Research On Risk Identification And Early Warning Of External Debt In Countries Along The Belt And Road Initiative

In recent years,more and more countries and regions have participated the “Belt and Road” construction,but external debt defaults of these countries are frequent,and it is increasingly important to assess the risk of external debt of countries along the“Belt and Road” and to establish an effective risk warning mechanism.Since most of these countries are non-developed countries,they often lack advanced management experience when developing their own economies and neglect the debt structure,which may lead to external debt default in serious cases,affecting the reputation of the countries and hindering the sustainable development of the “Belt and Road” Initiative.This will affect the reputation of countries along the “Belt and Road” and hinder the sustainable development of the “Belt and Road” Initiative.Therefore,the objectives of this paper are as follows: to understand the current situation of the external debt risk of these countries included in the “Belt and Road” construction;to conduct a comprehensive evaluation of the external debt risk of these countries;to establish a model for the early warning of the external debt risk of these countries,and to identify the factors that have a greater impact on the default or non-default of the external debts of the countries.The study will provide advice for the development of the “Belt and Road” construction and the investment decisions of Chinese enterprises in these countries.This paper firstly analyzes the macroeconomic environment,external debt scale,external debt maturity distribution and external debt service capacity of the countries along the “Belt and Road” to understand the current situation of the external debt risk of these countries;then uses fuzzy matter element analysis to make a detailed evaluation of the external debt risk of the sample countries to understand the changes in the external debt risk of these countries and their current level of external debt risk;finally,by comparing three different machine learning early warning models,the accuracy of the judgment of the external debt default situation of the countries along the “Belt and Road” is compared to find out the most suitable model for predicting the external debt default risk of the countries along the “Belt and Road”,and after optimizing the relevant parameters of the algorithm model,the importance of the influence factors is analyzed using the model.The study finds that: the level of external debt risk of countries along the “Belt and Road” varies greatly,among which Jordan,Georgia,Turkey,Lebanon,Belarus,Sri Lanka and Mongolia have relatively high external debt risk,and investments in these countries need to be judged more prudently in terms of default risk;the random forest algorithm is more accurate in analyzing the external debt risk of countries along the“Belt and Road”.The random forest algorithm is more accurate in analyzing the foreign debt risk of countries along the “Belt and Road”,and the model optimization can improve the model performance.The importance of these indicators is high,and the early warning and monitoring of the external debt risk of the countries along the route should focus on the abnormal status of these indicators.