Bond risk predication method and system based on machine learning algorithms

Abstract

The invention provides a bond risk predication method and system based on machine learning algorithms. According to the method, a bond data sample is obtained and stored; statistics software is used to preprocess the bond data and obtain preprocessing data; different types of machine learning algorithms are used to carry out modeling on the preprocessing data, models established via the different machine learning algorithms are evaluated and compared in an integrated manner via the indexes of model specificity, flexibility and total prediction accuracy, and a most appropriate model of highest prediction performance is selected; parameter adjustment and optimization are carried out on the model selected in the modeling step to obtain an optimal model; and bond data is obtained in real time, and the optimal model is used to predict the bond data. The method can be used to predict the bond risk precisely in real time and determines and tracks the risk accurately, an investor is helped to master the bond risk condition timely and make correct investment decisions, possible loss caused by bond default is avoided, and the investor has lower risk in investment.

Description

technical field [0001] The invention belongs to the technical field of big data, and in particular relates to a bond risk prediction method and system based on a machine learning algorithm. Background technique [0002] Investors in the financial market, especially commercial banks, securities companies, insurance institutions, fund companies, etc., are under the requirements of financial supervision and risk control, and have a considerable amount of funds to purchase fixed-income products, and bonds are an important investment target. In order to ensure that the bonds they invest in can bring stable interest income and avoid default losses, investors need to carry out risk warnings and follow-up forecasts for the bonds they invest in, so as to buy high-quality bonds and sell potentially risky bonds in a timely manner. bond. [0003] Existing bond risk early warning or prediction technologies mainly use risk-related information of bond issuers, such as credit information, ...

AI Technical Summary

Problems solved by technology

Credit rating agencies only take relevant downgrade measures after the occurrence of risk events on bond issuers, and many investment institutions may face huge bond default risks due to lack of ability to predict risks in advance or insufficient prediction capabilities

At the same time, due to the complexity of the financial market and the lack of relevant financial technologies, very few financial institutions can make accurate predictions and judgments on bond risks

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