Abstract: Aiming at the problem of low prediction accuracy when classical machine learning algorithms (e.g., decision trees, random forests) modelled complex implicit interaction relationships, we proposed a fluctuation  prediction model based on recurrent neural networks and an attention mechanism. We first calculated the complex interaction relationships among various  influencing factors through the attention mechanism, and then used  recurrent neural networks to learn the hidden variable representations of the model, thereby achieving precise prediction. The results of simulation and comparative experiments with multiple classical prediction models show that the  prediction accuracy of proposed model is significantly higher than other machine learning models,  providing a more efficient and accurate solution for the field of volatility prediction.

Key words:  , recurrent neural network,  , attention mechanism, machine learning, prediction model