基于ASVM的创业板上市企业风险评估研究
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摘要
创业板的开设对缓解我国中小企业融资难、推进经济可持续发展以及提高国际竞争力都具有非常重要的现实意义和战略意义。然而，由于创业板上市企业的不成熟性、动态性、前景不确定性等复杂特征，其风险问题更加受到市场的关注。因此，建立一个科学的创业板上市企业风险评估模型，对创业投资者、创业板上市企业以及二级市场投资者准确识别、评估风险、提高风险管理能力有着极为重要的作用。
本文以创业板上市企业为研究对象，以帮助创业板市场相关各方准确评估企业风险，提高风险控制水平为目标，研究创业板上市企业风险评估方法。主要研究思路如下：首先，建立创业板上市企业风险评估指标体系；其次，应用数据挖掘方法，考虑数据的不平衡性和决策者的风险偏好，构造基于自适应支持向量机（adaptive support vector machine, ASVM）的风险评估模型；第三，充分利用主观决策方法和数据挖掘方法的优势，考虑群体决策过程中的成本，构造主观指标权重约束下的ASVM风险评估模型；最后，考虑指标风险的演化过程，构造基于过程信息的ASVM风险评估模型。本文的具体研究内容如下：
1.系统总结影响创业企业风险的因素，构建创业板上市企业风险评估指标体系。运用平衡计分卡的思想，从财务、市场及客户、内部流程、管理及员工和环境等5个视角，并考虑创业板上市企业在行业分布、公司规模、收益结构、所处的生命周期阶段、公司治理结构以及稳定性等方面不同于主板上市企业的特征，采用财务和非财务信息反映创业板上市企业风险，归纳出初步的风险评估指标体系。利用专家咨询法对指标的重要性评分，结合重要性、指标可得性等原则选择最终用于创业板上市企业风险评估的指标。
2.运用数据挖掘技术，基于历史数据构造基于ASVM的创业板上市企业风险评估模型。针对国内创业板上市企业风险数据的小样本、贫信息、非线性、不平衡等复杂特征，考虑不同的决策者对分类错误风险的不同的偏好，提出了一种ASVM分类模型。针对基于ASVM的风险评估模型在非线性的情况下可解释性不强的问题，利用二次规划技术提取创业板上市企业指标体系中各个指标的权重值。
3.融合历史数据的客观信息、专家的主观判断以及决策者的偏好信息构造创业板上市企业风险评估模型。针对指标权重确定过程中专家判断的一致性、群体判断的一致性以及多轮交互机制的控制等问题，提出一个考虑决策成本的群决策框架，这一框架包括两个反馈机制：第一个反馈机制是根据专家对群体意见形成的贡献来调整专家的权重，第二个反馈机制是引导专家根据群体意见和其他专家意见改进他们自身判断的质量。
针对常用主客观融合策略不能真正将专家意见融入到模型的求解过程，有可能背离专家的原始判断或历史信息所确定的客观权重，出现决策结果出来后的主观调节等问题，提出基于主观指标权重约束ASVM的风险评估模型。基于数理统计的观点，将决策群体中各位专家给出的权重看作是真实权重的一个样本，在权重样本估计的权重分布区间内利用ASVM学习得到真实的权重值。
4.考虑指标风险的演化过程，构造基于过程信息ASVM的风险评估模型。针对常用风险评估模型单纯以单期截面数据构造模型，对风险动态演化趋势信息反映能力不足和不重视决策者的主观判断等问题，对不同特性的指标运用不同的方法度量其风险。对能以单期截面数据反映的状态变量，采用一种包含期望目标的S型函数度量指标蕴含的风险；对于需要用时间序列数据反映的过程变量，则借助现代金融理论的资产风险度量方法，综合考虑决策者对指标的期望、时间序列的均值、方差或分布偏斜等特征，将包含企业风险演化趋势信息的时间序列数据映射为一个截面值，从而使风险评估模型具有处理动态信息的能力。
The opening of the Growth Enterprise Market (GEM) has important practical and strategicsignificance for alleviating problems of financing difficulties facing small-medium enterprises(SME), promoting sustainable economic development and improving the internationalcompetitiveness. However, GEM listed companies have characteristics of immaturity, dynamic,future uncertainty and so on, so the risk is even more concerned by the market. There are very fewliteratures on China’s Growth Enterprise Market to study the risk assessment. The establishment of ascientific risk assessment model for listed companies on the GEM is very important for venturecapitalists, the listed company on GEM and the investors in the secondary market to identify andpredict risks accurately and to improve risk management capability.
