Strategy Analysis of Financial Neural Network Model in Bond Investment Prediction

Abstract

In the diversified and complex environment of financial markets, effective investment strategy prediction has become particularly crucial. Especially in the bond market, due to its close connection with macroeconomic factors, the accuracy of predictions is of great significance for investment returns and risk management. This article delves into the application and strategic analysis of financial neural network models in bond investment prediction. We first reviewed the basic architecture of financial neural network models and emphasized their advantages in capturing nonlinear market dynamics. Then, we describe in detail how to use this model to predict the bond market, including the impact of interest rate changes, credit risk and macroeconomic factors. Furthermore, we propose a bond investment strategy framework based on neural network prediction. This framework considers various factors such as market liquidity, bond duration, and credit rating to optimize the returns and risks of investment portfolios. With the further development of deep learning and neural network technology in the financial field, its application potential in bond market prediction and strategy formulation will be more widely recognized and utilized.