基于破产传染的智能投资决策支持：概念建模与模型开发
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摘要
近些年来,在全球经济一体化愈演愈烈的整体环境下,经济和金融系统表现出更加相互依赖和紧密联系的趋势。正是这种“牵一发而动全身”的特性,成为2008年美国次贷危机爆发的直接导火索。大西洋彼岸刮起的这场始料未及的“金融飓风”,以美国著名的住房抵押贷款公司新世纪金融公司为代表的贷款机构,以美林公司为代表的投资银行,以及以花旗集团为代表的金融超市和以全球财富管理著称的瑞银集团都成为这场“金融飓风”的直接风眼,同时,受这场“金融飓风”的影响,大大小小的对冲基金,海外投资者都收到了飓风的波及,众多金融机构披露出巨额亏损并宣布破产。
我们认为这种危机传染现象在供应链网络中也是存在的。随着全球经济竞争的加剧,供应网络企业成员之间比以往任何时候都更加重视交流和合作。通常说来,当一个经济体因为长期的,恶性盈利亏损而导致资不抵债的情况下,会申请破产保护。破产的原因有很多种,例如企业陈旧而不愿意打破传统的管理模式和独裁统治致使信息传递低效而不能应对快速变化的消费者市场。然而,在当今供应链中愈来愈加强合作和交流的情境下,一个个体企业作为供应链的一员,它的生死存亡不仅仅决定于自己的盈利情况和管理模式,还与供应链中其他成员的经营状况息息相关。位于供应链中的某个企业的亏损或者破产可能导致与其有合作关系(通过商业信用渠道)的其他企业的经营和运行受阻,从而将这种窘迫的经营状况传染给其他企业,产生流动性危机,濒临破产处境。在网络经济时代,这种现象称为破产传染或者金融危机蔓延。对于这种破产危机现象,一个非常重要并且合乎逻辑的解释就是通过商业信贷债务链的传播而导致的流动性不足。美国次贷危机的发生就是这种现象的一个典型例证。
而正是由于现代供应链网络中的复杂结构和动态因素,使得目前研究供应链网络的企业财务状况以及对应股票价格表现所使用的分析模型和方法存在一些明显的不足,尤其表现在对一些重大事件发生对整个供应链企业以及投资市场波动产生影响的预测力不足上。传统的数学建模和运筹分析方法往往从众多与实际情况并不完全符合的假设开始,对于这类包含众多实体,关系,属性,参数和约束的复杂系统并不能提供一个有效的解决方案。例如,传统的金融数据分析方法往往只着重于大量结构性数据和历史事件序列的收集和分析,而忽略了网络上些及时事件和信息(往往是文本类信息)对企业的财务状况造成的影响。这类模型因为不能全面和深入的分析及时消息对供应链个体的财务状况的冲击,从而不能有效预测在金融投资市场中相应供应链企业的股票表现情况。股票价格瞬间万变,错过这类信息会使投资面临风险甚至蒙受巨大损失。毫无疑问,如果能够对供应链网络中不同个体的财务状况给予及时有效的监控,例如,当某企业发生破产宣告时,通过评估与该企业上下游紧密相连企业的商业贷款风险值,可以有效的预测有可能受到冲击的企业财务状况以及其市场表现。基于信贷传染的财务监控将会在众多领域具有广泛的应用前景,例如投资组合管理中的风险监控。
鉴于此,本文在投资领域的应用背景下,对由于破产危机传染而产生的股票价格波动问题进行了深入的探讨。主要研究内容如下：
首先,对破产危机传染的显现进行概念建模。概念建模为后一阶段的原型系统分析,设计和开发打下了坚实的基础。这部分的概念建模包括两个部分：1).用来表达和实施由某个破产事件而导致的破产危机传染的领域知识的形式本体以及2).在这个形式本体的基础上构建的语义规则。语义规则能够加强推理能力和问题的自动化求解。
其次,在建立的关于破产危机传染的本体模型基础上,构建了基于多智能代理的金融决策系统原型,用来帮助相应的投资者,政策制定者以及相关管理人员及时有效的发现潜在被传染的供应链企业以及在投资市场上的股票价格波动。基于概念建模上的多智能代理决策系统能够不断的处理在网络上发现的及时文本类型的新闻信息并且根据语义规则和知识库,对可能出现的大幅股价波动情况提出预警。在识别出可能股票价格出现大幅波动的企业后,无线推送信息服务会将信息快速的传达给投资者。
最后,本研究通过对通用汽车破产的案例结合概念建模和原型发展对本文所有的研究方法进行初步的验证。通用汽车的案例说明本研究的方法能够有效的管理供应链网络中复杂动态的传染效应,并且为相关投资者,分析人员以及管理者提供积极有效的建设性意见来指导对快速变化的投资市场做出敏捷的反应。
It is widely believed that economic and financial systems have exhibited an increasingly intertwined nature in economic globalization and regional integration, which provides the diffusion path for the U.S. subprime mortgage crisis. Corporate bankruptcy occurs when an entity has chronic and serious losses and/or when an entity becomes insolvent with liabilities that are disproportionate to assets. Traditionally, bankruptcy of a firm is attributed to the entity's own poor management and autocratic leadership. However, with the increasing interconnection among trading partners along the supply network, bankruptcy of a supply chain firm may make other members get into distressed situations. Specifically, distress related to bankruptcy filing at one firm may have value implications for firms with which it is connected through trade credit channels. In a network economy, this phenomenon is termed bankruptcy contagion or financial contagion. An intuitive explanation of bankruptcy contagion is the avalanche of debt chain or credit chain. The current financial crisis triggered by the2008sub-prime mortgage crisis is a typical evidence of this viewpoint.
