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This study predicted the bankruptcy risk of companies listed in Japanese stock markets for the entire industry and individual industries using multiple discriminant analysis (MDA), artificial neural network (ANN), and support vector machine (SVM) and compared the methods to determine the best one. The financial statements of the companies listed in the Tokyo Stock Exchange in Japan were used as data. The data of 244 companies that went bankrupt between 1991 and 2015 were used. Additionally, the data of 64,708 companies that did not go bankrupt between 1991 and 2015 (24 years) were used. The data was acquired from the Nikkei NEEDS database. It was found from the results of empirical analysis that the SVM is more accurate than the other models in predicting the bankruptcy risk of companies. In the ANN analysis and MDA, bankruptcy prediction could be made accurately only for some individual industries. In contrast, the SVM could predict the bankruptcy risk of companies almost perfectly for either entire and individual industries. This bankruptcy prediction model can help customers, investors, and financiers prevent losses by focusing on the financial indicators before finalizing transactions.

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Developed countries like Germany and Japan, and many of their municipalities currently have large debts that will likely increase over time. These debts may never be repaid because debt repayment per capita will increase as a result of decreasing populations. It has become difficult for them to maintain many of their existing bridges because of large debts. Given the challenges faced with maintaining these bridges, it is necessary to formulate plans that may include discontinuing the maintenance on certain bridges. Therefore, this study proposes the use of a triage methodology for managing bridges (e.g., highway bridges) much like the triage methodology used by medical institutions to handle large numbers of patients after a severe disaster. More specifically, the proposed methodology makes decisions based on whether or not to build a new bridge after an existing bridge has reached the end of its lifespan and is decommissioned by evaluating factors such as cost and convenience. This is done by establishing a virtual network comprising of links made up of bridges that span across towns. This study proposes models and simulates this social problem by virtual networks and virtual bridge data, which can then be used to derive an optimal solution.

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