Application of Multi-Layer Linear Model and Neural Network Fusion Algorithm in Corporate Bond Yield Prediction
In recent years,corporate bonds represented by the inter-bank bond market in China have been developing continuously.Corporate bonds are one of the important financing channels for listed enterprises,and the forecast research on their yield can provide important investment basis for market participants.Combined with the liquidity proxy index synthesized by the double entropy weight method,the corporate bond yield prediction model is constructed based on the multi-layer linear regression and neural network model,and the Fama-French three factors of the equity market are incorporated into the fusion prediction model.The results show that the liquidity proxy index synthesized by entropy weight method and Fama-French three factors are significant in many bond yield prediction models.Compared with a single model such as linear regression or machine learning,the fusion model combining multi-layer linear regression and neural network learning has smaller prediction deviation and multiple evaluation indicators are the best,which can provide a more scientific and reasonable method for solving this problem.
entropy weight methodmulti-layer linear modelneural networkcorporate bondfusion algorithm
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