Due to the complex structure and dynamic interaction of modern supply networks, there are some difficulties faced by pure analytic approaches in analyzing financial status of the supply chain members and hence predicting stock price movements in response to some unexpected events. Mathematical and operation research models usually do not function very well for this kind of financial decision making, for they always start with many assumptions and have difficulty modeling such complex systems that include many entities, relationships, features, parameters and constraints. In addition, traditional modeling and analytic tools lack the ability to predict the impact of a specific event on the performance of the entire supply network. For example, traditional financial data analysis with large volumes of structured data and historical time series cannot offer the full picture and intrinsic insights into the risk nature of the company's financial status and hence its stock performance from different perspectives. There is no doubt that studying the structure and dynamics of trade credit contagion will provide a revolutionary insight into our understanding of financial status of different entities in the supply network, which suggest predictive implications for many application domains such as risk monitoring in portfolio management.
This research presents an information and knowledge exchange framework to support distributed problem solving. From the application viewpoint the study concentrates on the financial investment domain; however, many presented solutions can be extended to other dynamic domains. A conceptual model is firstly present to help lay a solid foundation for analysis, design and development of our prototype system. This conceptual model includes two components:(1) a formal ontology that makes it possible to represent and implement relevant domain knowledge of financial contagion effects triggered by a bankruptcy event and (2) semantic rules added to extend inference capability and enable automation of problem-solving. Based on the ontological knowledge model of bankruptcy contagion, the proposed prototype is developed to help shareholders, policy makers and relating managers to identify candidate firms affected by a financially distressed firm and analyze possible valuation effects from investment perspective. Based on the well-developed conceptual model, a multi-agent decision support system is developed to continuously observe real-time news reports and forecasts their potential impact on the corresponding stock price. After identifying the relating companies for which significant market reactions can be expected, a wireless push-based message service promptly supplies information to investors. Lastly, a case study is used demonstrating the use of our approach, going through from the conceptual part to the implementation stage. The case study shows that our approach can effectively manage the intricate and dynamic contagion effects occurring along the supply chain and provide constructive advice for investors and analysts to take proactive action.
我们认为这种危机传染现象在供应链网络中也是存在的。随着全球经济竞争的加剧,供应网络企业成员之间比以往任何时候都更加重视交流和合作。通常说来,当一个经济体因为长期的,恶性盈利亏损而导致资不抵债的情况下,会申请破产保护。破产的原因有很多种,例如企业陈旧而不愿意打破传统的管理模式和独裁统治致使信息传递低效而不能应对快速变化的消费者市场。然而,在当今供应链中愈来愈加强合作和交流的情境下,一个个体企业作为供应链的一员,它的生死存亡不仅仅决定于自己的盈利情况和管理模式,还与供应链中其他成员的经营状况息息相关。位于供应链中的某个企业的亏损或者破产可能导致与其有合作关系(通过商业信用渠道)的其他企业的经营和运行受阻,从而将这种窘迫的经营状况传染给其他企业,产生流动性危机,濒临破产处境。在网络经济时代,这种现象称为破产传染或者金融危机蔓延。对于这种破产危机现象,一个非常重要并且合乎逻辑的解释就是通过商业信贷债务链的传播而导致的流动性不足。美国次贷危机的发生就是这种现象的一个典型例证。
而正是由于现代供应链网络中的复杂结构和动态因素,使得目前研究供应链网络的企业财务状况以及对应股票价格表现所使用的分析模型和方法存在一些明显的不足,尤其表现在对一些重大事件发生对整个供应链企业以及投资市场波动产生影响的预测力不足上。传统的数学建模和运筹分析方法往往从众多与实际情况并不完全符合的假设开始,对于这类包含众多实体,关系,属性,参数和约束的复杂系统并不能提供一个有效的解决方案。例如,传统的金融数据分析方法往往只着重于大量结构性数据和历史事件序列的收集和分析,而忽略了网络上些及时事件和信息(往往是文本类信息)对企业的财务状况造成的影响。这类模型因为不能全面和深入的分析及时消息对供应链个体的财务状况的冲击,从而不能有效预测在金融投资市场中相应供应链企业的股票表现情况。股票价格瞬间万变,错过这类信息会使投资面临风险甚至蒙受巨大损失。毫无疑问,如果能够对供应链网络中不同个体的财务状况给予及时有效的监控,例如,当某企业发生破产宣告时,通过评估与该企业上下游紧密相连企业的商业贷款风险值,可以有效的预测有可能受到冲击的企业财务状况以及其市场表现。基于信贷传染的财务监控将会在众多领域具有广泛的应用前景,例如投资组合管理中的风险监控。
鉴于此,本文在投资领域的应用背景下,对由于破产危机传染而产生的股票价格波动问题进行了深入的探讨。主要研究内容如下：
首先,对破产危机传染的显现进行概念建模。概念建模为后一阶段的原型系统分析,设计和开发打下了坚实的基础。这部分的概念建模包括两个部分：1).用来表达和实施由某个破产事件而导致的破产危机传染的领域知识的形式本体以及2).在这个形式本体的基础上构建的语义规则。语义规则能够加强推理能力和问题的自动化求解。
其次,在建立的关于破产危机传染的本体模型基础上,构建了基于多智能代理的金融决策系统原型,用来帮助相应的投资者,政策制定者以及相关管理人员及时有效的发现潜在被传染的供应链企业以及在投资市场上的股票价格波动。基于概念建模上的多智能代理决策系统能够不断的处理在网络上发现的及时文本类型的新闻信息并且根据语义规则和知识库,对可能出现的大幅股价波动情况提出预警。在识别出可能股票价格出现大幅波动的企业后,无线推送信息服务会将信息快速的传达给投资者。
