Research on the Construction of Efficient Application System of Financial Market Prediction Model Based on Deep Learning

In this paper, we propose a deep learning framework that combines Positional Word Embedding (PWE), a modified version of Word Embedding (WE), with Gated Recurrent Unit (GRU). It aims to improve the accuracy and efficiency of financial market forecasting. PWE extends traditional word embedding techniques by taking into account not only the semantics of words, but also their position information in the sentence. In the context of financial markets, this allows the model to distinguish different meanings carried by the same word in different contexts. To capture long-term dependencies in financial market time series, the framework uses PWE to capture the semantic and location information of words in text data, and then uses GRU to process this information and make predictions. It makes full use of the ability of deep learning in processing structured and unstructured data, improves the prediction performance of the model, and is suitable for financial market analysis with a large amount of text data and time series data.