需求势能理论在多级物流网络预选点中的应用

针对分销物流网络的特点,提出了基于服务时限的多级物流网络选址问题和选点思路,应用需求势能理论建立了多网点选址数学模型,并提出了详尽的求解方法和步骤。最后通过医药分销物流网络系统的实例分析,说明了该方法在网络选点过程中的模型化方法和求解步骤。     

盈利要求与投资决策

本讨论了公司盈利要求对经理投资决策行为的影响，并进一步将该结果与社会最优结果进行了比较分析。本研究结果表明，较高的盈利要求将导致经理选择高风险的投资项目，即使该投资项目的预期收益未必高于低风险投资项目。从社会福利最优角度来看，盈利要求对社会福利的影响取决于公司所面临的投资环境和投资项目性质。     

油气勘探目标的经济评价

勘探目标经济评价要坚持常规经济评价和风险评价相结合，在用多种方法合理预测经济评价参数，充分考虑各种风险因素的基础上，认真做好风险评价研究，提高勘探目标经济评价的可靠性。     

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Ahman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．     

国际国内甲醇市场分析及预测

介绍了近年来国际国内甲醇市场供需、生产、贸易、价格等变化特点，并对未来市场走向进行了分析预测。     

An explainable machine learning approach to improve take-off time predictions

Accurate aircraft trajectory predictions are necessary to compute exact traffic demand figures, which are crucial for an efficient and effective air traffic flow and capacity management. At present, the uncertainty of the take-off time is one of the major contributions to the loss of trajectory predictability. In the EUROCONTROL Maastricht Upper Area Control Centre, the predicted take-off time for each individual flight relies on the information received from the Enhanced Traffic Flow Management System. However, aircraft do not always take-off at the times reported by this system due to several factors, which effects and interactions are too complex to be expressed with hard-coded rules. Previous work proposed a machine learning model that, based on historical data, was able to predict the take-off time of individual flights from a set of input features that effectively captures some of these elements. The model demonstrated to reduce by 30% the take-off time prediction errors of the current system one hour before the time that flight is scheduled to depart from the parking position. This paper presents an extension of the model, which overcomes this look-ahead time constraint and allows to improve take-off time predictions as early as the initial flight plan is received. In addition, a subset of the original set of input features has been meticulously selected to facilitate the implementation of the solution in an operational air traffic flow and capacity management system, while minimising the loss of predictive power. Finally, the importance and interactions of the input features are thoroughly analysed with additive feature attribution methods.     

An integrated system for information security management with the unified framework

Information security management plays an essential role for drawing the roadmap of information security; thus, many theoretical methodologies and practical standards are brought into this domain. However, many standards and methodologies are too cumbersome to be adopted by an organization. Additionally, there is no unified framework to systematically handle the tedious tasks of information security management. This study’s primary goal is to design an integrated system for information security management (ISISM) that aims to use current methodologies and standards to solve the above-mentioned issues. Because business impact analysis and risk analysis are the most important areas within this domain, we carefully select the related methods and then integrate them into a unified framework, upon which the proposed ISISM depends. To achieve this outcome for this study, security requirement engineering is adopted, which enables the designed system to support system users in generating risk assessment reports with related information security policies.     

Solvency Ⅱ与C-ROSS非寿险保险风险最低资本要求比较研究——基于中国60家财产保险公司的经验证据

本文以非寿险业务保险风险最低资本要求为考察对象,研究了欧盟Solvency Ⅱ与中国C-ROSS的差异,并利用中国保险市场60家财险公司的经验数据,对两者之间的差异进行了实证和模拟分析。研究结果表明,Solvency Ⅱ和C-ROSS对中国财险公司保险风险最低资本要求存在差异。对于拥有传统业务结构的财险公司,Solvency Ⅱ对保险风险最低资本要求更高,但是这种差距随着公司业务规模的缩小而减弱;对于以经营某些专业险种为主的财险公司,主营业务险种对两者差异具有决定性影响。本文的研究结论详细解释了Solvency Ⅱ与C-ROSS在非寿险保险风险最低资本计算上的异同,对C-ROSS下一步的修订工作提供了一定的支持与参考。     

Performance assessment of ensemble learning systems in financial data classification

Financial data classification plays an important role in investment and banking industry with the purpose to control default risk, improve cash and select the best customers. Ensemble learning and classification systems are becoming gradually more applied to classify financial data where outputs from different classification systems are combined. The objective of this research is to assess the relative performance of existing state‐of‐the‐art ensemble learning and classification systems with applications to corporate bankruptcy prediction and credit scoring. The considered ensemble systems include AdaBoost, LogitBoost, RUSBoost, subspace, and bagging ensemble system. The experimental results from three datasets: one is composed of quantitative attributes, one encompasses qualitative data, and another one combines both quantitative and qualitative attributes. By using ten‐fold cross‐validation method, the experimental results show that AdaBoost is effective in terms of low classification error, limited complexity, and short time processing of the data. In addition, the experimental results show that ensemble classification systems outperform existing models that were recently validated on the same databases. Therefore, ensemble classification system can be employed to increase the reliability and consistency of financial data classification task.     

基于SVM的房地产财务决策风险预测及敏感性分析

本文以房地产上市公司的财务数据为参考依据,结合相关性分析,筛选并建立影响财务风险的指标体系;利用支持向量机建立财务风险分析预测模型;并使用灰色关联理论对指标进行敏感性分析,从而得到影响财务风险最敏感的指标。