Exploring the Use of High-Impact Political and Economic Statements in LLM for Judging Financial Market Trend—A Technical Indicator-Based Approach

In volatile global political and economic shifts, High-Impact Political and Economic Statements (HIPES) by influential leaders often trigger immediate market panic and sharp fluctuations in economic indicators. The forecasting of these indicators is critical for informed decision-making. This study addresses the challenge of enhancing Large Language Models’ (LLMs’) forecasting performance by integrating academic theories as optimization variables, which guide LLMs to analyze HIPES, map relevant academic theories, and convert them into optimization variables, while equipping LLMs with scholars’ diverse perspectives on academic theories. Using Gemini Pro 3, we developed a Dual-Path Comparative Framework: one path employs Direct Forecast (named as F1) based solely on HIPES, while the other combines HIPES with mapped Optimization Variables (named as F2). Both paths were experimented to forecast 75 International Economic Indicators across five asset categories. Results show F2 significantly outperforms F1 in overall accuracy in volatile assets (e.g., crypto), while F1 favors relatively stable asset classes (e.g., commodities). These findings address gaps in “Theory-Guided AI” research, providing strategies for asset-specific forecasting.