Research on the construction of financial market volatility prediction model in digital economy environment based on machine learning algorithm

Achieving accurate prediction of financial market fluctuations is beneficial for investors to make decisions, while machine learning algorithms can utilize a large amount of data for training and learning, which has good effect on predicting financial market fluctuations. The article first analyzes the financial dataset, and then constructs a feature selection model by combining Boruta and SHAP to screen the financial data features. Based on the LSTM model, a new Dropout layer and fully connected layer are designed to construct the AMP-LSTM model to realize the prediction of financial market fluctuations. The Boruta SHAP algorithm has a RMSPE of 0.242, which is good for screening. The prediction performance of the AMP-LSTM model is significantly better than that of the traditional LSTM (p<0.01), and the predicted values are closer to the actual values. The method in this paper performs better than MLP, RNN and other methods in general in terms of error performance when predicting indicators such as WTI, Brent, LGO, etc., and is able to realize the prediction of financial market volatility in the digital economy environment.