A Comprehensive Framework for Stock Prediction: Leveraging NLP Models, Technical Analysis, and Fundamental Data

This study delineates a thorough methodology for forecasting stock trends by amalgamating news sentiment analysis through various Natural Language Processing (NLP) models with both technical and fundamental analysis. Conventional stock prediction techniques frequently depend on discrete methodologies, concentrating either on historical pricing data or market fundamentals. However, given the burgeoning accessibility of real-time news alongside advancements in NLP, the integration of qualitative data into predictive models has become not only viable but increasingly crucial. In this scholarly inquiry, we establish a hybrid framework that fuses multiple NLP models to scrutinise and quantify the sentiment and pertinence of financial news. The insights garnered are subsequently integrated with technical indicators and fundamental metrics to augment the precision of stock trend forecasts. The NLP models employed in this investigation encompass sentiment analysis, named entity recognition, and topic modelling, which collectively furnish a robust comprehension of news that influences market movements. Technical analysis is executed utilizing algorithms that assess price trajectories, trading volume, and additional market indicators, whereas fundamental analysis capitalizes on financial ratios and macroeconomic data. The proposed model undergoes evaluation on a varied dataset that includes historical stock prices, financial disclosures, and news articles. We apply an array of machine learning and deep learning algorithms to scrutinise the data and anticipate prospective stock movements. The findings indicate that our integrated framework surpasses traditional models, yielding considerable enhancements in predictive accuracy. This research accentuates the significance of a multifaceted strategy to stock prediction, amalgamating the strengths of NLP, technical, and fundamental analysis to furnish a more comprehensive insight into market dynamics.