Within the context of the listed companies on GEM, this dissertation investigates the riskassessment methods. Our aim is to help the interested parties in GEM to assess business riskaccurately and to improve the risk control level. In this dissertation, firstly, we establish a riskassessment index system for the listed companies on GEM. Secondly, we propose a risk assessmentmodel based on support vector machine approach using historical data and the imbalance of risk dataon GEM and the decision-makers’ risk preferences are considered in the proposed model. Thirdly,with the aid of the experts’ domain knowledge and experience, we establish coordination frameworkfor groups in which the corresponding cost constraints for reaching acceptable consistency isconsidered. In order to obtain more effective results, we establish an evaluation model that makesfull use of the advantages of subjective decision-making model and of data mining models.Therefore, the subjective and objective information are integrated. Finally, the risk assessmentmodel based specific–attribute risk is constructed with some dynamic features. Detailed contents ofthis dissertation are as follows.
1. In this dissertation, we first summarize and review the risk factors affect venturesystematically. Then, we conclude the preliminary risk assessment index system using the balancedscorecard thoughts from five perspectives: financial, marketing and customer, internal processes,management and employees as well as environment. When designing risk assessment index system,we also consider the differences between companies listed on the GEM and companies listed on theMain Board in the industry distribution, the company size, income structure, in which the life-cyclestage, corporate governance structure and stability. In this dissertation, the risks of companies listedon the GEM are reflected by both financial information and non-financial information. After thepreliminary risk assessment index system is obtained, weights of all indexes are given through usingquestionnaire, experts’ advice and the final risk index can be selected by combining with the ratingof importance of index and the availability of the index.
2. The risk assessment model for the companies listed on the GEM is induced from historicaldata using data mining techniques. An adaptive support vector machine is proposed for thecharacteristics of risk data in the domestic GEM such as small samples of data, poor information,nonlinear, unbalanced and other complex features listed companies. Different preferences of thedifferent decision-makers for the risk of misclassification are also considered. For the riskassessment model based on SVM is not strong interpretability under the nonlinear case, weights ofall indexes are extracted from SVM through using the quadratic programming techniques.
3. In this dissertation, we build risk assessment models for companies listed on GEM throughusing experts’ methods within the framework of multi-attribute group decision making based on AHP. In this dissertation, for improving the consistency of comparison matrix, the consensus of theexperts and the control mechanisms of interaction, a cost constrained group decision makingframework was proposed that includes two feedback mechanisms: the first feedback mechanism is toadjust the experts’ weight according to their contribution to the decision making; the second is toguide the experts to improve the quality of their own judgments based on the opinions andperspectives.
For the strategies commonly used in the integration of subjectivity and subjectivity can notreally integrate experts’ subjective judgments into the process of solving the model, there may be adeparturing from experts’ original judgment or objective weight determined by history informationand questions emerging out of subjective adjustment when outcomes of decision-making have beenobtained. On the basis of statistics, regarding experts’ weight as a real sample in the decision-makinggroups, this dissertation obtains the true weights using ASVM within the estimated weight of theweight distribution in the sample interval.
4. Considering the risk evolution process of index of assessment, this dissertation structuresASVM based on process information to assess the risk for listed companies on the GEM. Commonlyused risk assessment model for a simple single-structure model of cross-sectional data, trendinginformation on the risks reflect the dynamic evolution of insufficient capacity and not attachingimportance to issues such as subjective judgments of decision makers, the dissertation, on the use ofdifferent characteristics of the indicators, measures the risks in different ways. Single cross-sectionof the data can reflect the state of variables, including expectations of an S-function target metricsinherent risks, time series data reflecting the need to process variables, the use of modern financialtheory of asset risk measure, considering the expectations of policy makers on the indicators, timeseries of the mean, variance or skewed distribution and other characteristics, including informationon trends in enterprise risk evolution of time series data mapped to a cross section. So the riskassessment model has the ability to handle dynamic information.