最后,本研究通过对通用汽车破产的案例结合概念建模和原型发展对本文所有的研究方法进行初步的验证。通用汽车的案例说明本研究的方法能够有效的管理供应链网络中复杂动态的传染效应,并且为相关投资者,分析人员以及管理者提供积极有效的建设性意见来指导对快速变化的投资市场做出敏捷的反应。
It is widely believed that economic and financial systems have exhibited an increasingly intertwined nature in economic globalization and regional integration, which provides the diffusion path for the U.S. subprime mortgage crisis. Corporate bankruptcy occurs when an entity has chronic and serious losses and/or when an entity becomes insolvent with liabilities that are disproportionate to assets. Traditionally, bankruptcy of a firm is attributed to the entity's own poor management and autocratic leadership. However, with the increasing interconnection among trading partners along the supply network, bankruptcy of a supply chain firm may make other members get into distressed situations. Specifically, distress related to bankruptcy filing at one firm may have value implications for firms with which it is connected through trade credit channels. In a network economy, this phenomenon is termed bankruptcy contagion or financial contagion. An intuitive explanation of bankruptcy contagion is the avalanche of debt chain or credit chain. The current financial crisis triggered by the2008sub-prime mortgage crisis is a typical evidence of this viewpoint.
Due to the complex structure and dynamic interaction of modern supply networks, there are some difficulties faced by pure analytic approaches in analyzing financial status of the supply chain members and hence predicting stock price movements in response to some unexpected events. Mathematical and operation research models usually do not function very well for this kind of financial decision making, for they always start with many assumptions and have difficulty modeling such complex systems that include many entities, relationships, features, parameters and constraints. In addition, traditional modeling and analytic tools lack the ability to predict the impact of a specific event on the performance of the entire supply network. For example, traditional financial data analysis with large volumes of structured data and historical time series cannot offer the full picture and intrinsic insights into the risk nature of the company's financial status and hence its stock performance from different perspectives. There is no doubt that studying the structure and dynamics of trade credit contagion will provide a revolutionary insight into our understanding of financial status of different entities in the supply network, which suggest predictive implications for many application domains such as risk monitoring in portfolio management.
This research presents an information and knowledge exchange framework to support distributed problem solving. From the application viewpoint the study concentrates on the financial investment domain; however, many presented solutions can be extended to other dynamic domains. A conceptual model is firstly present to help lay a solid foundation for analysis, design and development of our prototype system. This conceptual model includes two components:(1) a formal ontology that makes it possible to represent and implement relevant domain knowledge of financial contagion effects triggered by a bankruptcy event and (2) semantic rules added to extend inference capability and enable automation of problem-solving. Based on the ontological knowledge model of bankruptcy contagion, the proposed prototype is developed to help shareholders, policy makers and relating managers to identify candidate firms affected by a financially distressed firm and analyze possible valuation effects from investment perspective. Based on the well-developed conceptual model, a multi-agent decision support system is developed to continuously observe real-time news reports and forecasts their potential impact on the corresponding stock price. After identifying the relating companies for which significant market reactions can be expected, a wireless push-based message service promptly supplies information to investors. Lastly, a case study is used demonstrating the use of our approach, going through from the conceptual part to the implementation stage. The case study shows that our approach can effectively manage the intricate and dynamic contagion effects occurring along the supply chain and provide constructive advice for investors and analysts to take proactive action.
