An Efficient Hybrid Model for Stock Market Price Prediction Using CNN-BiLSTM with Attention Mechanism and Sentimental Analysis

Stock price prediction is a challenging task with dynamic trends and volatile markets due to opinion and sentiment forces in the market. The conventional Autoregressive Integrated Moving Average (ARIMA) and Support Vector Regression (SVR) methods only take into account historical numerical values and ignore the influence of current financial news. Thus, they do not model the interdependence between historical stock prices and opinion data and are plagued by lower precision and prediction power. To overcome these drawbacks, this study proposes a Hybrid Sentiment-Aware Stock Prediction Model (HSASP) that integrates Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) with an Attention Mechanism (AM). The CNN captures spatial relations from Tesla's past stock history, and BiLSTM uses opinion-based information from Reddit News to capture temporal relations. The AM selects the most significant features by assigning weights to valuable details, enhancing the predictability of the model. The suggested HSASP model improves accuracy by 18%, precision by 16%, and trend consistency by 20%, being successful for stock price prediction. With the integration of price- and opinion-based information, the suggested model provides a strong option for decision-making with high efficiency and precision.