、成本费用利润率以及股权比例这4个变量作为债券违约的最终预警指标,在此基础上运用AdaboostSVM模型进行风险识别。研究结果表明:在建模过程中克服了样本非均衡化问题使得分类精度显著提高,同时通过解决高维数据冗余问题,识别违约债券的准确率进一步提高,反复验证表明该模型具有较强的稳健性和有效性,具有一定的应用价值。     

非均衡数据的债券违约预警研究

将上海交易所和深证交易所发行的30只违约债券和468只未违约债券作为研究样本，将债券是否违约设定为一个二分类问题进行识别分析，针对该问题构建了基于SVM的ADmR-AdaboostSVM分类模型；从企业资本结构、盈利能力、现金流量、偿债能力4个评估因素中筛选16个预警指标，运用ADASYN方法进行过采样合成新样本点，将特征提取mRMR方法引入债券违约领域，得出长期负债率、资本收益率、成本费用利润率以及股权比例这4个变量作为债券违约的最终预警指标，在此基础上运用AdaboostSVM模型进行风险识别。研究结果表明：在建模过程中克服了样本非均衡化问题使得分类精度显著提高，同时通过解决高维数据冗余问题，识别违约债券的准确率进一步提高，反复验证表明该模型具有较强的稳健性和有效性，具有一定的应用价值。     

电力系统短期负荷预测的多神经网络Boosting集成模型

提出了一种改进的多神经网络集成自适应Boosting回归算法.算法中采用相对误差模型代替绝对误差模型,可以更接近于回归预测问题的要求,并在Boosting迭代过程中,在对训练集采样得到新的训练子集的同时,也对校验集采样得到新的校验子集,保证了两者的一致性.进而采用美国加州电力市场的实际数据,建立了由多个神经网络集成的电力系统短期负荷预测模型.预测结果表明,与传统的单网络预测模型相比,Boosting集成预测模型能显著提高模型输出的稳定性,增强网络结构及模型选择的可靠性,获得更高的预测精度.     

Markedness and its Interpretation in Language Use

Language markedness is a common phenomenon in languages, and is reflected from hearing, vision and sense, i.e. the variation in the three aspects such as phonology, morphology and semantics. This paper focuses on the interpretation of markedness in language use following the three perspectives, i.e. pragmatic interpretation, psychological interpretation and cognitive interpretation, with an aim to define the function of markedness.     

Characterization of Ordovician carbonate reservoirs, southeastern Saskatchewan, Canada

The discovery of the prolific Ordovician Red River reservoirs in 1995 in southeastern Saskatchewan was the catalyst for extensive exploration activity which resulted in the discovery of more than 15 new Red River pools. The best yields of Red River production to date have been from dolomite reservoirs. Understanding the processes of dolomitization is, therefore, crucial for the prediction of the connectivity, spatial distribution and heterogeneity of dolomite reservoirs.The Red River reservoirs in the Midale area consist of 3～4 thin dolomitized zones, with a total thickness of about 20 m, which occur at the top of the Yeoman Formation. Two types of replacement dolomite were recognized in the Red River reservoir: dolomitized burrow infills and dolomitized host matrix. The spatial distribution of dolomite suggests that burrowing organisms played an important role in facilitating the fluid flow in the backfilled sediments. This resulted in penecontemporaneous dolomitization of burrow infills by normal seawater. The dolomite in the host matrix is interpreted as having occurred at shallow burial by evaporitic seawater during precipitation of Lake Almar anhydrite that immediately overlies the Yeoman Formation. However, the low δ18O values of dolomited burrow infills (-5.9‰～ -7.8‰, PDB) and matrix dolomites (-6.6‰～ -8.1‰, avg. -7.4‰ PDB) compared to the estimated values for the late Ordovician marine dolomite could be attributed to modification and alteration of dolomite at higher temperatures during deeper burial, which could also be responsible for its 87Sr/86Sr ratios (0.7084～0.7088) that are higher than suggested for the late Ordovician seawaters (0.7078～0.7080). The trace amounts of saddle dolomite cement in the Red River carbonates are probably related to "cannibalization" of earlier replacement dolomite during the chemical compaction.     

Features of Women's language

Language is a means of verbal communication. People use language to communicate with each other. In the society, no two speakers are exactly alike in the way of speaking. Some differences are due to age, gender, statue and personality. Above all, gender is one of the obvious reasons. The writer of this paper tries to describe the features of women＇s language from these perspectives： pronunciation, intonation, diction, subjects, grammar and discourse. From the discussion of the features of women＇s language, more attention should be paid to language use in social context. What＇s more, the linguistic phenomena in a speaking community can be understood more thoroughly.     

