Prediction of Sovereign Credit Risk Rating Using Sensing Technology

MUIR > College of Human Ecology > Department of Hospitality Management > Papers > Item 987654321/4061

Please use this identifier to cite or link to this item: http://ir.meiho.edu.tw/ir/handle/987654321/4061

Title:	Prediction of Sovereign Credit Risk Rating Using Sensing Technology
Authors:	Yen, Chen-Ying Ju, Yi-Ling Sung, Shih-Fu Wu, Yu-Lung Su, En-Der
Contributors:	民生學院
Keywords:	Internet of Things (IoT) CDS spread sovereign credit rating
Date:	2021
Issue Date:	2022-03-02T08:35:54Z (UTC)
Abstract:	In recent years, the study and application of sensor technology have expanded from the industrial to the commercial, financial, and even medical fields. In Taiwan, many studies link sensor technology and the Internet of Things (IoT) with commercial finance, and research on their applications to commercial financial warning or company management is being conducted. The cross-discipline integration of IoT and finance has advanced through the diversification of IoT, and that is why the financial service industry will use IoT as a service platform to provide cross-disciplinary integration services. After exchanging and collecting information by using sensors and handheld devices, it helps to develop new business models such as supply chain financial services to share and manage networks. This enables the financial industry to have a more comprehensive and different understanding of customers and enterprises, which can help the financial industry explore different business opportunities. The aim of this study is to use IoT technology to perform real-time analysis of treasury bill big data and to apply the principles behind sensing to predict and analyze the probability of international defaults in order to reduce investment risks and estimate the credit default swap (CDS) spread. We will then compare the results for the macroeconomic variables with the results of relevant analyses on indicators. The results of the correlation analysis show that the CDS spread and the ratio of the current account balance (CAB) to the gross domestic product have a negative correlation with the gross domestic product growth (GDPG) rate. It has a positive correlation with the inflation rate (INF), the ratio of government debt to gross domestic product (DEBT), and the industrial production index annual growth rate (IPIG). Therefore, the result and the rating-implied expected loss (RIEL) are the same as those in the relevant analysis results of these five macroeconomic indicators. It was also found that IoT technology can be used in the real-time analysis of large-scale treasury bill data, and the principle of sensing can be applied to predict and analyze the accuracy of international defaults.
Appears in Collections:	[Department of Hospitality Management ] Papers

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