基于多模态特征小波分解的深度学习股价概率预测

This paper constructs an innovative deep learning model for probabilistic stock price prediction based on multi-modal feature wavelet decomposition(MWDPF). This model integrates multi-source heterogeneous information, including dynamic continuous features, dynamic categorical features,static continuous features,and static categorical features. Through a parallel fusion strategy, it fully explores the complementary information in different feature subspaces,comprehensively characterizing the multiple dimensions affecting stock price fluctuations. It adopts an auto-regressive recurrent neural network architecture,which can directly output the probability distribution prediction of stock price changes, rather than a single deterministic value prediction, more closely matching the actual probabilistic distribution characteristics of stock prices. Additionally, this model introduces wavelet decomposition technology to denoise the original time series,adaptively filtering out noise components at different scales, improving its ability to capture intrinsic fluctuation patterns. In the empirical analysis phase,this study collects multi-modal data from financial databases and internet forums,and through a series of preprocessing steps such as missing value imputation,outlier removal,and time alignment,as well as careful feature engineering and model optimization, achieves excellent prediction performance, significantly outperforming traditional statistical models and deep learning models, with substantial improvements in evaluation metrics. The prediction results generated by the proposed model are used to construct a multi-factor stock selection strategy, achieving considerable excess returns in real-world backtesting,further verifying the effectiveness of the model in practical investment decision-making. This study provides an effective solution for stock price prediction, enriches the theories and methods of quantitative investment,and has significant theoretical and application value.