引文
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Leroy, G., & Chen, H.2001. Meeting medical terminology needs-the ontology-enhanced medical concept mapper. Information Technology in Biomedicine, IEEE Transactions on,5(4):261-270.
Lin, H., & Harding, J.2007. A manufacturing system engineering ontology model on the semantic web for inter-enterprise collaboration. Computers in Industry,58(5): 428-437.
Miiller, H. M., Kenny, E. E., & Sternberg, P. W.2004. Textpresso:an ontology-based information retrieval and extraction system for biological literature. PLoS biology,2(11):e309.
March, S. T., & Smith, G. F.1995. Design and natural science research on information technology. Decision support systems,15(4):251-266.
Markus, M. L., Majchrzak, A., & Gasser, L.2002. A design theory for systems that support emergent knowledge processes. Mis Quarterly:179-212.
McGuinness, D. L., & Van Harmelen, F.2004. OWL web ontology language overview. W3C recommendation,10:2004-2003.
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Petrovic, D., Roy, R., & Petrovic, R.1998. Modelling and simulation of a supply chain in an uncertain environment. European Journal of Operational Research, 109(2):299-309.
Picker, R. C.1997. Simple games in a complex world:A generative approach to the adoption of norms. The University of Chicago Law Review,64(4):1225-1288.
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Sun, S. X., Zhao, J., & Wang, H.2011. An agent based approach for exception handling in e-procurement management. Expert Systems with Applications.
Teo, T. S. H., & Choo, W. Y.2001. Assessing the impact of using the Internet for competitive intelligence. Information& management,39(1):67-83.
Tsalgatidou, A., & Pilioura, T.2002. An overview of standards and related technology in web services. Distributed and Parallel Databases,12(2):135-162.
Tsichritzis, D.1998. The dynamics of innovation:Copernicus Books, New York.
Uschold, M.1996. Building ontologies:Towards a unified methodology. TECHNICAL REPORT-UNIVERSITY OF EDINBURGH ARTIFICIAL INTELLIGENCE APPLICATIONS INSTITUTE AMI TR.
Van Rijckeghem, C., & Weder, B.2001. Sources of contagion:is it finance or trade? Journal of International Economics,54(2):293-308.
Von Alan, R. H., March, S. T., Park, J., & Ram, S.2004. Design science in information systems research. Mis Quarterly,28(1):75-105.
Walls, J. G., Widmeyer, G. R., & E1 Sawy, O. A.1992. Building an information system design theory for vigilant EIS. Information Systems Research,3(1): 36-59:
Wand, Y., Monarchi, D. E., Parsons, J., & Woo, C. C.1995. Theoretical foundations for conceptual modelling in information systems development. Decision support systems,15(4):285-304.
Wand, Y., & Weber, R.1990. Mario Bunge's Ontology as a formal foundation for information systems concepts. Studies on Mario Bunge's Treatise, Rodopi, Atlanta:123-149.
Wand, Y., & Weber, R.2002. Research commentary:Information systems and conceptual modeling-a research agenda. Information Systems Research,13(4): 363-376.
Wang, H.1997. A conceptual model for virtual markets. Information & management, 32(3):147-161.
Wang, S., Zhe, Z., Kang, Y., Wang, H., & Chen, X.2008. An ontology for causal relationships between news and financial instruments. Expert Systems with Applications,35(3):569-580.
Wang, W., & Rada, R.1995. Experiences with semantic net based hypermedia. International journal of human computer studies,43(3):419-440.
Weber, R.2003. Research Review Paper Conceptual Modelling and Ontology: Possibilities and Pitfalls. Journal of Database Management,14(3):1-20.
Wooldridge, M., & Jennings, N. R.1995. Intelligent agents:Theory and practice. Knowledge engineering review,10(2):115-152.
Xu, X., Sun, Y., & Hua, Z.2010. Reducing the probability of bankruptcy through supply chain coordination. Systems, Man, and Cybernetics, Part C:Applications and Reviews, IEEE Transactions on,40(2):201-215.
Ye, K., Wang, S., Yan, J., Wang, H., & Miao, B.2009. Ontologies for crisis contagion management in financial institutions. Journal of Information Science,35(5): 548-562.
Zhang, J., Cheng, L., & Wang, H.2011. A multi-agent-based decision support system for bankruptcy contagion effects. Expert Systems with Applications.
李建忠.2002.多AGENT结构产品制造链信息环境系统的实现技术.南京理工大学.