本文以创业板上市企业为研究对象，以帮助创业板市场相关各方准确评估企业风险，提高风险控制水平为目标，研究创业板上市企业风险评估方法。主要研究思路如下：首先，建立创业板上市企业风险评估指标体系；其次，应用数据挖掘方法，考虑数据的不平衡性和决策者的风险偏好，构造基于自适应支持向量机（adaptive support vector machine, ASVM）的风险评估模型；第三，充分利用主观决策方法和数据挖掘方法的优势，考虑群体决策过程中的成本，构造主观指标权重约束下的ASVM风险评估模型；最后，考虑指标风险的演化过程，构造基于过程信息的ASVM风险评估模型。本文的具体研究内容如下：
1.系统总结影响创业企业风险的因素，构建创业板上市企业风险评估指标体系。运用平衡计分卡的思想，从财务、市场及客户、内部流程、管理及员工和环境等5个视角，并考虑创业板上市企业在行业分布、公司规模、收益结构、所处的生命周期阶段、公司治理结构以及稳定性等方面不同于主板上市企业的特征，采用财务和非财务信息反映创业板上市企业风险，归纳出初步的风险评估指标体系。利用专家咨询法对指标的重要性评分，结合重要性、指标可得性等原则选择最终用于创业板上市企业风险评估的指标。
2.运用数据挖掘技术，基于历史数据构造基于ASVM的创业板上市企业风险评估模型。针对国内创业板上市企业风险数据的小样本、贫信息、非线性、不平衡等复杂特征，考虑不同的决策者对分类错误风险的不同的偏好，提出了一种ASVM分类模型。针对基于ASVM的风险评估模型在非线性的情况下可解释性不强的问题，利用二次规划技术提取创业板上市企业指标体系中各个指标的权重值。
3.融合历史数据的客观信息、专家的主观判断以及决策者的偏好信息构造创业板上市企业风险评估模型。针对指标权重确定过程中专家判断的一致性、群体判断的一致性以及多轮交互机制的控制等问题，提出一个考虑决策成本的群决策框架，这一框架包括两个反馈机制：第一个反馈机制是根据专家对群体意见形成的贡献来调整专家的权重，第二个反馈机制是引导专家根据群体意见和其他专家意见改进他们自身判断的质量。
针对常用主客观融合策略不能真正将专家意见融入到模型的求解过程，有可能背离专家的原始判断或历史信息所确定的客观权重，出现决策结果出来后的主观调节等问题，提出基于主观指标权重约束ASVM的风险评估模型。基于数理统计的观点，将决策群体中各位专家给出的权重看作是真实权重的一个样本，在权重样本估计的权重分布区间内利用ASVM学习得到真实的权重值。
4.考虑指标风险的演化过程，构造基于过程信息ASVM的风险评估模型。针对常用风险评估模型单纯以单期截面数据构造模型，对风险动态演化趋势信息反映能力不足和不重视决策者的主观判断等问题，对不同特性的指标运用不同的方法度量其风险。对能以单期截面数据反映的状态变量，采用一种包含期望目标的S型函数度量指标蕴含的风险；对于需要用时间序列数据反映的过程变量，则借助现代金融理论的资产风险度量方法，综合考虑决策者对指标的期望、时间序列的均值、方差或分布偏斜等特征，将包含企业风险演化趋势信息的时间序列数据映射为一个截面值，从而使风险评估模型具有处理动态信息的能力。
The opening of the Growth Enterprise Market (GEM) has important practical and strategicsignificance for alleviating problems of financing difficulties facing small-medium enterprises(SME), promoting sustainable economic development and improving the internationalcompetitiveness. However, GEM listed companies have characteristics of immaturity, dynamic,future uncertainty and so on, so the risk is even more concerned by the market. There are very fewliteratures on China’s Growth Enterprise Market to study the risk assessment. The establishment of ascientific risk assessment model for listed companies on the GEM is very important for venturecapitalists, the listed company on GEM and the investors in the secondary market to identify andpredict risks accurately and to improve risk management capability.
