Intelligent financial forecasting using transformers, neuro-symbolic AI, and agent-based systems

Forecasting the stock market is a difficult task because of the volatile price movements and complex temporal dependencies. Established models frequently do not realize these unstable trends, which leads to changeable forecasting. This paper introduces a comfortable AI-driven framework that incorporates a sequence-to-prediction transformer model with LLM-based decision-making for accurate and understandable stock price prediction. The prediction starts with a transformer-enabled deep learning approach, which forecasts closing prices for the NIFTY Consumer Durables index based on multi-head attention mechanisms for trend identification. The predicted results are then provided to two decision-making approaches: Neuro-Symbolic AI and an Advanced AI Agent Architecture, to check for accuracy as well as interpretability in financial forecasting. The NSAI model is focused on predictions that are derived from rule-based reasoning, and thus, it ensures that they are in line with actual market behaviors and risk factors. On the other hand, the design of Advanced AI Agent Architecture aimed at enhancing the quality of decisions by utilizing the LLM-based information, bringing in external financial information feeds and memory-based historical data for the trading decision making process. With these AI methods, the model can adapt to changes in market more effectively and, thus, provide more accurate forecasts. This study goes a step further in the development of financial markets analysis by combining deep learning with symbolic reasoning, thereby verifying that the stock market price predictions are not only correct but also interpretable and useable by investors and traders. Through the use of AI-driven stock market strategies, this research work provides a more flexible and explainable way of stock market forecasts, thus it significantly advances the financial market analysis which in turn can be utilized for a better investment method.