知识关联视角下的金融知识表示及风险识别
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摘要
金融风险分析方法的局限性,如数据来源单一性、数据类型简单化和研究角度片面性等,归根结底是金融大数据组织层面的问题。传统金融大数据的扁平化组织忽略了数据中丰富的知识关联。本文从知识组织的角度出发,对金融知识表示方法进行了探究,将知识表示过程分为知识表示模式层、知识实例层和知识挖掘层。本文首先分析了金融大数据的四种典型关联模式,即分类关联、时空关联、统计关联和事件关联,并从实现方式的差异角度,将各类关联模式归类于静态本体、动态本体和社会本体之中;针对每一类本体,本文提出了相应的实现方案,如复用现有的FIBO本体;最后,基于金融风险识别应用案例进行了详细说明。
The limitations of financial risk analysis, such as homogeneous data source, simplistic data types, and one-sided research perspective, are attributed to insufficient representation of big data in finance; the traditional flat data organization ignores the rich knowledge connection among financial data. This paper explores the knowledge representation model of big data on financials from a knowledge organization perspective. It classifies the process of knowledge representation into three layers: knowledge representation mode, knowledge instance, and knowledge mining. First, the paper summarizes the typical connection patterns that exist in the financial domain, such as classification connection, spatial-temporal connec‐tion, statistical connection, and event connection, and categorizes these into static ontology, dynamic ontology, and social ontology according to the differential realization. Then, following the implementation of each ontology, it proposes related implementation schemes as well as the reuse of the existing ontology of Financial Industry Business Ontology(FIBO).Lastly, it demonstrates the representation process using the case of financial risk identification.
The limitations of financial risk analysis, such as homogeneous data source, simplistic data types, and one-sided research perspective, are attributed to insufficient representation of big data in finance; the traditional flat data organization ignores the rich knowledge connection among financial data. This paper explores the knowledge representation model of big data on financials from a knowledge organization perspective. It classifies the process of knowledge representation into three layers: knowledge representation mode, knowledge instance, and knowledge mining. First, the paper summarizes the typical connection patterns that exist in the financial domain, such as classification connection, spatial-temporal connec‐tion, statistical connection, and event connection, and categorizes these into static ontology, dynamic ontology, and social ontology according to the differential realization. Then, following the implementation of each ontology, it proposes related implementation schemes as well as the reuse of the existing ontology of Financial Industry Business Ontology(FIBO).Lastly, it demonstrates the representation process using the case of financial risk identification.
引文
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[19]朱曼玲.基于空间属性的金融危机传染研究[D].哈尔滨:哈尔滨工业大学, 2010.
[20]李广建,化柏林.大数据分析与情报分析关系辨析[J].中国图书馆学报, 2014(5):1422.
[21]何盛明.财经大辞典[M].北京:中国财政经济出版社, 1990.
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[25] Carlson A, Betteridge J, Wang R C, et al. Coupled semisupervised learning for information extraction[C]//Proceedings of the Third ACM International Conference on Web Search and Data Mining. New York:ACM Press, 2010:101110.
[26] Jurisica I, Mylopoulos J, Yu E. Ontologies for knowledge management:An information systems perspective[J]. Knowledge and Information Systems, 2004, 6(4):380401.
[27] Dai H J, Dai B, Song L. Discriminative embeddings of latent variable models for structured data[C]//Proceedings of the 33rd International Conference on Machine Learning, 2016, 48:27022711.
[28] Poledna S, Luis MolinaBorboa J, MartinezJaramillo S, et al.The multilayer network nature of systemic risk and its implications for the costs of financial crises[J]. Journal of Financial Stability, 2015, 20:7081.
[29] Glattfelder J B, Battiston S. Backbone of complex networks of corporations:The flow of control[J]. Physical Review E, 2009,80:036104.
[30] Kodres L E, Pritsker M. A rational expectations model of financial contagion[J]. The Journal of Finance, 2002, 57(2):769799.
[31]李小玉,薛有志,周杰. CEO关联、内部治理与企业绩效[J].管理科学, 2017, 30(5):102118.
[32]叶康.金融机构系统性风险管理的知识层面建模[D].合肥:中国科学技术大学, 2009.
[33]温立,周书民,王延章.基于辅助决策的应急事件本体模型研究[J].情报杂志, 2010, 29(2):131134.
[34]刘新民,郜庆.我国资本系相关法学问题初探[J].贵州警官职业学院学报, 2006, 18(2):4448, 72.
[2] Schwarcz S. Systemic risk[J]. Georgetown Law Journal, 2008,97:193249.
[3]陈尾虹,唐振鹏.金融机构系统性风险研究述评——基于机制、测度与监管视角[J].当代财经, 2016(5):5767.
