Hybrid Approaches for Stocks Price Prediction: A New Institutive Way of Neural Architecture Design

Abstract

Stock price prediction is a critical aspect of financial decision-making for both individual investors and institutions. Traditional methods such as ARIMA and deep learning techniques like ANNs, LSTM, and GRU have demonstrated effectiveness in forecasting stock prices but suffer from inherent limitations. In recent years, hybrid models have emerged as a promising approach to address these shortcomings. This research paper introduces and evaluates three novel hybrid models for stock price prediction. First model integrates GRU, LSTM, and attention mechanisms to leverage the strengths of each component. By employing attention mechanisms, the model can focus on relevant information, thereby enhancing prediction accuracy. The second model combines GRU and LSTM networks with attention mechanisms, incorporating additional architectural elements to further refine predictive capabilities. The third proposed model is a fusion of the first two, augmented with additional features and enhancements to achieve superior performance in stock price prediction tasks. Evaluation of these proposed models utilizes commonly used metrics to assess predictive accuracy and generalizability. The results demonstrate the effectiveness of the proposed hybrid approaches in improving stock price prediction performance. This research contributes to the advancement of stock price prediction methodologies by proposing innovative hybrid models, offering valuable tools for researchers and practitioners in the finance industry seeking enhanced forecasting accuracy and decision-making capabilities.