Intelligent Investment Decision Support For Bankruptcy Contagion:Conceptual Modeling And Prototype Development

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.