Hybrid deep learning for forex prediction : integrating social media sentiment and technical indicators

Abstract

This study proposed a new way to predict currency prices in the Forex market by combining statistical indicators with social media sentiment analysis. The research classifies tweets from influential financial analysts as positive, negative, or neutral and combines sentiment analysis results with technical indicators derived from historical price behavior. This mix of methods improves pattern recognition in the market and leads to smarter trade choices that are more likely to be right. The VADER Sentiment library, a powerful sentiment analysis tool, has been employed to analyze Forex-related tweets of the most influential market players. The study identifies the most influential tweets based on impact engagement measures (likes, replies, and retweets) and selects the most relevant technical indicators. A dataset comprising ten technical indicators, along with news tweets from influential players in USD and EURO trades, has been collected for the study. A prediction model was built using several deep learning approaches including Gated Recurrent Units (GRU), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, Recurrent Neural Networks (RNN), and some combined models. The LSTM-CNN-Attention model, which is a recent and specialized architecture for Forex data, was also considered in our experiment. In addition, the best-performing models were tested both with and without impact ratio - Sentiment Score of Social media Text. To see which worked best, their performance is compared using key points like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and the R-squared score. Comparative analysis reflected the weaknesses and strengths of individual models, revealing the effectiveness of the ensemble strategy. The research also points out its limitations. It includes a detailed look at what other popular models are being used in predicting the Forex market. The results show that combining sentiment analysis with technical indicators improves how accurately it can be predicted Forex prices in this research. The hybrid GRU-LSTM model was the best among the deep learning options in this research experiment. It outperformed all of the earlier methods used by other researchers on the EUR/USD Forex data