Leveraging Extended Context Windows in LLMs for Time-Series and Cross-Sectional Analysis of Financial Reports

This notebook explores the application of large language models (LLMs), specifically Gemini 1.5-pro, in analyzing financial reports to enhance decision-making processes in finance. The experimental design involves analyzing single financial reports, multiple reports from a single company, and reports from various companies. The study demonstrates that Gemini 1.5-pro can effectively handle up to 2 million tokens, enabling the processing of numerous financial documents simultaneously while maintaining response quality. Key findings indicate that simplifying prompts through chat session is essential for optimizing model performance at high token counts. The notebook presents a systematic approach to analyzing financial reports through a series of structured questions aimed at extracting critical metrics, identifying trends, and evaluating risk factors. when applying LLMs. Two in-depth analysis examples show that Gemini 1.5-pro is able to analyze the competitive landscape of companies within the same industry, rank companies based on financial performance, and incorporate stock price changes to identify key factors influencing future stock price movements. How to apply these qualitative responses generated from LLMs with large volumes of text, such as using it to assess future stock performance, could be a potential direction for future research. Long-context processing capabilities can also be applied to various financial topics, opening up new possibilities for financial research.