A computer generator for randomly layered structures

AcomputergeneratorforrandomlylayeredstructuresYUJia shun1,2,HEZhen hua2(1.TheInstituteofGeologicalandNuclearSciences,NewZealand;2.StateKeyLaboratoryofOilandGasReservoirGeologyandExploitation,ChengduUniversityofTechnology,China)Abstract:Analgorithmisintrod…     

低渗透非均质砂岩油藏启动压力梯度研究

理论推导与室内实验相结合,建立了低渗透非均质砂岩油藏启动压力梯度确定方法。首先借助油藏流场与电场相似的原理,推导了非均质砂岩油藏启动压力梯度计算公式。其次基于稳定流实验方法,建立了非均质砂岩油藏启动压力梯度测试方法。结果表明:低渗透非均质砂岩油藏的启动压力梯度确定遵循两个等效原则。平面非均质油藏的启动压力梯度等于各级渗透率段的启动压力梯度关于长度的加权平均;纵向非均质油藏的启动压力梯度等于各渗透率层的启动压力梯度关于渗透率与渗流面积乘积的加权平均。研究成果可用于有效指导低渗透非均质砂岩油藏的合理井距确定,促进该类油藏的高效开发。     

On Tragic Consciousness In The Works By Ernest Hemingway

As an American modern novelist who were famous in the literary world, Hemingway was not a person who always followed the trend but a sharp observer. At the same time, he was a tragedy maestro, he paid great attention on existence, fate and end-result. The dramatis personae's tragedy of his works was an extreme limit by all means tragedy on the meaning of fearless challenge that failed. The beauty of tragedy was not produced on the destruction of life, but now this kind of value was in the impact activity. They performed for the reader about the tragedy on challenging for the limit and the death.     

海南岛地体及其毗邻陆缘晚中生代—新生代古地磁研究和构造演化

本文叙述了对海南岛及其毗邻大陆边缘白垩纪到第四纪地层岩石进行古地磁研究的全部工作过程。通过分析岩石中剩余磁矢量的磁偏角及磁倾角的变化,提出海南岛白垩纪以来经历的构造演化模式如下:早期伴随顺时针旋转而向南迁移,后期伴随逆时针转动并向北运移。联系该地区及邻区的地质、地球物理资料,对海南岛上述的构造地体运动提出以下认识:北部湾内早期有一拉张作用,主要是该作用使湾内地壳显著伸长减薄,形成北部湾盆地。从而导致了海南岛的早期构造运动,而海南岛后期的构造运动则主要是受南海海底扩张的影响。海南地体运动规律的阐明对于了解北部湾油气盆地的形成演化有重要的理论和实际意义。     

An Index mechanism for multi-scale view in spatial databases

There are numerous geometric objects stored in the spatial databases. An importance function in a spatial database is that users can browse the geometric objects as a map efficiently. Thus the spatial database should display the geometric objects users concern about swiftly onto the display window. This process includes two operations:retrieve data from database and then draw them onto screen. Accordingly, to improve the efficiency, we should try to reduce time of both retrieving object and displaying them. The former can be achieved with the aid of spatial index such as R-tree, the latter require to simplify the objects. Simplification means that objects are shown with sufficient but not with unnecessary detail which depend on the scale of browse. So the major problem is how to retrieve data at different detail level efficiently. This paper introduces the implementation of a multi-scale index in the spatial database SISP (Spatial Information Shared Platform) which is generalized from R-tree. The difference between the generalization and the R-tree lies on two facets: One is that every node and geometric object in the generalization is assigned with a importance value which denote the importance of them, and every vertex in the objects are assigned with a importance value,too. The importance value can be use to decide which data should be retrieve from disk in a query. The other difference is that geometric objects in the generalization are divided into one or more sub-blocks, and vertexes are total ordered by their importance value. With the help of the generalized R-tree, one can easily retrieve data at different detail levels.Some experiments are performed on real-life data to evaluate the performance of solutions that separately use normal spatial index and multi-scale spatial index. The results show that the solution using multi-scale index in SISP is satisfying.     

《Chinese Science Bulletin》SUBJECT INDEX TO VOLUME 59(2014)

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Achieving Unusual States of Matter Under High Pressure

The elongation method,originally proposed by Imamura was further developed for many years in our group.As a method towards O(N)with high efficiency and high accuracy for any dimensional systems.This treatment designed for one-dimensional(ID)polymers is now available for three-dimensional(3D)systems,but geometry optimization is now possible only for 1D-systems.As an approach toward post-Hartree-Fock,it was also extended to