Advanced LLM Transformers and Zero-Shot XGBoost for Accurate Arabic Text Insights and Profit Predictions

Abstract This research proposes an innovative Arabic financial forecasting model that integrates linguistic relation extraction with advanced deep learning and machine learning techniques. The framework combines Arabic Open Information Extraction (AOIE), BERT-based contextual sentiment analysis combined with zero-shot XGBoost, forming an end-to-end architecture capable of interpreting both the semantics and emotions of Arabic financial text. The model aims to address the linguistic and resource challenges inherent in Arabic financial data by leveraging syntactic and semantic structures extracted from unstructured sources such as news reports and financial statements. Through extensive experiments, the proposed approach demonstrated consistent and superior predictive performance across different configurations. The optimal setting achieved an accuracy of 97.4%, with a Mean Absolute Error (MAE) of 0.13 and a Root Mean Square Error (RMSE) of 0.18, confirming its reliability and robustness in forecasting stock market trends. Compared to traditional statistical models (ARIMA, VAR) and deep learning baselines (LSTM, CNN, Transformer-only), the proposed AOIE–BERT–zero-shot XGBoost framework achieved the lowest prediction error and highest interpretability. The findings underscore the significance of incorporating Arabic linguistic structures into predictive modeling and demonstrate the potential of transformer-based NLP integration for financial analytics. This research contributes a scalable and linguistically adaptive solution, paving the way for more accurate, explainable, and multilingual applications of Natural Language Processing (NLP) in the global financial domain.