Fusion of Technical Indicators and Sentiment Analysis in a Hybrid Framework of Deep Learning Models for Stock Price Movement Prediction

Stock price movement prediction is challenging due to unpredictable fluctuations and the significant impact of market sentiment and news. Accurate prediction models can enhance investor decision-making and control over stock price movements. Creating a model for predicting high-accuracy stock price movements can improve investor control over stock prices. In this study, a wide range of technical indicators and various aspects of sentiment analysis in tweets were used to predict stock price movement. The impact of the maximum number of positive comments on Tesla stocks on price increases is investigated. Also, we proposed a method for adding sentiments to each tweet. Extracted advanced sentiment analysis features such as the number of positive comments, the number of negative comments, the average score of positive comments, the average score of negative comments, daily tweet volume, ratio of positive to negative tweets. Effect of time windows with variate size is investigate. A CNN-LSTM deep neural network is used to predict stock price movement and compared with LSTM and GRU models. According to the results, the proposed CNN-LSTM deep neural network has the best results to predict stock price movement over the 30-day interval.