[4] Silva W, Kimura H, Sobreiro V A. An analysis of the literature on systemic financial risk:A survey[J]. Journal of Financial Stability, 2017, 28:91114.
[5] Glasserman P, Young B P. How likely is contagion in financial networks?[J]. Journal of Banking&Finance, 2015, 50:383399.
[6] Lo Duca M, Peltonen T A. Assessing systemic risks and predicting systemic events[J]. Journal of Banking&Finance, 2013, 37(7):21832195.
[7]刘向丽,顾舒婷.房地产对金融体系风险溢出效应研究——基于ARGARCHCoVaR方法[J].系统工程理论与实践, 2014, 34(s1):106111.
[8]谢福座.基于GARCH-Copula-CoVaR模型的风险溢出测度研究[J].金融发展研究, 2010(12):1216.
[9]孙艳霞,鲍勤,汪寿阳.房地产贷款损失与银行间市场风险传染——基于金融网络方法的研究[J].管理评论, 2015(3):315.
[10] Burkholz R, Herrmann H J, Schweitzer F. Explicit size distributions of failure cascades redefine systemic risk on finite networks[J]. Scientific Reports, 2018, 8:Article No. 6878.
[11] Petrone D, Latora V. A dynamic approach merging network theory and credit risk techniques to assess systemic risk in financial networks[J]. Scientific Reports, 2018, 8:Article No. 5561.
[12] Newell A. The knowledge level[J]. Artificial Intelligence, 1982, 1(18):87127.
[13]王向前,张宝隆,李慧宗.本体研究综述[J].情报杂志, 2016(6):163170.
[14]洪娜,钱庆,范炜,等.关联数据中关系发现的可视化实践[J].现代图书情报技术, 2013(2):1117.
[15] Ruan T, Xue L J, Wang H F, et al. Building and exploring an enterprise knowledge graph for investment analysis[C]//Proceedings of the International Semantic Web Conference. Heidelberg:Springer, 2016:418436.
[16]卢文兴,陈黎,朱洪波,等.基于本体翻译的领域本体自动构建[J].计算机工程与设计, 2011, 32(9):32033207.
[17]李智山,黄光庆,陈凤桂,等.中国全国性股份制商业银行地理分布特征研究[J].经济地理, 2014(2):1927.
[18] Anselin L. Local indicators of spatial association—LISA[J]. Geographical Analysis, 1995, 27(2):93115.
[19]朱曼玲.基于空间属性的金融危机传染研究[D].哈尔滨:哈尔滨工业大学, 2010.
[20]李广建,化柏林.大数据分析与情报分析关系辨析[J].中国图书馆学报, 2014(5):1422.
[21]何盛明.财经大辞典[M].北京:中国财政经济出版社, 1990.
[22]从异动金融事件中汲取教训[J].中国金融, 2013(17):3.
[23]张炜.构建突发重大金融事件应对机制探析[J].金融经济,2005(8):3637.
[24] Mitchell T M, Betteridge J, Carlson A, et al. Populating the semantic Web by macroreading Internet text[C]//Proceedings of the 8th International Semantic Web Conference. Heidelberg:Springer, 2009:9981002.
[25] Carlson A, Betteridge J, Wang R C, et al. Coupled semisupervised learning for information extraction[C]//Proceedings of the Third ACM International Conference on Web Search and Data Mining. New York:ACM Press, 2010:101110.
[26] Jurisica I, Mylopoulos J, Yu E. Ontologies for knowledge management:An information systems perspective[J]. Knowledge and Information Systems, 2004, 6(4):380401.
[27] Dai H J, Dai B, Song L. Discriminative embeddings of latent variable models for structured data[C]//Proceedings of the 33rd International Conference on Machine Learning, 2016, 48:27022711.
[28] Poledna S, Luis MolinaBorboa J, MartinezJaramillo S, et al.The multilayer network nature of systemic risk and its implications for the costs of financial crises[J]. Journal of Financial Stability, 2015, 20:7081.
[29] Glattfelder J B, Battiston S. Backbone of complex networks of corporations:The flow of control[J]. Physical Review E, 2009,80:036104.
[30] Kodres L E, Pritsker M. A rational expectations model of financial contagion[J]. The Journal of Finance, 2002, 57(2):769799.
[31]李小玉,薛有志,周杰. CEO关联、内部治理与企业绩效[J].管理科学, 2017, 30(5):102118.
[32]叶康.金融机构系统性风险管理的知识层面建模[D].合肥:中国科学技术大学, 2009.
[33]温立,周书民,王延章.基于辅助决策的应急事件本体模型研究[J].情报杂志, 2010, 29(2):131134.
[34]刘新民,郜庆.我国资本系相关法学问题初探[J].贵州警官职业学院学报, 2006, 18(2):4448, 72.