Within the context of the listed companies on GEM, this dissertation investigates the riskassessment methods. Our aim is to help the interested parties in GEM to assess business riskaccurately and to improve the risk control level. In this dissertation, firstly, we establish a riskassessment index system for the listed companies on GEM. Secondly, we propose a risk assessmentmodel based on support vector machine approach using historical data and the imbalance of risk dataon GEM and the decision-makers’ risk preferences are considered in the proposed model. Thirdly,with the aid of the experts’ domain knowledge and experience, we establish coordination frameworkfor groups in which the corresponding cost constraints for reaching acceptable consistency isconsidered. In order to obtain more effective results, we establish an evaluation model that makesfull use of the advantages of subjective decision-making model and of data mining models.Therefore, the subjective and objective information are integrated. Finally, the risk assessmentmodel based specific–attribute risk is constructed with some dynamic features. Detailed contents ofthis dissertation are as follows.
1. In this dissertation, we first summarize and review the risk factors affect venturesystematically. Then, we conclude the preliminary risk assessment index system using the balancedscorecard thoughts from five perspectives: financial, marketing and customer, internal processes,management and employees as well as environment. When designing risk assessment index system,we also consider the differences between companies listed on the GEM and companies listed on theMain Board in the industry distribution, the company size, income structure, in which the life-cyclestage, corporate governance structure and stability. In this dissertation, the risks of companies listedon the GEM are reflected by both financial information and non-financial information. After thepreliminary risk assessment index system is obtained, weights of all indexes are given through usingquestionnaire, experts’ advice and the final risk index can be selected by combining with the ratingof importance of index and the availability of the index.
2. The risk assessment model for the companies listed on the GEM is induced from historicaldata using data mining techniques. An adaptive support vector machine is proposed for thecharacteristics of risk data in the domestic GEM such as small samples of data, poor information,nonlinear, unbalanced and other complex features listed companies. Different preferences of thedifferent decision-makers for the risk of misclassification are also considered. For the riskassessment model based on SVM is not strong interpretability under the nonlinear case, weights ofall indexes are extracted from SVM through using the quadratic programming techniques.
3. In this dissertation, we build risk assessment models for companies listed on GEM throughusing experts’ methods within the framework of multi-attribute group decision making based on AHP. In this dissertation, for improving the consistency of comparison matrix, the consensus of theexperts and the control mechanisms of interaction, a cost constrained group decision makingframework was proposed that includes two feedback mechanisms: the first feedback mechanism is toadjust the experts’ weight according to their contribution to the decision making; the second is toguide the experts to improve the quality of their own judgments based on the opinions andperspectives.
For the strategies commonly used in the integration of subjectivity and subjectivity can notreally integrate experts’ subjective judgments into the process of solving the model, there may be adeparturing from experts’ original judgment or objective weight determined by history informationand questions emerging out of subjective adjustment when outcomes of decision-making have beenobtained. On the basis of statistics, regarding experts’ weight as a real sample in the decision-makinggroups, this dissertation obtains the true weights using ASVM within the estimated weight of theweight distribution in the sample interval.
4. Considering the risk evolution process of index of assessment, this dissertation structuresASVM based on process information to assess the risk for listed companies on the GEM. Commonlyused risk assessment model for a simple single-structure model of cross-sectional data, trendinginformation on the risks reflect the dynamic evolution of insufficient capacity and not attachingimportance to issues such as subjective judgments of decision makers, the dissertation, on the use ofdifferent characteristics of the indicators, measures the risks in different ways. Single cross-sectionof the data can reflect the state of variables, including expectations of an S-function target metricsinherent risks, time series data reflecting the need to process variables, the use of modern financialtheory of asset risk measure, considering the expectations of policy makers on the indicators, timeseries of the mean, variance or skewed distribution and other characteristics, including informationon trends in enterprise risk evolution of time series data mapped to a cross section. So the riskassessment model has the ability to handle dynamic information.
引文
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