Corporate Bond Default Risk Estimation Based On Machine Learning

In 2007,China took the lead in issuing bonds.With the help of the east wind of China's economic development,the development of corporate bonds was very rapid,and in March 2014,the first substantial default occurred.This is not an accident,because in the following years,bond defaults have become more frequent.Especially after 2018,the number and scale of bond defaults have risen like a “rocket”.Behind it lies the system,market,regulators,and issuers.The issues of debtors,investors,intermediaries,and so on are thought-provoking.Looking at my country's financial market in recent years,the risks and probabilities of various default events are increasing.For example,credit defaults and trust defaults have frequently come into people's eyes.In the face of relatively high returns,people are often unable to withstand the temptation,lack rationality,and credit risk-related information,there is no comprehensive risk compensation mechanism.Fully assumed all the losses caused by the credit risk.This article uses the public financial information of listed companies to find indicators that can represent the company's profitability,operation,and solvency from financial data,and use these indicators to make judgments on the financial risks of the analysis objects,and evaluate the credit risks of corporate bonds based on financial risks Qualitative identification to make investment decisions.Even though financial reports,financial data,and financial ratios are often easily criticized by people: they are easy to be faked and whitewashed.Considering the current market and academic conditions,using financial information is the simplest and easiest way to distinguish between credit risk and corporate bonds.effective method.This article avoids the use of complex models that are restricted by many conditions and the practicality of the models.Based on the advantages of financial information,its application is easier to be accepted by investors,because the establishment and use of models are usually sought after by professionals.Considering time,cost and effectiveness,financial information should be the first choice.And from the actual application effect,the credit risk of corporate bonds can be identified.Although it is impossible to give an accurate quantitative indicator for the identification of corporate bond credit risk,through judgment,investors can make investment decisions based on their own circumstances.In this article,the author conducts specific research on the theoretical foundation and mathematical principles of machine learning and the actual operation method of data mining,which proves the pivotal position of data cleaning and data dimensionality reduction in the process of using machine learning training algorithms.SPSS,a powerful data processing software and Python,a newly emerging "top-level" software in the programming world,verified the credibility of the model through empirical analysis.