Utilizing Recurrent Neural Networks for Sentiment Analysis of Financial News to Enhance Market Prediction and Investment Strategies

In this study, an attempt was made to analyze the prospect of Recurrent Neural Networks (RNNs) in carrying out sentiment analysis of financial news in order to improve market prediction and advancement in investment plans. Thus, employing developed NLP methods and neural networks, the study purpose is to develop an automatic solution for identifying the sentiment of financial news articles. The approach that is going to be used includes the data acquisition of great volume of financial news, the data preprocessing, the model training using BiLSTM with BERT embeddings for the sentiment analysis and the combination of the sentiment scores with the other standard financial figures in order to create the novel hybrid market predictor model. The comprehensive analysis shows a high increase in the accuracy of sentiment classification and market prediction and a higher efficiency of the hybrid model over traditional ones. Feedback from the financial analysts substantiates the applicability of the presented system describing it as convenient, precise, and compatible with other systems. In regards to the concerns of the study, hypotheses preoccupy the question of data quality as well as the interpretability of models, focusing on ethical concerns. Finally, the study proves that sentiment analysis based on RNN can give a significant contribution to the illumination of financial activities and help to enhance the effectiveness of the financial investment decision-making process.